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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ANTARCTIC LITHOSPHERIC ANOMALIES FROM 0RSTED SATELLITE AND NEAR-SURFACE MAGNETIC OBSERVATIONS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

Hyung Rae Kim, B.Sc., M.Sc.

The Ohio State University

2002

Dissertation Committee: Approved by

Dr. Ralph R. B. von Frese, Adviser

Dr. Hallan C. Noltimier Adviser Dr. Jeffery J. Daniels Department of Geological Sciences Dr. Beata Csatho

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ABSTRACT

We investigate the utility of combining satellite and near-surface magnetic anoma

lies for enhanced studies of the Antarctic lithosphere. We process magnetic data from

the 0rsted satellite lauched in Feburary in 1999 to confirm the veracity of the Antarc

tic lithospheric anomalies mapped by the Magsat mission over twenty years ago. Our

analysis reveals that core field model estimates between degree 11 and 13 can contain

significant lithospheric components. To extract these components, we use the pseudo

magnetic effect of a model of Antarctic crustal thickness variations that we obtain

by spectrally comparing the terrain gravity to free-air gravity anomalies. From the

correlation spectrum between the pseudo magnetic and degree 11-13 satellite mag

netic anomalies, we inversely transfrom positively correlated satellite wavenumber

components for estimates of the magnetic crustal thickness effects. By combining

these crustal thickness effects with the degree 13 and higher anomaly components, we

obtain 0rsted and Magsat comprehensive magnetic anomaly maps of the Antarctic

lithosphere at 700 km and 400 km altitudes, respectively. The comprehensive mag

netic anomalies provide important constraints for estimating near-surface magnetic

anomalies in the regional coverage gaps in the Antarctic magnetic map being pro

duced by the Antarctic Digital Magnetic Anomaly Project (ADMAP). We develop

an effective procedure for estimating near-surface anomaly values in unmapped areas

from the joint inversion of satellite and available near-surface data. Relative to the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Magsat data, we find that the 0rsted dta offer significant advantages for this appli

cation because of their greatly enhanced measurement accuracy. We extend the joint

inversion of satellite and near-surface anomalies for modeling the crustal magnetic

properties of the Maud Rise in the Southwest Indian Ocean off the coast of East

Antarctica. We also find that the quantative crustal model for the Maud Rise can be

extrapolated via the satellite magnetic anomalies to the conjugate Agulhas Plateau

off the South African coast for new tectonic perspectives on the Cretaceous breakup

of Gondwana.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. To my parents

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGMENTS

Foremost, I would like to thank Dr. Ralph von Frese for his sincere academic

advice and guidance and financial support over the years. My thanks are also extended

to the other members of the Dissertation Committee, Drs. Jeffery Daniels, Hallan

Noltimier, and Beata Csatho for critical reviews of this effort.

I am grateful to Drs. Michael Purucker and Patrick Taylor at NASA for their

academic supports and to Dr. Jerome Dyment at CNRS and Dr. Alexander Golynsky

at VNIIOkeangeologia for providing their valuable data and advice.

I am also thankful to my OSU colleagues and alumni, Sangsuk Lee, Tim Leftwich,

Drs. J.W. Kim, Eung-Seok Lee, Dan Roman, Laramie Potts, Changryol Kim and

Giehyeon Lee for their friendship.

Elements of this research were supported by grants from NASA Headquarters

(Washington D.C.) and the Geodynamics Branch at the Goddard Space Flight Center

(Greenbelt, MD). Additional support was provided by the Department of Geological

Sciences, the Byrd Polar Research Center, the Center for Mapping, and the Ohio

Supercomputer Center at the Ohio State University.

And finally, I thank my parents, sisters, brothers-in-law and “only” nephew for

their loving support. Also I want to thank God for giving me such a wonderful life

with my wife-to-be Jaeeun.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. VITA

March 14, 1969 Che-Ju, Korea

February, 1993 B.Sc. Geology, Yonsei University, Ko rea. Fall, 1995 - Spring, 1997 Graduate Teaching Assistant, Purdue University, Indiana, USA. August, 1997 M.Sc. Earth and Atmospheric Sci., Purdue University, Indiana, USA. Winter, 1997 - present Graduate Research Assistant, The Ohio State University, USA.

PUBLICATIONS

Leftwich, T. E., R. R. B. von Frese, H. R. Kim, L. V. Potts, D. R. Roman and L. Tan, “Crustal Analysis of Venus from Magellan satellite observations at Atalanta Planitia, Beta Regio and Theta Regio,” J. Geophys. Res., 104(E4), pp. 8441-8462, 1999.

Kim, H. R. and S. D. King, “A study of local time and longitudinal variability of the amplitude of the equatorial electrojet observed in POGO satellite data,” Earth, Planets, Space (formerly J. Geomag. Geoelec.), 51, pp. 373-381, 1999.

von Frese, R. R. B., H. R. Kim, L. Tan, J. W. Kim, P. T. Taylor, M. E. Purucker, D. E. Alsdorf, and C. A. Raymond, “Satellite magnetic anomalies of the Antarctic crust,” Annali di Geofisica, 42, N.2, pp. 309-326, 1999.

Kim, J. W., von Frese, R. R. B., and H. R. Kim, “Crustal modeling from spectrally correlated free-air and terrain gravity data - A case study of Ohio,” Geophysics, 65, pp. 1057-1069, 2000.

Golynsky, A. V., M. Chiappini, D. Damaske, F. Ferraccioli, J. Ferris, C. Finn, M. Ghidella, T. Ishihara, A. Johnson, H. R. Kim, L. Kovacs, J. LaBreque, V. Masolov,

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Y. Nogi, M. Purucker, P. Taylor, M. Torta, “ADMAP - Magnetic anomaly map of the Antarctic, 1:10,000,000 scale map,” Morris, P. and R. von Frese., eds., BAS (Misc) 10., Cambridge, British Antarctic Survey, 2002.

Kim, H. R., von Frese, R.R.B., J.W. Kim, P.T. Taylor, T. Neubert, “0rsted verifies regional magnetic anomalies of the Antarctic lithosphere,” Geophys. Res. Lett., (in press).

FIELDS OF STUDY

Major Field: Geological Sciences

Studies in: Geoelectric Methods Prof. Jeffrey Daniels Remote Sensing Profs. Ken Jezek & Carolyn Merry Paleomagetism and Rheology Prof. Hal Noltimier Potential Field Geophysics Prof. Ralph von Frese Geodynamics Prof. Ian Willans Geotectonics Prof. Terry Wilson

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS

Page

A b stra c t ...... ii

D edication ...... iv

Acknowledgments ...... v

V i t a ...... vi

List of Tables ...... x

List of Figures ...... xi

Chapters:

1. General Introduction ...... 1

2. 0rsted Satellite Magnetometer Observations Verify Regional Magnetic Anomalies of the Antarctic Lithosphere ...... 4

2.1 Introduction ...... 5 2.2 Data Processing for Lithospheric Components ...... 7 2.2.1 Orbital data processing ...... 7 2.2.2 Map data processing ...... 21 2.3 Discussion ...... 26 2.4 Conclusions ...... 33

3. Comprehensive Assessment of Lithospheric Anomalies from Antarctic Satel lite Magnetometer D a ta ...... 35

3.1 Introduction ...... 36

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3.2 Estimating Crustal Components from Regional Magnetic Observations 38 3.3 Discussion ...... 53 3.4 Conclusions ...... 61

4. Utility of Satellite Magnetic Observations for Estimating Near-Surface Magnetic Anom alies ...... 65

4.1 Introduction ...... 66 4.2 Magnetic Anomaly Inversion ...... 70 4.3 Near-Surface Magnetic Anomaly Simulations ...... 74 4.3.1 Joint inversion of magnetic anomalies ...... 79 4.4 ADMAP Coverage Gap Predictions ...... 90 4.5 Summary and Conclusions ...... 99

5. Crustal Analysis of Maud Rise from Combined Satellite and Near-Surface Magnetic Survey D a ta ...... 103

5.1 Introduction ...... 104 5.2 Magnetic Modeling of the Crust ...... 107 5.2.1 0rsted anomaly modeling ...... 109 5.2.2 Modeling the near-surface magnetic anomalies ...... 116 5.3 Integrated Magnetization Contrasts ...... 121 5.4 Regional Geology and Magnetization Variations ...... 127 5.5 Crustal Magnetic Anomaly Perspectives with Altitu d e ...... 133 5.6 Tectonic Implications ...... 141 5.7 Conclusions ...... 145

6. Summary and recommendations ...... 147

Bibliography ...... 151

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF TABLES

Table Page

2.1 Pass-to-pass processing of Antarctic 0rsted magnetic observations for lithospheric anomalies ...... 20

2.2 The alphabetical identifiers, affiliated geological/geographical features, and relative anomaly polarities in parentheses are listed below for the correlative 0rsted and Magsat anomalies of the Antarctic in Figure 2.12. 30

4.1 Performance statistics for using minimum curvature (Figure 4.5. A) and Magsat (Figure 4.6.A), 0rsted (Figure 4.8.B), and CHAMP (Figure 4.11.A) magnetic anomalies to fill a simulated gap in aeromagnetic anomaly coverage. The prediction statistics include the root-mean- square (RMS) difference in nT and the correlation coefficient (CC). . 78

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF FIGURES

Figure Page

2.1 Histogram of 0rsted scalar magnetic anomaly values from the A) as cending and B) descending orbits over the austral winters of 1999 and 2000 8

2.2 Orbit data variances against I

2.3 Core field model 0rsted 99c (Olsen et al., 2000) to degree and order 13 updated to 1999.0 at 700 km altitude. Grid interval of geomagnetic intensities in nT (colored) is 2°x 2°. Degrees of inclination (thick lines) and declination (dashed lines) are also given ...... 12

2.4 Two spatially adjacent 0rsted satellite tracks with designated pass numbers 6283 and 5446 are compared for comparable lithospheric mag netic anomalies. Panels A and B give the map and altitude coordinates, respectively, for the two passes. Panel C compares the pass amplitudes from the satellite measurements. Panels D an d ...... 14

2.4 (cont.): E give the core field estimates and the corresponding pass residuals, respectively. Panels F and G give the third order polyno mials that were fitted to the core field residuals and the subsequently adjusted pass residuals, respectively. Panel H gives the lithospheric anomaly estimates from correlation filtering of the residuals in Panel G. 15

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2.5 Three spatially adjacent 0rsted satellite tracks with designated pass numbers 5085, 5847 and 5340 are compared for comparable lithospheric magnetic anomalies. Panels A and B give the map and altitude coor dinates, respectively, for the three pass. Panel C compares the pass amplitudes from the satellite measurements. Panels D a n d ...... 16

2.5 (cont.): E give the core field estimates and the corresponding pass residuals, respectively. Panels F and G give the third order polyno mials that were fitted to the core field residuals and the subsequently adjusted pass residuals, respectively. In panel G, pass 5847 was re jected and the next pass, 5340, was compared. Panel H gives the two lithospheric anomaly estimates from correlation filtering of the selected residuals in Panel G ...... 17

2.6 Distributions of A) ascending and B) descending tracks used to esti mate Antarctic lithospheric anomalies from 0rsted satellite magnetic d a ta ...... 19

2.7 0rsted anomalies gridded from the correlation filtered A) ascending and B) descending passes by least squares collocation ...... 22

2.8 Correlation filtered A) ascending and B) descending 0rsted anomalies. 24

2.9 A) Ascending anomaly map of Figure 2.8. A and B) descending anomaly map of Figure 2.8.B adjusted for coherent long wavelength differences in Figure 2.10.A due to nonlithospheric effects ...... 25

2.10 A) Anomaly differences (Figure 2.8.A - Figure 2.8.B) low-pass filtered for roughly 14°and larger wavelengths. The bold circle indicates the location of the geomagnetic south pole off the coast of Wilkes Land. B) 0rsted magnetic anomalies of degree 13 and larger for the Antarctic lithosphere ...... 27

2.11 Track-line noise in the A) ascending and B) descending anomaly data obtained by subtracting Figure 2.10.B from Figures 2.9.A and 2.9.B, respectively ...... 28

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2.12 Degree 13 and larger scalar total magnetic anomalies for the Antarctic south of 55°S from A) Magsat at 430-km altitude with 1-nT contour interval, and B) 0rsted at 700-km altitude with 0.5-nT contour inter val. Data gaps out to about 87°S occur because both satellite mis sions were not completely polar orbiting. Annotations for correlative anomaly features are given in Table 2.2 ...... 29

3.1 Data reduction scheme for extracting lithospheric anomalies and up dated degree 11-13 core field components from polar satellite magne tometer data ...... 40

3.2 Logarithmic spectrum of degree n geomagnetic field power ( Rn) at the surface of the Earth from Magsat data (adapted from Langel and Estes, 1982). Significant overlap between degrees 11 and 15 may occur in the core field and long wavelength crustal field components ...... 41

3.3 A) Antarctic 0rsted scalar total field magnetic anomalies (nT) rela tive to the spherical harmonic core field model 0rsted99c (Olsen et al., 2000) at degree 11. Annotations include the amplitude maximum (MAX), minimum (MIN), mean (AM), and amplitude standard devia tion (ASD). B) Intensity differences (nT) obtained by subtracting the dgreel3+ from degree 11+ components in the core field model, where the magnetic effects due to crustal thickness variations are presumably strongly intermixed ...... 43

3.4 A) Antarctic 0rsted scalar total field magnetic anomalies (nT) rela tive to the spherical harmonic core field model 0rsted99c (Olsen et al., 2000) at degree 13. B) Degrees 11-13 scalar total field magnetic anomaly differences obtained by subtracting Figure 3.4. A. from Figure 3.3.A 45

3.5 A) Compensated terrain gravity effects (mGals) for the Antarctic (von Frese et al., 1999a) at 400 km altitude. B) First vertical derivatives (nGals/m) of the compensated terrain gravity effects of the Antarctic at 700 km altitude...... 46

3.6 A) Scalar pseudo magnetic effects (nT) of the compensated terrain gravity effects of the Antarctic at 700 km altitude. B) Orsted scalar magnetic anomalies from Antarctic crustal thickness variations. . . . 48

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3.7 A) 0rsted scalar comprehensive lithospheric magnetic anomalies of the Antarctic at 700 km altitude. B) Antarctic 0rsted magnetic anomaly differences (Figure 3.4.B - Figure 3.6.B) that probably are dominated by core field effects, but also may reflect additional lithospheric and external field contributions ...... 49

3.8 A) Possible residual core field effects obtained by low-pass filtering the Antarctic 0rsted scalar total field magnetic anomaly differences in Figure 3.4.B for 1400 km and longer wavelength components. B) Complementary noise in the Antarctic 0rsted scalar total field mag netic anomaly differences in Figure 3.4.B with wavelengths shorter than about 1400 km ...... 51

3.9 Antarctic Magsat scalar total field magnetic anomalies (nT) relative to the spherical harmonic core field model GSFC 12/83 (Langel and Estes, 1985) at degrees 11 (A) and 13 (B) at 400 km altitude ...... 52

3.10 A) Degree 11-13 scalar total field magnetic anomaly differences ob tained by subtracting Figure 3.9.B from Figure 3.9.A. B) First vertical derivatives (nGal/m) of the compensated terrain gravity effects of the Antarctic at 400 km altitude ...... 54

3.11 A) Total field pseudo magnetic effects (nT) of the compensated terrain gravity effects of the Antarctic at 400 km altitude. B) Total field Magsat anomalies from the Antarctic crustal thickness variations. . . 55

3.12 A) Magsat scalar comprehensive magnetic anomalies of the Antarctic at 400 km altitude. B) Antarctic Magsat magnetic anomaly differences. (Figure 3.10.A - Figure 3.11.B) that probably are dominated by core field affects but also may reflect additional lithospheric and external field contributions ...... 57

3.13 A) Possible residual core field effects obtained by low-pass filtering the Antarctic Magsat scalar total field magnetic anomaly differences in Figure 3.10.A for 400 km and larger wavelength components. B) Complementary noise in the Antarctic Magsat scalar total field mag netic anomaly differences of Figure 3.10.A with wavelengths shorter than about 400 km ...... 58

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3.14 Antarctic DRTP magnetic anomalies from A) 0rsted (Figure 3.7.A) and B) Magsat (Figure 3.12.A) data. Alphabetically labelled anomaly features are discussed in the text ...... 60

3.15 Degree 13 core field estimates from A) the 0rsted99c model (Olsen et al. 2000) and B) the Magsat GSFC 12/83 model (Langel and Estes, 1985) at sea level over the Antarctic updated to 1999.0 and 1980.0, re spectively. Geomagnetic field intensities are shaded, while inclinations and declinations are marked by thick black and dashed white contours, respectively ...... 62

4.1 Comparison of single-field continuations of regional (A) aeromagnetic and (B) Magsat magnetic anomalies at respective altitudes of 2 km and 400 km centered on Kursk, Russia. Equivalent point dipole inversion was used to (C) downward continue the Magsat data to 2 km and (D) upward continue the aeromagnetic data to 400 km in spherical Earth coordinates ...... 68

4.2 The near-surface ADMAP anomalies over the Antarctic ...... 71

4.3 A) ADMAP aeromagnetic anomalies (nT) over the Weddell Sea at 2 km above sea level. The grid interval for these anomalies is 5 km. B) The coordinates of the long wavelength ADMAP aeromagnetic anomalies over the Weddell Sea. The anomaly locations are spaced approximately 200 km in both longitudinal and latitudinal directions. The red dots delineate a simulated coverage gap and the locations at which we seek effective near-surface magnetic anomaly predictions. The distribution of spherical crustal prisms used for the anomaly inversion is also shown. 75

4.4 A) ADMAP aeromagnetic anomalies (nT) at 2 km altitude over Wed dell Sea low-passed filtered for 400 km and larger wavelengths. Listed attributes for the map include the minimum (MIN) and maximum (MAX) amplitudes and amplitude mean (AM), and amplitude stan dard deviation (ASD). B) ADMAP aeromagnetic anomalies (nT) at 5 km. The anomalies that our simulations seek to estimate are within the white-bordered area ...... 76

4.5 A) Regional ADMAP aeromagnetic anomaly predictions from mini mum curvature. B) Minimum curvature prediction errors obtained by subtracting Figure 4.5.A from Figure 4.4.B ...... 77

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4.6 A) Simulated Magsat anomalies at 400 km altitude with 3 nT errors. B) Near-surface magnetic anomaly estimates at 5 km altitude for the coverage gap (white bordered area) by joint inversion of simulated near surface anomaly data outside the gap and Magsat anomaly simulations at 400 km altitude...... 80

4.7 A) Magsat trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the correlation coefficient (CC) and RMS difference of the predictions within the hole (dashed blue lines referring to the left vertical axes) and the surrounding vicinity (solid green lines referring to the right vertical axes) ...... 81

4.8 A) Gap anomaly differences obtained by subtracting the Magsat-based estimates of Figure 4.6.B from the ‘true’ anomaly values in Figure 4.4.B. B) Simulated 0rsted anomalies at 700 km altitude with 0.3 nT errors...... 82

4.9 0rsted trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the correlation coefficient (CC) and RMS difference of the predictions within the hole (dashed blue lines referring to the left vertical axes) and the surrounding vicinity (solid green lines referring to the right vertical axes) ...... 83

4.10 A) Near-surface magnetic anomaly estimates at 5 km altitude for a coverage gap (white bordered area) by joint inversion of simulated near-surface anomaly data outside the gap and 0rsted anomaly sim ulations at 700 km altitude. B) Gap anomaly differences obtained by subtracting the 0rsted-based estimates of Figure 4.10.B from the ‘true’ anomaly values in Figure 4.4.B ...... 85

4.11 A) Simulated CHAMP anomalies at 350 km altitude with 0.3 nT er rors. B) Near-surface magnetic anomaly estimates at 5 km altitude for a coverage gap (white bordered area) by joint inversion of simulated near-surface anomaly data outside the gap and CHAMP anomaly sim ulations at 350 km altitude ...... 86

4.12 CHAMP trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the correlation coefficient (CC) and RMS difference of the predictions within the hole (dashed blue lines referring to the left vertical axes) and the surrounding vicinity (solid green lines referring to the right vertical axes) ...... 88

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4.13 Gap anomaly differences obtained by subtracting the CHAMP-based estimates of Figure 4.11.B from the ‘true’ anomaly values in Figure 4.4.B 89

4.14 0rsted comprehensive lithospheric magnetic anomalies at 700 km from Chapter 3 (Figure 3.7.A) ...... 91

4.15 ADMAP magnetic anomalies at 5 km altitude low-passed filtered for 400 km and longer wavelengths ...... 92

4.16 Distribution of regional ADMAP anomalies of Figure 4.15 resampled approximately 200 km in both longitudinal and latitudinal directions. Numbers mark the regional coverage gaps where estimates were devel oped by joint inversion of the 0rsted and available regional ADMAP d ata...... 94

4.17 Error variance (EV) spectra for the ADMAP coverage gaps or holes. For each hole, a cross marks the ‘optimal’ EV-value for developing the best anomaly predictions from the joint inversion of the 0rsted and regional ADMAP anom alies ...... 95

4.18 Regional ADMAP magnetic anomaly grid with coverage gaps filled in by joint inversion using 0rsted lithospheric anomalies at 700 km altitude. 97

4.19 Regional ADMAP magnetic anomaly grid with coverage gaps filled in by minimum curvature ...... 98

4.20 Differences in the gap anomaly predictions obtained by subtracting Figure 4.18 from Figure 4.19 ...... 100

4.21 Antarctic magnetic anomaly map at 5 km altitude that includes the superposition of the 0rsted-based predictions of Figure 4.18 and the high-pass filtered (< 400 km) ADMAP anomalies ...... 101

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 5.1 Stereographically projected bathymetry of the southwest Indian Ocean from the NOAA/NGDC 5 arc minute digital terrain model. The thin white bathymetric contours are at 1000 m intervals. The thick black border delineates the study area. Annotated features include AG (Ag ulhas Plateau); AP (Antarctic Peninsula); AR (Astrid Ridge); CL (Coats Land); CR (Conrad Rise); DML (Dronning Maud Land); EE (Explora Escarpment); EL (Enderby Land); GR (Gunnerus Ridge); KS (Kainanmaru Seamount); MAR (Madagascar Ridge); MB (Mozam bique Basin); MOZ (Mozambique Ridge); MP (Mozambique Plateau); MR (Maud Rise); RLS (Riiser-Larsen Sea); SF (Sveshjfella); SOI (South Oakney Islands); SWIOR (Southwest Indian Ocean Ridge); and WSE (Weddell Sea Embayment) ...... 106

5.2 Magnetic anomalies in nT over the study area from A) 0rsted (Fig ure 2.10.A) and B) near-surface ADMAP observations. The ADMAP anomalies were low-pass filtered for 500 km and larger wavelengths. . 108

5.3 A) Crustal thickness data from von Frese et al. (1999) used by our inversions with the study area outlined. B) Distribution of spherical crustal prisms used for the anomaly inversions. Blue-colored oceanic prisms were modeled with a 0.03 SI susceptibility, while the red-colored continental prisms were modeled with a 0.01 SI susceptibility ...... 110

5.4 A) Predicted scalar total field magnetic effects of crustal thickness variations at 700 km altitude. B) Residual anomalies obtained by subtracting the magnetic crustal thickness effects (Figure 5.4.A) from the observed 0rsted anomalies (Figure 5.2.A) ...... 112

5.5 Paleopolarization (A) inclinations and (B) declinations in degrees used in modeling magnetization contrasts in the oceans. Note that in the blank areas off coastlines, we used the core field attitudes (Figure 5.13) because of the lack of paleoattitude data ...... 113

5.6 A) Magnetization contrast model for the residual 0rsted anomalies of Figure 5.4.B contoured at 0.5 A/m intervals. The black dashed line delineates the KQZ boundary. B) Scalar total magnetic anomalies in nT modeled by the superposition of the crustal thickness effects and the effects of the magnetization contrasts in Figure 5.6.A at 700 km altitude...... 115

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 5.7 A) Unmodeled 0rsted anomalies in nT obtained by subtracting Figure 5.6.B from Figure 5.2.A. B) The scalar magnetic anomaly predictions at 5 km altitude from the induced and the remanent magnetizations of the 0rsted anomalies modeled in Figure 5.6.B ...... 117

5.8 A) Residual near-surface magnetic anomaly differences obtained by subtracting the anomaly predictions in Figure 5.7.B from Figure 5.2.B. B) Magnetization contrasts contoured at 0.3 A/m intervals as obtained from the joint inversion of Figure 5.7.A and 5.8.A. The black dashed line delineates the KQZ boundary...... 118

5.9 Trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the compliment to the correlation coefficient (1 - CC) and the standard deviations (SD) of the solution susceptibility contrasts (As) in SI. The curves are color coded to the vertical axes of the plot ...... 120

5.10 Superposition of crustal magnetizations (left panels) as well as their corresponding magnetic anomalies at 1 km altitude (right panels). . . 122

5.11 A) Integrated remanent intensities of magnetization in A/m from Fig ure 5.6.A and the remanent components of Figure 5.8.B. B) Integrated induced magnetizations from the crustal thickness magnetizations and the induced components of Figure 5.8.B ...... 124

5.12 Remanent magnetic effects in nT from Figure 5.11.A at A) 700 km and B) 5 km altitudes...... 125

5.13 0rsted99c core field A) inclinations and B) declinations in degrees for 1999.0 ...... 126

5.14 Induced magnetic effects in nT from Figure 5.12.B at A) 700 km and B) 5 km altitudes...... 128

5.15 Modeled magnetic anomalies from the induced and remanent magne tization contrasts at A) 700 km and B) 5 km altitudes ...... 129

5.16 Residual anomalies in nT that are not accounted for by our magnetiza tion modeling in the A) 0rsted and B) regional near-surface ADMAP data of Figures 5.2.A and 5.2.B, respectively...... 130

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 5.17 Adjusting satellite anomaly magnetizations for related near-surface anomaly magnetizations ...... 131

5.18 Magnetic anomaly predictions in nT from the combined magnetization model at altitudes of A) 5 km, B) 10 km, C) 25 km, D) 50 km, E) 100 km, F) 200 km, G) 400 km, and H) 700 km ...... 134

5.18 (continued) ...... 135

5.18 (continued) ...... :...... 136

5.18 (continued) ...... 137

5.19 Bias of joint inversion anomaly estimates to satellite and near-surface magnetic anomalies. These biases are expressed in terms of the corre lation coefficients between the predictions and the 0rsted anomalies at 700 km altitude (dashed curve) and the regional near-surface ADMAP anomalies at 5 km (solid curve) ...... 139

5.20 Normalized amplitude spectra from magnetic surveys flown at Magsat (450 km), U2 (20 km), and conventional airborne DNAG (1 km) alti tudes (adapted from Hildenbrand et al., 1996) ...... 140

5.21 Comparison of 0rsted magnetic anomalies of the Maud Rise (MR) with the Magsat/POGO anomalies over the Agulhas Plateau (AG) at 700 km altitude. The comparison is made on the 93 Ma plate recon struction model of Martin and Hartnady (1986) when a triple junction was detaching the Maud Rise and the northern Agulhas Plateau was forming (Tucholke et al., 1981). Double thick lines mark the presumed spreading ridges ...... 143

5.22 Jurassic plate reconstruction of East Antarctica and Africa from Mar tin and Hartnady (1986) with superposed Antarctic 0rsted and South African Magsat/POGO magnetic anomalies at 700 km altitude. . . . 144

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 1

GENERAL INTRODUCTION

The Antarctic is the most poorly understood region of the planet due to its re

moteness, harsh environment, and nearly complete (£ 99%) cover of snow, ice and

sea water. Hence, magnetic and other geophysical data are greatly useful for geolog

ical studies of the Antarctic. Satellite magnetic surveys, in particular, offer a unique

window on the regional Antarctic geology in terms of nearly uniformly distributed

observations collected over relatively short periods of time with minimal corruption

of the lithospheric anomaly components from the secular variations of the core field.

Until very recently, satellite magnetic studies of the Antarctic lithosphere relied

almost exclusively on magnetometer observations collected by NASA’s seven-month

Magsat mission that was launched in November, 1979. Unfortunately for Antarctic

geological applications, the Magsat mission was operated during austral summer and

fall when Iarge-amplitude external field activity was at a maximum in corrupting the

lithospheric anomaly components.

The first real test of the veracity of the Magsat anomalies of the Antarctic litho

sphere came with the February, 1999 launch of Denmark’s 0rsted satellite magnetic

mission. In Chapter 2, we process the higher altitude (650-865 km) 0rsted magnetic

data from the austral winter periods for spherical harmonic degree 13 and higher

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. lithospheric anomalies. With these results, we verify the commensurate lithospheric

anomaly components in the lower altitude (350-550 km) Magsat data. We also in

vestigate the role of the more numerous, but higher altitude 0rsted measurements in

effectively separating core, lithospheric, and external magnetic field components for

enhanced studies of the Antarctic lithosphere.

The degree 13+ lithospheric anomaly components are a relatively incomplete mag

netic picture of the lithosphere. These components do not include the more regional

lithospheric effects due to crustal thickness variations in the degree 11 to 13 range of

the satellite observations that are incorrectly ascribed to core field effects. In Chapter

3, we investigate the role of satellite altitude gravity data for separating the degree

11-13 components into crustal thickness and improved core field estimates. To fa

cilitate lithospheric studies of the Antarctic, we also combine these crustal thickness

effects with the degree 13+ lithospheric anomaly estimates in the Magsat and 0rsted

data for comprehensive lithospheric anomaly maps at 400 km and 700 km altitude,

respectively.

The geologic utility of the satellite magnetic data will be greatly enhanced by the

multinational efforts of the Antarctic Digital Magnetic Anomaly Project (ADMAP)

to compile all available airborne, shipborne, and terrestrial magnetic survey data. In

Chapter 4, we consider the use of satellite magnetic observations for augmenting the

regional coverage gaps in the ADMAP compilation of near-surface surveys. We de

velop a procedure for effectively estimating near-surface anomaly values in unmapped

areas from the joint inversion of satellite and near-surface survey data. We also find

that, relative to the Magsat data, the 0rsted data offer significant advantages for this

application because of their greatly enhanced measurement accuracy.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In Chapter 5, we consider the combined application of satellite and near-surface

magnetic anomalies for crustal modeling of the Maud Rise that lies in the South

west Indian Ocean olf East Antarctica. The crustal properties of this feature provide

important insight on the Cretaceous tectonic development and breakup of Gond-

wana. Maud Rise also involves an extensive Cretaceous Quiet Zone of remanently

magnetized crust that complicates magnetic modeling at both near-surface and satel

lite altitudes. However, the combined analysis of these data by joint inversion gives

unique qualitative and quantitative insights on the magnetic properties of the crust

for the Maud Rise that may be extrapolated via the satellite data for new tectonic

perspective on the Cretaceous tectonic development of the conjugate Agulhas Plateau

off the South African coast in the Southwest Indian Ocean.

In Chapter 6, we summarize the results of this study. We also present our con

clusions and make recommendations for extending our study of the geologic utility of

satellite magnetic anomalies.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 2

0RSTED SATELLITE MAGNETOMETER OBSERVATIONS VERIFY REGIONAL MAGNETIC ANOMALIES OF THE ANTARCTIC LITHOSPHERE

Abstract

Magnetic measurements from the 0rsted satellite mission reveal lithospheric anoma

lies over the Antarctic that are similar to those obtained by Magsat. This result

indicates that lithospheric anomalies can be extracted from the 0rsted data, despite

the much greater operational altitude of 0rsted (650-865 km) relative to Magsat

(350-550 km). Furthermore, these correspondences confirm the lithospheric origins

for the resulting small-amplitude anomalies in the satellite data. In studies of the

Antarctic lithosphere, the Magsat data were particularly limited by the relatively

large uncertainties of their lithospheric components. These uncertainties occurred

because the short nearly seven-month mission more than 20 years ago collected data

over austral high summer and early fall when the contaminating large-amplitude

external field variations were at a maximum. Therefore, these recent and more nu

merous 0rsted measurements greatly facilitate our efforts to separate effectively the

core, lithospheric, and external field components for enhanced studies of the Antarctic

lithosphere.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2.1 Introduction

Since February 23, 1999, Denmark’s first satellite, 0rsted, has been providing

high-precision vector and scalar geomagnetic field measurements over nearly polar

orbits at altitudes between roughly 650-865 km (Neubert et al., 2001). Delays caused

the satellite to be launched nearer to solar maximum then originally planned. Hence,

the orbital altitude was increased to insure longer mission life by reducing atmospheric

drag and enhancing the performance of the attitude control system. Scalar and vector

magnetic field data are being recorded by an Overhauser and a compact flux-gate

spherical coil magnetometer, respectively. Satellite attitude information is measured

with an innovative star-imager (Neubert et ah, 2001).

The 0rsted measurements augment previous data obtained by NASA’s POGO

(Polar Orbiting Geophysical Observatory) and Magsat satellites. POGO scalar mag

netic field data were collected in a series of missions, flown from 1967 to 1971 with

altitudes ranging from 410 to 1100 km, whose goal was to map the core magnetic

field and its variation (Cain et ah, 1967; Langel, 1990). The POGO data, however,

also revealed small magnetic anomalies that appeared to reflect regional lithospheric

features (Regan et ah, 1975). Hence, Magsat was launched on October 30, 1979 to

map these small anomalies in greater detail over a period of nearly seven months at

altitudes between 352-561 km (e.g., von Frese et ah, 1982; Meyer et ah, 1985; Lan

gel, 1990; Purucker et ah, 1999). While both scalar total field and vector magnetic

data were gathered, attitude errors significantly degraded the vector measurements.

Hence, in this study we focus only on comparing the scalar total field anomalies from

Magsat and 0rsted for their lithospheric components.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Magsat geomagnetic field observations include a massive (97.8 %) core field con

tribution, significant (< 2 %) external field components, and very weak (~ 0.2 %)

lithospheric signals (von Frese et al., 1999a). Magsat lithospheric anomalies range

typically between + /- 20 nT while the 0rsted signals vary by about +/-3 nT (Taylor

et al., 2000). Errors for the scalar anomaly values are about 3 nT for Magsat (Langel,

1990) and 0.3 nT or less for 0rsted (Neubert et al., 2001).

Lithospheric anomaly errors predominantly result from errors in modeling the core

and external field contributions (Alsdorf et al., 1994). These errors are especially

problematic over the poles due to the ubiquitous presence of highly dynamic external

fields produced by the auroral electrojets, field-aligned currents and large-scale ring

currents (e.g., Langel and Hinze, 1999). The core and external fields cannot be

modeled with sufficient sensitivity at present to extract accurate lithospheric anomaly

estimates (Alsdorf et al., 1994; von Frese et al., 1999a). The problem of extracting

lithospheric components is particularly critical in the Antarctic Magsat data that were

obtained during austral summer and fall when south polar external field activity was

at a maximum.

However, we can achieve effective separation of these polar magnetic fields by

statistically exploiting the coherent or static properties of lithospheric anomalies and

the core field relative to the dynamic signals of the external fields (Alsdorf et al., 1994;

von Frese et al., 1999a). In particular, we use spectral correlation theory (von Frese

et al., 1997) to differentiate static from dynamic spatial and temporal components in

the polar geomagnetic observations.

In this study, we isolate the lithospheric field signals for spherical harmonic degree

13 and higher from the external and other noise components based on the static

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. properties of these lithospheric field signals relative to the external field and noise

components that are dynamic in space and time. In reducing the 0rsted observations

for the lithospheric field anomalies, we use the procedures of Alsdorf et al. (1994)

that we updated for the enhanced removal of track-line noise (Kim et al., 1998) and

other effects.

In the following sections, we describe the reduction of the lithospheric magnetic

anomalies of degree 13 and larger from the 0rsted data. We also compare these results

with the Magsat data at 430 km (Alsdorf et al., 1994) for geological commonalities

to confirm the veracity of satellite lithospheric observations for the Antarctic.

2.2 Data Processing for Lithospheric Components

We processed scalar total field data from the Overhauser magnetometer over the

Antarctic region south of 55°S. These efforts focus on extracting lithospheric field

signals directly from the individual orbits, as well as maps made from various subsets

of the orbital data.

2.2.1 Orbital data processing

Magsat observations had been collected over a six month period during austral

summer and fall when the large and dynamic external fields of the Antarctic were

especially agitated by the passage of the solar winds through the Earth’s magneto

sphere (Langel and Hinze, 1999). These external fields significantly corrupted the

crustal contributions to the Magsat magnetometer measurements. For 0rsted, we

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. x 10 A. Ascending Data Set

June July August

X 104 Descending Data Set

June July Auaust

Figure 2.1: Histogram of 0rsted scalar magnetic anomaly values from the A) ascend ing and B) descending orbits over the austral winters of 1999 and 2000.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. used data from the austral winter periods where the external field activity is rela

tively minimal. The selected data were partitioned into two subsets according to pass

orientation. The ascending data set was taken from orbits progressing from north

east to southwest, while the descending data set was from orbits progressing from

southeast to northwest. For Magsat, this division facilitated effective suppression of

the influences of asymmetric external fields at different local times in the reduction

for lithospheric anomalies (Yanagisawa and Kono, 1985; Arkani-Hamed et al., 1985;

Maeda et al., 1985; Alsdorf et al., 1994). Figure 2.1 shows the distributions of both

ascending and descending data by month for the 1999 and 2000 austral winter periods.

External field activity is described in terms of planetary indices (e.g., Kp or Ae) de

termined at ground-based geomagnetic observatories (Mayaud, 1980). These indices

are also commonly used in selecting orbits for lithospheric anomaly studies (Cohen

and Achache, 1990; Counil et al., 1989; Ravat et al., 1995). However, the distribu

tion of geomagnetic observatories is regionally biased to the northern hemisphere, so

that little or no correlation is evident in the Antarctic between the planetary indices

and orbital variances that provide another measure of external field disturbance in

the satellite data (Alsdorf et al., 1994). Figure 2.2 shows this poor correlation also

for the Antarctic 0rsted data. The correlation coefficients between the orbital vari

ances and Kp indices are roughly 0.27 and 0.36 for the ascending and descending data

sets, respectively. In view of these poor correlations, we processed the 0rsted orbits

without consideration of their planetary indices.

The orbital data were statistically screened for noisy orbits and erroneous data

‘spikes.’ The cleaned data passes were then geographically sorted for pass-to-pass

correlation filtering. The core field component to spherical harmonic degree and

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2000 10000 0.32 = 0.28 = 1500 307/953 6000 8000 90/321 1000 ++ 4000 Orbital variance (nT2) Orbital variance (nT2) Orbital +++ 500 B. Accepted B. descending passes + + -H- 2000 -IH- + + «- -HW4HHH- -HW4HHH- + + -fUBHIH ■++ + + ■++ -fUBHIH 10 20«mW«H+-HH- 40 70 H- 60 ++ 50 20 60 .E .E 30 2000 10000 0.35 0.26 1500 = = 6000 8000 55/211 335/952 1000 4000 Orbital variance (nT2) Orbital Orbital variance (nTz) Orbital C. C. Rejected ascending passes Rejected D. descending passes A. Acceptedascending A. passes 500 it t it i -H- + -H- 2000 -H- llll » t I t » llll -H -+ + Mil III I I li t t ItI Ill I «.»! li I I III Mil H B t l t f l r l I I I R It 11 T KfHH- -tt* + -tt* - - H - + +KfHH- + + + + 4M-++ •B- + -H ■ H t-++H - «HW-+ -H- + -H- «HW-+ m iiiiai ii i . I I■ l > l l l I O O c \ M j IIB M I m i I- ■II 70 60 50 1- + 60 50 70 S 40 * 3 0 « 4fii mmiiii 13 13 = (Kp = 1+), 17 = (I

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. order 13 was estimated and removed from the passes using the 0rsted99c model

(Olsen et al., 2000). Figure 2.3 shows the attributes of this core field model at Orsted

altitude (700 km). In general, the core field model also includes and therefore removes

regional lithospheric signals with degree smaller than 13 (Langel and Hinze, 1999).

Hence, long wavelength errors remain in the residual observations that provide an

incomplete lithospheric anomaly field (Alsdorf et al., 1994). In particular, significant

components with the wavelengths greater than 2500 km were still evident in the

0rsted residual anomalies. These regional signals may reflect core field modeling

errors because they do not appear to be related to regional geology (Alsdorf et al.,

1994).

To remove these regional components more effectively, we implemented polynomi

als that were fitted by least squares methods to the residual anomalies. This approach

was borrowed from our earlier Magsat efforts to derive lithospheric anomalies during

the three-year period following the mission when the Magsat main field model (Langel

et al., 1982) was still under development and generally unavailable.

Specifically, polynomials up to degree 3 were fitted to each pass and the residuals

between two adjacent passes were taken. The residual amplitudes were compared

to the amplitude range of the Antarctic Magsat lithospheric anomalies at 430 km

(Alsdorf et al., 1994) upward continued to 0rsted altitude by equivalent poince source

inversion (von Frese et al., 1981b). 0rsted passes with residual amplitudes that

exceeded the Magsat-predicted lithospheric anomaly range of roughly 6 nT predicted

were rejected for further processing.

Figure 2.4 shows an example for two spatially adjacent, ascending 0rsted passes

that we designated by orbit numbers 6283 and 5446, respectively. Figures 2.4.A and

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. MIN =22,938 MAX = 47,824 AM =37,848 ASD = 6,730

> 46000 I 44000 - 46000 I 42000 -44000 BSSS3 40000 - 42000 □ 38000 - 40000 C 3 36000 - 38000 E M 34000 - 36000 ' 32000 - 34000 30000 - 32000 28000 - 30000 26000 - 28000 24000 - 26000 24000

180°W

Figure 2.3: Core field model 0rsted 99c (Olsen et al., 2000) to degree and order 13 updated to 1999.0 at 700 km altitude. Grid interval of geomagnetic intensities in nT (colored) is 2°x 2°. Degrees of inclination (thick lines) and declination (dashed lines) are also given.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2.4.B give the map and altitude distributions for the passes, respectively, whereas

the raw pass observations and related core field estimates are given in Figures 2.4.C

and 2.4.D, respectively. Removing these core field estimates from each pass yields the

residuals in Figure 2.4.E with greatly different amplitudes. However, these long wave

length residuals that are strongly correlated (CC = 0.95) can not reflect lithospheric

signals that in general are only a few nT in amplitude at 0rsted satellite altitudes.

However, fitting and removing the degree 3 polynomials in Figure 2.4.F yields the

residuals in Figure 2.4.G that are significantly improved in amplitude compatibility.

Figure 2.5 shows another example for three adjacent ascending passes with desig

nated orbit numbers 5085, 5847 and 5340. In this case, pass 5847 was rejected from

our analysis because it is significantly out of phase with respect to the two other

passes. The phase relationships between the three pass residuals are quantitatively

indicated by the correlation coefficients (CC) given in Figure 2.5.G.

Spectral correlation filtering (von Frese et al., 1997) can further improve the cor

relation of anomalies between neighboring passes. For two adjacent passes, X and Y,

the filters can be designed from their respective wavenumber domain representations,

X and Y, using the correlation spectrum

CC(k) = cos{A9k), (2.1)

where (CC (k)) and (A0&) are the correlation coefficient and phase difference, re

spectively, between the two &-th wavenumber components. For this application, fast

Fourier transforms can be used to obtain X and Y with absolutely no loss of gener

ality because the correlation spectrum depends only on the phase differences between

co-registered, orthogonally gridded representations of the passes X and Y. We used

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. -50 -55 -55 -50 5446 5446 -70 -65 -60 Degrees Latitude 6283 6283 -75 -85 -80 -75 -70 -65 -60 -85 -80 3.2 3.8 3.4 880 860 840 800 780 760 820 £ CO o 5446 180°W 6283 -70 -65 -60 -55 -50-85 -80 -75 -70 -65 -60 -55 -50-85 Degrees Latitude Degrees Latitude 0 5 3.2 3.8 c 3.4 Figure 2.4: Two spatially adjacent 0rsted satellite tracks with designated pass numbers 6283 and 5446 are compared for comparable lithospherictwo passes. magnetic Panel anomalies. C compares Panels the A passand amplitudesB give the frommap theand satellitealtitude measurements. coordinates, respectively, Panels D forand the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. -50 -50 -55 6283 5446 Degrees Latitude Degrees Latitude -75 -70 -65 -60 C.C. C.C. = 0.95 C.C. C.C. = 0.95 -80 H. -85 -80 -75 -70 -65 -60 -55 -85 2.5 0.5 -0.5 C i- Id -50 ------1 -55 5446 ------. 6283 ------. ------. 5446 Degrees Latitude Degrees Latitude ------. C.C. C.C. = 0.65 6283 ------. C.C. C.C. = 0.95 E. G. ------85 -80 -75 -70 -65 -60 -55 -50 -85 -80 -75 -70 -65 -60 401 140 120 100 2.5 0.5 -0.5 -1.5 c t- c Figure 2.4: (cont.):give the third E give orderthe polynomialscore field thatestimates were andfitted theto thecorresponding core field residualspass residuals,and the respectively. subsequently adjusted Panels F passand residuals, G respectively. Panel H gives the lithospheric anomaly estimates from correlation filtering of the residuals in Panel G. On

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 3.4 3.6 3.8 700 750 800 850 -60 -50 5847 -60 -50 5085 -70 -70 5847 5085 Dearees Latitude Degrees Latitude 5340 5340 -80 -80 D. B. -90 -90 3.8 3.6 *- *- 3.6 4.2 680 660 700 720 740 760 780 800 c i- E 3.4 3.6 3.8 5847 -60 -50 -70 180 W 5340 Dearees Latitude 508! -80 -90 3.6 L3.6 3.8 4.2 X c o I- the three pass. Panel C compares the pass amplitudes from the satellite measurements. Panels D and Figure 2.5: Three spatiallyfor comparable adjacent lithospheric 0rsted satellitemagnetic tracks anomalies. with designated Panels passA numbersand B 5085, give5847 theand 5340map are comparedand altitude coordinates, respectively, for a

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100 -50 -0.5 0.5 -50

-60 atitude 5085 C.C. C.C. =0.92 Degrees Latitude Degrees'Ll CC(5085,5847) = -0.22 CC(5085,5340) = 0.77 CC(5340,5847) = 0.43 5340

1 0 -90 -80 -85 -80 -75 -70 -65 -60 -55 -50 _1 1 ■ 1 . . . 1_ 20 c t— 100 -50 50 -5 -50

5847 t5340 5847 CC(5085,5847) = -0.42 CC(5085,5340) = 0.81 CC(5340,5847) = 0.26 Degrees'Latitude ^ Degrees Latitude 5085 CC(5085,5847) = -0.23 CC(5085,5340) = 0.77 CC(5340,5847) = 0.42 -80 -70 -60 -50 5085 5340 - l 1 0 2 -90 -80 -1 -1 40 80 60 Figure 2.5: (cont.): E give the core field estimatesanomaly estimatesand thefrom correspondingcorrelation filtering pass ofresiduals, the selected respectively. residuals in Panel Panels G. F and G give the third orderrespectively. polynomials In panel that G, pass were 5847fitted was rejected to andthe thecore nextfield residualspass, 5340, andwas compared.the subsequently Panel H givesadjusted the two lithosphericpass residuals,

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the geodetic interpolation routines of Kim (1995) to obtain these representations of

X and Y.

Analysis of the correlation spectra between neighboring tracks suggested the use of

correlation filters that passed all wavenumber ( k) components for which CC(k) > 0.5

for estimating the lithospheric components. Figures 2.4.H and 2.5.H give examples

of the lithospheric anomaly estimates correlation filtered from the residual signals in

Figures 2.4.G and 2.5.G, respectively.

For orbits that are close to each other (< 70 km) relative to the distance to the

lithosphere (> 700 km) where the lithosphere is also the only source of spatially

and temporally static magnetic anomalies, the increase in correlation coefficients in

Figures 2.4.H and 2.5.H provides a measure of the improvement in the ratios of

lithospheric signal-to-nonlithospheric noise obtained by the processing. Specifically,

the signal-to-noise ratio (S/N) can be estimated (e.g., Kim, 1995) from inverting

~s ~ i\cc\~1' ^ Hence, the correlation filtering of the polynomial residuals in Figure 2.4.G and 2.5.G

produced corresponding outputs in Figure 2.4.H and 2.5.H where the presumed non-

lithospheric noise levels are reduced by about 68% and 40%, respectively.

Table 2.1 summarizes the effects of the pass-to-pass processing on the Antarctic

0rsted data. For example, from the original 1163 ascending orbits, the analysis

retained 952 passes that are distributed over the study region as shown in Figure

2.6.A. Similarly for the original 1274 descending orbits, the pass-to-pass processing

retained 883 passes distributed as in Figure 2.6.B. Roughly 42% of these retained

passes would have been rejected if the orbits were selected only for Kp < 2+. Figures

2.2.A and 2.2.B indicate that 35% and 32% of our accepted ascending and descending

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2.6: Distributions of A) ascending and B) descending tracks used to estimate Antarctic lithospheric anomalies from 0rsted satellite magnetic data.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Orbits Ascending Descending Total # of Passes 1163 1274 Rejected # of Passes 211 391 Statistics < V AR > < VAR > < C C > CF Adjusted 282 nT2 0.69 314 nT 2 0.68 Polyn. Adjusted 1.96 nT2 0.42 1.80 nT 2 0.47 CC - Filtered 0.98 nT2 0.81 1.14 nT2 0.83

Table 2.1: Pass-to-pass processing of Antarctic 0rsted magnetic observations for lithospheric anomalies.

orbits, respectively, would have been rejected by this planetary index criterion. On

the other hand, this criterion would also have accepted 26% and 28% of our rejected

ascending and descending passes, respectively, as shown by Figures 2.2.C and 2.2.D.

Table 2.1 also shows that the polynomial adjustments reduced the average residual

variances () from 282 nT2 to 1.96 nT2 and 314 nT2 to 1.80 nT 2 for the

ascending and descending data sets, respectively. In addition, correlation coefficient

(CC) filtering of the polynomial adjusted residuals further reduced the mean pass

variances while improving the average signal-to-noise ratios of the ascending and

descending data tracks by 58% and 57%, respectively.

For the core field (CF) adjusted residuals, the relatively strong mean correlation

coefficients in Table 2.1 reflect the regionally static errors in the core field model.

As mentioned previously, ascribing these residuals to lithospheric features seems un

realistic because their amplitudes are much too large and they display no apparent

sensitivity to the regional geology. Additional insight on the core field modeling errors

is indicated by the residual signals in Figure 2.4.E. Here the core field reduction yields

significantly larger amplitude residuals for the higher altitude pass (6283) relative to

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the lower altitude pass (5446), which is clearly contrary to the magnetic anomaly

behavior of lithospheric features.

2.2.2 Map data processing

The correlation filtered data tracks were then gridded at a common altitude of

700 km by least squares collocation (Goyal et al., 1990). The maps for the ascending

and descending data tracks are shown in Figures 2.7.A and 2.7.B, respectively. Sig

nificant anomaly features related to the geology of the Antarctic regions are evident

in both maps. However, the correlation coefficient between them is only 0.35, which

is statistically significant at the 99.9 % confidence level.

The low correlation coefficient suggests the presence of external field components

that may be coherent within each of the ascending and descending data sets, but also

much less coherent between the two data sets. Hence, maps produced at different

local magnetic times, such as represented by the ascending and descending data sets,

are commonly compared as a means for identifying further external field components

in the satellite magnetic observations (Arkani-Hamed et al., 1985; Alsdorf et al.,

1994). To reduce the satellite magnetic data for these external field components,

we correlation filtered Figures 2.7.A and 2.7.B using a pass cutoff of CC(k) > 0.66.

Figures 2.8.A and 2.8.B show the results that are now correlated at CC = 0.74.

In the Magsat data, the longer wavelength differences between the correlation

pass-filtered dawn (i.e., descending) and dusk (i.e., ascending) data maps revealed

symmetric patterns about the south geomagnetic pole that were interpreted for co

herent standing wave effects of the external fields in the two data sets (Alsdorf et al.,

1994). These differences existed because the dusk orbits sampled the solar energized

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN =-4.161 MAX = 5.195 AM = 0.0 ASD = 0.86

> 5 4 - 5 3 - 4 2 - 3

1 - 2

0 - 1

- 1-0 -2 --1 -3 - - 2 -4 - - 3 < -4

180°W

(B) MIN =-2.775 MAX = 2.928 AM = 0.0 ASD = 0.80

m > 3 M 2.5 - 3 2 - 2.5 1.5 - 2 □ 1 - 1.5 [ ^ 3 0.5 - 1 MSI 0 - 0.5 IsJEjJSEJ -0.5 - 0 HBSB -1 - -0.5 IBM -1.5 - -1 m EB -2 - -1.5 HR -2.5 - -2 <: -2.5

180 W

Figure 2.7: 0rsted anomalies gridded from the correlation filtered A) ascending and B) descending passes by least squares collocation.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. geomagnetic field on the daytime side of the dawn-dusk terminator relative to the

dawn orbits that passed through the less energized field on the nighttime side of the

terminator. A simple least squares leveling adjustment was used to remove these

regional standing wave effects that further improved the anomaly correspondences

between the dawn and dusk Magsat data (Alsdorf et al., 1994).

For the higher altitude 0rsted data, the differences between Figures 2.8.A and

2.8.B do not simply reflect nighttime and daytime external field differences because

the 0rsted orbits cover all local magnetic times. However, in studying the differences

between Figures 2.8.A and 2.8.B, we found that their longer wavelength components

revealed dramatically decreased sensitivity for the regional geology of the Antarctic.

An example in given Figure 2.10.A where these differences were low-pass filtered for

wavelengths of roughly 14° and larger.

The differences in Figure 2.10.A were removed accordingly from Figures 2.8.A

and 2.8.B using the least squares leveling procedure of Alsdorf et al. (1994) to obtain

the adjusted anomalies in Figures 2.9.A and 2.9.B, respectively. This adjustment

improves the correlation coefficient to 0.89 between the ascending and descending

anomalies. Furthermore, it normalizes the energy differences in the two data sets as

expressed by their respective anomaly standard deviations (ASD) so as to minimize

the bias in combining the results in Figures 2.9.A and 2.9.B for a final 0rsted magnetic

anomaly map of the Antarctic lithosphere.

To combine Figures 2.9.A and 2.9.B into a lithospheric anomaly map, we used

the spectrum reconstruction method of Kim et al. (1998) rather than simple aver

aging as had been previously done for the Magsat data (Alsdorf et al., 1994). This

procedure minimizes track-line or corrugation noise due to the various along-track

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -2.59 MAX = 2.69 AM = 0.00 ASD= 0.70

> 2.5 2 - 2.5 1.5 - 2 1 - 1.5 I I 0 . 5 - 1 H 0-0.5 -0.5 - 0 -1 - -0.5 -1.5 - -1 -2 - -1.5

< -2

180 W (B)

MIN =-2.31 MAX = 2.15 AM = -0 .0 0 ASD = 0.63

> 2.5 2 - 2.5 1.5 - 2 'msa 1 - 1.5 0.5 - 1 0 - 0.5 -0.5 - 0 -1 - -0.5 -1.5 - -1 -2 - -1.5

< -2

180°W

Figure 2.8: Correlation filtered A) ascending and B) descending 0rsted anomalies.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -2.69 MAX = 2.65 AM = 0.00 ASD = 0.66

> 2.5 2 - 2.5 1.5 - 2 1 - 1.5 0.5 - 1 0 - 0.5 -0.5 - 0 -1 — 0.5 -1.5 - -1 -2 — 1.5

< -2

180°W

(B) MIN = -2.13 MAX = 2.20 AM = 0.00 ASD = 0.61

> 2.5 2 - 2.5 1.5 - 2 1 - 1.5 0.5 - 1 0 - 0.5 -0.5 - 0 -1 - -0.5 -1.5 - -1 -2 — 1.5

< -2

180°W

Figure 2.9: A) Ascending anomaly map of Figure 2.8. A and B) descending anomaly map of Figure 2.8.B adjusted for coherent long wavelength differences in Figure 2.10.A due to nonlithospheric effects.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. processing efforts (e.g., polynomial removal, track-to-track correlation filtering). For

each ascending and descending data grid, the along-track errors are predominant in

only two of the corresponding spectral quadrants. However, the most strongly cor

rupted quadrants are mutually exclusive between these two data sets because the

ascending and descending orbits cross each other. Hence, the two cleaner quadrants

from each data set can be recombined into a single spectrum that when inversely

transformed yields a scalar total field anomaly map with greatly reduced along-track

errors. Figure 2.10.B gives our 0rsted magnetic anomaly estimates for the Antarctic

lithosphere as derived from Figures 2.9.A and Figure 2.9.B by the spectral quadrant

swapping method. Subtracting Figure 2.10.B from Figures 2.9.A and 2.9.B yields

Figures 2.11.A and 2.11.B, respectively, that show the deleted track-line noise effects.

2.3 Discussion

Figures 2.12 compares our degree 13 and larger 0rsted magnetic anomalies with

those of the Magsat mission from Alsdorf et al. (1994). These anomalies are super

posed on the Antarctic topography surface free from snow, ice and sea water from von

Frese et al. (1999c). The remarkable geologic correspondences between the magnetic

anomalies of the two missions are highlighted in Figure 2.12 by alphabetical labels

that are listed in Table 2.2. These satellite anomalies are generally consistent with

the large-scale lithospheric features of the Antarctic, although long wavelength dis

tortions from residual core and coherent external field effects cannot be totally ruled

out.

Over East Antarctica, for example, prominent anomaly minima mark Queen Maud

Land (A) and the Gamburtsev Subglacial Mountains (B). The Pensacola Basin is

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -0.56 MAX = 0.81 AM = 0.00 ASD = 0.23

HHB > 0.5 EBB 0.4 - 0.5 m 0.3 - 0.4 0.2 - 0.3 □ 0.1 - 0.2 m m 0 - 0.1 m -0.1 - 0 ms -0.2 - -0.1 o n m -0.3 - -0.2 ms -0.4 - -0.3 BHBIH 9 -0.5 - -0.4 m < -0.5

180 W

(B)

MIN = -2.131 MAX = 2.428 AM = -0.00391 ASD = 0.6497

> 2.5 2 - 2.5 1.5 - 2 1 - 1.5 0.5 - 1 0 - 0.5 -0.5 - 0 -1 — 0.5 -1.5 - -1 -2 - -1.5

< -2

180 W Figure 2.10: A) Anomaly differences (Figure 2.8.A - Figure 2.8.B) low-pass filtered for roughly 14°and larger wavelengths. The bold circle indicates the location of the geomagnetic south pole off the coast of Wilkes Land. B) 0rsted magnetic anomalies of degree 13 and larger for the Antarctic lithosphere. 27

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -0.61 MAX = 0.63 AM = 0.00 ASD = 0.19

BO > 0.6 BB 0.5 - 0.6 BBS 0.4 - 0.5 0.3 - 0.4 (= □ 0.2 - 0.3 0.1 - 0.2 m 0 - 0.1 m at -0.1 - 0 iMBtl -0.2 --0.1 n -0.3 - -0.2 BO -0.4 — 0.3 KSB -0.5 — 0.4 B c -0.5

180 W

(B) MIN = -0.86 MAX = 0.91 AM = 0.00 ASD = 0.18

M > 0.6 BBB 0.5 - 0.6 w a 0.4 - 0.5 0.3 - 0.4 cm 0.2 - 0.3 cm 0.1 - 0.2 wm 0 - 0.1 iSBaa -0.1 - 0 WBW -0.2 --0.1 BBB -0.3 — 0.2 -0.4 — 0.3 IBS -0.5 — 0.4 BO <-0.5

180°W

Figure 2.11: Track-line noise in the A) ascending and B) descending anomaly data obtained by subtracting Figure 2.10.B from Figures 2.9.A and 2.9.B, respectively.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2.12: Degree 13 and larger scalar total magnetic anomalies for the Antarctic south of 55°S from A) Magsat at 430-km altitude with 1-nT contour interval, and B) 0rsted at 700-km altitude with 0.5-nT contour interval. Data gaps out to about 87°S occur because both satellite missions were not completely polar orbiting. Annotations for correlative anomaly features are given in Table 2.2. 29

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Label Features Label Features

(A) Queen Maud Land (-) (B) Gamburtsev Mountains (-)

(E) Wikes Land (+) (F) Prince Charles Mountains (+)

(G) Enderby Land (+) (H) Continental margin ocean basins (-)

(I) Filchner and (J) Antarctic Peninsula Ellsworth Microplates (-) Microplate (+)

(K) Thurston Microplate (+) (L) Marie Byrd Land Microplate and Byrd Subglacial Basin (+)

(M) Maud Rise (+) (N) Southern Crozet Plateau (+)

(0) Southern Kerguelen Plateau (+) (P) Pacific-Atlantic Ridge (+)

(Q) Southeastern Pacific (R) Southeastern Pacific Basin Maxima (+) Basin Minima (-)

(S) Transantarctic Mountains and Ross Sea Margin (-)

Table 2.2: The alphabetical identifiers, affiliated geological/geographical features, and relative anomaly polarities in parentheses are listed below for the correlative 0rsted and Magsat anomalies of the Antarctic in Figure 2.12.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. bordered by regional anomaly maxima on the northwest (C) and its opposite end (D)

between the Gamburtsev and Transantarctic Mountains.

Continent-ocean edge effect anomalies (e.g., Bradley and Prey, 1991) may be re

flected along the eastern margin of Antarctica by several maxima extending from

Wilkes Land (E) up to the Prince Charles Mountains (F) and Enderby Land (G)

flanked possibly by complementary oceanic basin minima (H). Quantitative magnetic

modeling of available crustal thickness data (von Frese et al., 1999c) suggests the

edge effect anomalies can be accommodated by 2-A/m crust that abruptly thins from

about 35 km beneath the continent to roughly 12 km under the oceans. However, to

model the oceanic minima (H) fully, an additional contrast in crustal magnetization

of about -1 A/m is required. The demagnetization may be facilitated by hydrother

mal alteration of the oceanic crust beneath the basin cover of thermally insulating

sediments (Levi and Riddihough, 1986).

These satellite anomalies also facilitate extrapolating tectonic information into

East Antarctica from better-studied components of Gondwana (Frey et al., 1983;

Galdeano, 1983; von Frese et al., 1986; 1987) and earlier supercontinents (von Frese

et al., 1997). A particularly striking example is the Wilkes Land anomaly maximum

(E) that shows a Gondwana correlation with comparable satellite magnetic anomalies

overlying Archean-Proterozoic cratonic blocks in south central and western Australia

(von Frese et al., 1986; 1987). Another prominent example involves maxima (C and

G) between the southern margin of the Weddell Sea and Enderby Land that show an

apparent late Precambrian association with the east-west band of satellite magnetic

maxima over the U.S. mid-continent (von Frese et al., 1987). The U.S. anomalies,

observed by both Magsat and 0rsted missions (Purucker et al., 2002), have been

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. related to the distribution of Middle Proterozoic granite-rhyolite rocks inferred from

limited deep drilling of the mid-continent (Starich et al., 1985; von Frese et al., 1997).

West Antarctica is recognized as a series of microplates related to the circum-

Pacific Mobile Belt that also appear to be well marked by regional magnetic anoma

lies (von Frese et al., 1999a). For example, the region of the Filchner and Ellsworth

Microplates is overlain by a prominent anomaly minimum (I), whereas magnetic max

ima delineate the Antarctic Peninsula (J) and Thurston (K) Microplates. In addition,

a magnetic maximum (L) overlies the region of the Marie Byrd Land Microplate and

Byrd Subglacial Basin. These anomaly maxima also appear to be complemented by

ocean basin minima (H) much like those along the margin of East Antarctica.

In the off shore areas, prominent maxima overlie the Maud Rise (M), the southern

Crozet (N) and Kerguelen (0) Plateaux, and major portions of the Pacific-Atlantic

Ridge (P). These maxima may reflect strongly magnetized, possibly serpentinized and

thickened oceanic crust. Additional correlative marine anomalies include the maxima

(Q) in the southeastern Pacific Basin, as well as a number of minima (R) extending

around the western margin of the study region.

Discrepancies between these two data sets involve mostly distorted anomaly pat

terns rather than a total lack of correlative anomaly features. A good example is

the Pacific-Atlantic Ridge that reflects maxima (P) more prominently mapped in the

0rsted than the Magsat data. The minima (S) over the Transantarctic Mountains,

on the other hand, extend further westwards across the Ross Ice Shelf and Sea in

the 0rsted data than in the Magsat data. Clearly, a major advantage of the 0rsted

mission is that additional austral winter cycles of observations will be obtained to

further limit the uncertainties in these anomalies for lithospheric analysis.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Another advantage for lithospheric anomaly studies of the 0rsted mission relative

to Magsat is its much longer duration that is yielding a significantly larger data set,

albeit at higher altitudes than the Magsat data. The greater amount of data facilitates

improving the signal-to-noise ratio of lithospheric anomalies, which increases as the

square root of the number of data points, as well as lithospheric studies from a variety

of restricted altitude ranges. As this report is being prepared 0rsted is still producing

valuable data.

2.4 Conclusions

In general, the 0rsted mission confirms the veracity of satellite magnetometer

observations for studies of the Antarctic lithosphere. A number of these anomalies

seem quite robust because they are also observed in the POGO maps (Purucker et ah,

1999) prepared from visually screened, lower altitude (< 600 km) orbits (Langel,

1990). However, the higher altitude POGO data may also yield additional details on

these anomalies, even though their use for lithospheric studies has been limited to

date (Regan et ah, 1975). According to our Orsted experiences at least, a remarkably

decreased level of non-lithospheric noise is observed in the Orsted data relative to the

lower altitude Magsat data. Hence, the higher altitude POGO data too may have

additional utility for lithospheric analysis.

As it was with Magsat, the Orsted mission will spawn considerable development

of improved methods and strategies for extracting lithospheric information from the

Orsted signals. The Orsted data are clearly an invaluable augmentation to the Magsat

and POGO data sets for our studies of the Earth’s lithosphere. These data sets

will greatly facilitate planned studies of the lithospheric components in the satellite

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. magnetometer observations from the recently launched CHAMP and SAC-C/0rsted-

2 missions (Reigber et al., 1996; Neubert and Ultre-Guerrard, 2000; Neubert et al.,

2001 ).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 3

COMPREHENSIVE ASSESSMENT OF LITHOSPHERIC ANOMALIES FROM ANTARCTIC SATELLITE MAGNETOMETER DATA

Abstract

Spatially and temporally static crustal magnetic anomalies are contaminated by

static core field effects above spherical harmonic degree 11 and the very dynamic,

large-amplitude external fields. To extract lithospheric magnetic anomalies from the

0rsted and Magsat satellite magnetic data, we define satellite anomalies relative to

the degree 11 field and use spectral correlation theory to reduce them for external field

effects. Crustal effects are predominant in the satellite anomaly components above de

gree 13. However, in the degree 13 through 11 anomaly components, core and regional

crustal effects can strongly interfere and be difficult to separate. To help separate

these components, we use the pseudo magnetic effect of a model of Antarctic crustal

thickness that we obtain by spectrally comparing the terrain gravity effects to free-air

gravity anomalies. From the correlation spectrum between the pseudo magnetic and

degree 11-13 satellite anomalies, we inversely transform positively correlated satel

lite wavenumber components for estimates of the magnetic crustal thickness effects

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. mapped by the satellites. Combining these crustal thickness effects with the degree

13 and higher components yields the 0rsted and Magsat comprehensive magnetic

anomalies of the Antarctic lithosphere at altitudes of 700 km and 400 km, respec

tively. The satellite anomalies in combination with the near-surface magnetic survey

compilation from the Antarctic Digital Magnetic Anomaly Project (ADMAP) pro

vide an important new coherent magnetic reference field for geological investigations

of the Antarctic.

3.1 Introduction

Satellite magnetometer observations from the earth-orbiting missions (i.e., POGO,

Magsat, Orsted, and CHAMP) provide significant constraints for understanding re

gional petrological variations of the crust and upper mantle, and crustal thickness and

thermal perturbations (e.g., von Frese et al., 1982; Mayhew et al., 1985; Langel, 1990;

Purucker et al., 1999). These polar-orbiting satellites have obtained especially dense

coverage of the polar regions up to latitudes of about 83° (Magsat and Orsted), 86°

(POGOs) and 85° (CHAMP). Hence, satellite magnetic data represent an important

augmentation to near-surface surveys for geological studies of the poorly mapped and

understood polar regions.

In general, crustal sources of satellite magnetic anomalies can have both inductive

and remanent components of magnetization. These sources may be predominantly in

the lower crust that is believed to be substantially more magnetic than the upper crust

(Wasilewski et al., 1979; Wasilewski and Mayhew, 1982; 1992; Mayhew et al., 1985).

As crustal depth increases, conditions for coherent inductive regional magnetization

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. are enhanced. Remanence and thermal overprints are diminished, and viscous mag

netization and initial susceptibility are enhanced as temperatures increase to within

about 100°-150° C of the Curie point of magnetite (« 570° C). The thickness of the

crust in this thermal regime of the Curie point may be 5 to 20 km depending on the

steepness of the geothermal gradient.

Accordingly, deep crustal magnetic sources are probably related to Curie isotherm

topography and lateral variations of petrologic factors. Viscous remanent magneti

zation in the lower crust is in-phase with the induced component. Hence satellite

magnetic anomalies due to lower crustal sources have geometries that may be treated

effectively in the context of induced magnetization. Errors in using the assumption

of induced magnetization will be confined mostly to interpreting the magnetization

intensities for these satellite anomaly sources.

Within the relatively weaker magnetic upper crust, remanently magnetized sources

tend to produce high frequency signals that are substantially attenuated at satellite

altitudes. Possible exceptions are the Cretaceous quiet zones that can involve large

areas of remanently magnetized, normal polarity oceanic crust (LaBrecque and Ray

mond, 1985). However, their occurrence within the study region is limited and their

remanent components are also predominantly in-phase with inductive magnetization.

Hence upper crustal sources of satellite magnetic anomalies may also be treated ef

fectively in the context of induced magnetization.

Satellite magnetic data may be important for improving regional or global com

pilations of near-surface magnetic surveys. Long wavelength anomalies from these

compilations are often seriously corrupted due to gaps in data coverage and errors in

data leveling and correcting for secular core field variations (e.g., Schnetzler et al.,

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1985; Grauch, 1993; Verhoef et al., 1996). However, such errors can be reduced by

the use of satellite magnetic observations because they provide relatively uniform re

gional coverage and are taken over periods where secular variations of the core field

are negligible.

Hence, satellite magnetometer data can facilitate the efforts of the Antarctic Digi

tal Magnetic Anomaly Project (ADMAP) that is working to integrate more than one

million line kilometers of near-surface magnetic survey data into a magnetic anomaly

map for the Antarctic (Johnson et al., 1996; 1997; Chiappini et al., 1998). In partic

ular, Antarctic satellite magnetic observations can be used to develop a geologically

coherent reference anomaly field to help augment gaps in coverage and improve the

merger of disparate near-surface surveys into regional- and continental-scale compos

ite compilations where the spectral properties of the magnetic anomalies of the south

polar lithosphere are developed as fully as possible.

Applications of the Antarctic satellite magnetic data such as described above

require effective separation of lithospheric components from the core and external

field components. In the sections that follow, we develop procedures for extracting

lithospheric components from the degree 11 and higher satellite magnetic data. We

also consider the geologic utility of these satellite altitude magnetic anomalies of the

Antarctic lithosphere.

3.2 Estimating Crustal Components from Regional Magnetic Observations

Satellite altitude geomagnetic field observations include core, lithospheric, and

external field components along with measurement error. Lithospheric signals are

considerably weaker than the core field and external components that taken together

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. constitute roughly 99.8 % or more of the total geomagnetic energy observed at satellite

altitudes. Antarctic lithospheric anomalies at 0rsted altitudes (~ 700 km) commonly

vary between +/-3 nT (e.g., Taylor et al., 2000) in a background of core field variations

spanning 28,000 - 64,000 nT and superposed external field signals ranging typically

between 100 - 200 nT. Hence, even with the measurement errors better than 0.3 nT

(Neubert et al., 2001), the errors in reducing satellite magnetic observations for their

lithospheric components can be quite significant.

Errors in estimating lithospheric anomalies are derived predominantly from er

rors in the core field model (Alsdorf et al., 1994). Errors in the core field model

and lithospheric anomaly estimates are especially exacerbated in the polar regions,

where the raw data are contaminated by highly dynamic external fields from auro

ral electrojets, field-aligned currents and large-scale ring currents. Hence estimating

lithospheric anomalies from satellite magnetic observations is quite difficult because

the core and external fields cannot be modeled with sufficient accuracy to extract

these relatively weak signals.

Presently, effective separation of the polar anomaly fields is best approached as

a statistical problem that exploits the coherent or static properties of lithospheric

anomalies (Alsdorf et al., 1994). This approach involves the use of spectral correlation

theory (von Frese et al., 1997) for differentiating these components from the spatially

and temporally dynamic effects of the polar external fields.

Figure 3.1 outlines this approach that was used to process the Antarctic 0rsted

and Magsat data for crustal magnetic anomalies in support of the near-surface mag

netic survey compilation efforts of ADMAP. Initial efforts identify the orbital tracks

across the study area for Austral winter periods of relatively reduced external field

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Magnetic Processing Gravity Processing

COMPENSATED TERRAIN R A W DATA GRAVITY EFFECTS (E G M 96 )

SE L E C T T R A C K S (for austral winter periods)

CORE FIELD CORE FIELD CORE FIELD (degree 13 + model) REMOVAL (degree 11+ m odel)

PASS-BY-PASS ANALYSIS

SPECTRUM RECONSTRUCTION

LITHOSPHERIC LITHOSPHERIC LITHOSPHERIC ANOMALY MAP ANOMALY MAP ANOMALY MAPS (degree 13+) (degree 11+)

(Subtract) DIFFERENCED ANOMALIES (degree 11 - degree 13) PSEUDO MAGNETIC EFFECTS (by Poisson's relation) SPECTRAL CORRELATION ANALYSIS

(A dd) M A G N ETIC CRUSTAL THICKNESS EFFECTS

COMPREHENSIVE LITHOSPHERIC MAGNETIC ANOMALIES

DRTP LITHOSPHERIC MAGNETIC ANOMALIES

Figure 3.1: Data reduction scheme for extracting lithospheric anomalies and updated degree 11-13 core field components from polar satellite magnetometer data.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 24

20 on ithospheric 16

0 0 5 10 15 20 25

degree n Figure 3.2: Logarithmic spectrum of degree n geomagnetic field power ( R n ) at the surface of the Earth from Magsat data (adapted from Langel and Estes, 1982). Sig nificant overlap between degrees 11 and 15 may occur in the core field and long wavelength crustal field components.

activity. These data are then screened for obvious measurement errors, despiked, re

formatted from time to spatial coordinates, and geographically sorted (Alsdorf et al.,

1994; Alsdorf and von Frese, 1994; Kim, 1996).

Efforts focus next on isolating the external fields by wavenumber correlation filter

ing of immediately adjacent passes and the further filtering of maps at varying local

times (Alsdorf et al., 1994). These passes are reduced for core field components to

degree 11 because the lower degree components tend to be minimally contaminated

by the long wavelength magnetic effects of the crust relative to the residual higher

degree (i.e., 12 and 13) core field components (e.g., Langel and Estes, 1982; Meyer

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. et al., 1985; Hinze et al., 1991). Figure 3.2 shows the geomagnetic field spectrum ob

tained by Langel and Estes (1982) where core field effects are commonly interpreted

to be dominant to degree 11 in contrast to lithospheric effects that are felt to pre

dominate at degrees 15 and higher. Separation of core and lithospheric field effects

in the residual components between degrees 11 and 13 is attempted after the satellite

magnetic data have been reduced for the dynamic external field effects.

After removing the core field components to degree 11 using the 0rsted99c model

(Olsen et al., 2000), the passes are sorted by local time into roughly dawn (i.e.,

descending) and dusk (i.e., ascending) data sets, placed into several altitude bins,

and arranged geographically for processing by the procedures of Alsdorf et al. (1994)

to suppress the external field effects. Where the distance between adjacent passes

is small (< 70 km) compared to the distance to the lithosphere (> 700 km), these

nearest-neighbor passes exhibit similar lithospheric and residual core field signals.

Hence, we use pass-to-pass correlation filtering to extract the correlative signatures

from adjacent passes.

Some polar external fields are coherent across the passes and must be removed by

filtering the maps at different local times (Alsdorf et al., 1994). With the 0rsted data,

the polar external fields are generally asymmetric across the two local magnetic time

periods sampled by the descending and ascending data sets. Therefore, we correlation

filtered the ascending and descending anomaly maps to further reduce the effects

from external fields. The filtered results were then spectrally reconstructed using a

quadrant-swapping method (Kim et al., 1998) to minimize track-line or corrugation

noise from the along track processing of the orbits. Figure 3.3.A shows the resultant

scalar total field magnetic anomalies of the Antarctic.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -13.32 MAX = 11.05 AM = 0.00 ASD = 4.212

am > 10 r a a 8-10 BBHBI 6 - 8 4 - 6 □ 2 - 4 EZ3 0 - 2 awl! -2 - 0 Kawi -4 - -2 BBBB -6 - -4 -8 - -6 ms -10 - -8 mm -12 --1 0 H■ <-12

180°W

(B) MIN = -8.745 MAX = 9.083 AM = 0.00 ASD = 2.874

> 8 6-8 4 - 6 □ 2 - 4

V s/t* y m r

-4 - -2 -6 - -4

-8 - -6

< -8

180°W Figure 3.3: A) Antarctic 0rsted scalar total field magnetic anomalies (nT) relative to the spherical harmonic core field model 0rsted99c (Olsen et al., 2000) at degree 11. Annotations include the amplitude maximum (MAX), minimum (MIN), mean (AM), and amplitude standard deviation (ASD). B) Intensity differences (nT) ob tained by subtracting the dgreel3+ from degree 11+ components in the core field model, where the magnetic effects due to crustal thickness variations are presumably strongly intermixed. ^

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Efforts focus now on reducing these scalar anomalies for residual core field and/or

coherent external field effects by isolating the magnetic anomalies related to crustal

thickness variations. Our interpretation of the geomagnetic spectrum in Figure 3.2

suggests that core field model components between degrees 11 and 13, which are

shown in Figure 3.3.B, may be strongly contaminated by the regional magnetic effects

of crustal thickness variations. Hence, we are particularly interested in studying our

anomaly components in degrees 11-13 for possible crustal thickness effects.

Figure 3.4.A shows the degree 13+ scalar total magnetic anomalies from Chapter

2 that presumably are dominated by magnetization effects in the lithosphere. The

differences between the degree 11+ and the degree 13+ Orsted anomalies are shown

in Figure 3.4.B that may reflect the strongly inferring effects of the core field and

regional crustal anomalies.

To extract the possible magnetic effects of crustal thickness variations in these

Orsted anomaly differences, we derived the pseudo magnetic effects of the thickness

variations from their gravity effects via Poisson’s relation for correlative potentials

(von Frese et al., 1981b). Poisson’s relation relates the first derivative of these gravity

effects in the direction of magnetization to their equivalent magnetic effects by their

magnetization-to-density ratios (von Frese et al., 1982). The pseudo magnetic effects

provide effective phase properties for designing spectral correlation filters to extract

the 0rsted anomaly components that may be related to the magnetic effects of the

crustal thickness variations (e.g., von Frese et al., 1999c).

The compensated Antarctic terrain gravity effects estimated by von Frese et al.

(1999c) in Figure 3.5.A gives the gravity effects of the Antarctic crustal thickness

variations. The first vertical derivatives (FVD) of these gravity effects that were

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -2.012 MAX = 2.428 AM = 0.00 ASD = 0.6314

BB > 2.4 BBSBI 2 - 2.4 KMi 1.6 - 2 iwam 1.2 - 1.6 CZ1 0.8 - 1.2 EZ23 0.4 - 0.8 0 - 0.4 -0.4 - 0 n -0.8 - -0.4 -1.2 — 0.8 BB -1.6 — 1.2 -2 - -1.6 s < -2

180°W

(B) MIN = -13.84 MAX = 11.08 AM = 0.00 ASD = 4.151

QB > 10 BB 8 - 1 0 {jflaiMi 6 - 8 4 - 6 [= □ 2 - 4 EZ3 0 - 2 - 2 - 0 saga -4 - -2 isaa -6 - -4 WM -8 - -6 B -10 - -8 BMB -12 --10 HS <-12

180°W

Figure 3.4: A) Antarctic 0rsted scalar total field magnetic anomalies (nT) relative to the spherical harmonic core field model 0rsted99c (Olsen et al., 2000) at degree 13. B) Degrees 11-13 scalar total field magnetic anomaly differences obtained by subtracting Figure 3.4.A. from Figure 3.3.A.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -326.5 MAX = 221.8 AM = 0.00 ASD = 163.3

BE9 > 200 IWBIU 150 - 200

W B w B I 100 - 150 [Sail 50 - 100 □ 0 - 50 m -50 - 0 m s -100 - -50 -150 --100 B -200 —150 m -250 —200 m -300 - -250 B&ai < -300

(B) MIN = -179.1 MAX = 154.6 AM = 27.84 ASD = 100.9

m > 150 HBKMBHB 120 - 150 te l 90 - 120 US] 60 - 90 CO o □ 30 I m 0 - 30 ms -30 - 0 B -60 - -30 BH -90 - -60 m -120 - -90 m -150 - -120 B <-150

180 W

Figure 3.5: A) Compensated terrain gravity effects (mGals) for the Antarctic (von Frese et al., 1999a) at 400 km altitude. B) First vertical derivatives (nGals/m) of the compensated terrain gravity effects of the Antarctic at 700 km altitude.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. obtained by equivalent point source (EPS) inversion (von Frese et al., 1981b; 1998)

are shown in Figure 3.5.B at 0rsted altitude.

By Poisson’s relation, the FVD gravity anomalies (Figure 3.5.B) reflect the scalar

magnetic total intensity anomalies of the crustal thickness variations reduced-to-the-

pole (RTP) where geomagnetic inclinations and declinations are 90° and 0°, respec

tively, at all source and observation points. To obtain the corresponding pseudo

magnetic total field anomalies in Figure 3.6.A, the equivalent point gravity poles

were re-evaluated for magnetic dipole effects assuming a constant volume magnetic

susceptibility for the dipoles under the variable inclinations, declinations and inten

sities of the 0rsted core field model (Olsen et al., 2000). Comparing Figures 3.5.B

and 3.6.A reveals significant regional anomaly differences. Unlike the RTP anomalies,

the scalar total field anomalies do not directly map out the magnetization variations

of the crust, and hence considerable care must be exercised in using satellite scalar

magnetic anomalies in geologic analysis.

Inversely transforming all wavenumber components of Figure 3.4.B that are posi

tively correlated with the pseudo magnetic wavenumber components of Figure 3.6.A

yields Figure 3.6.B. These phase-coherent results give our estimate of the total field

magnetic anomalies in the 0rsted data caused by crustal thickness variations. Adding

back the degree 13 and larger lithospheric 0rsted anomalies from Figure 3.4.A gives

the comprehensive lithospheric anomaly map in Figure 3.7.A.

The comprehensive anomalies are limited, of course, because they do not include

the degree 11-13 effects from other regional variations of lithospheric magnetization

that may include large-scale structural, petrological and thermal perturbations of the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -168 MAX =154.6 AM = 28.17 ASD = 81.79

ESI >• 150 m i 120 - 150 ■ 90 - 120 60 - 90 □ 30 - 60 in 0 - 30 m u -30 - 0 M -60 - -30 MBBBR ai -90 - -60 mmBBS -120 - -90 BB -150 — 120 BB < -150

180 W

(B) MIN = -4.851 MAX = 5.859 AM = 0.000 ASD = 2.041

> 5 4 - 5 3 - 4 2 - 3

1 - 2

0 - 1

- 1-0 -2 -- 1 -3 - - 2 -4 - - 3 < -4

180°W

Figure 3.6: A) Scalar pseudo magnetic effects (nT) of the compensated terrain gravity effects of the Antarctic at 700 km altitude. B) 0rsted scalar magnetic anomalies from Antarctic crustal thickness variations.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -4.919 MAX = 6.243 AM = 0.000 ASD = 2.089

> 5 4 - 5 3 - 4 2 - 3

1 - 2

0 - 1

- 1-0 -2 --1 -3 - - 2 -4 - - 3 <-4

180 W

(B) MIN = -10.42 MAX = 9.52 AM = 0.00 ASD = 3.929

BB > 10 8-10 m 6 - 8 1 § 4 - 6 □ 2 - 4 0 - 2 BBS -2 -0 ■ -4 - -2 s a i -6 - -4 BB -8 - -6 HB -10 - -8 ESI <-10

180°W

Figure 3.7: A) 0rsted scalar comprehensive lithospheric magnetic anomalies of the Antarctic at 700 km altitude. B) Antarctic 0rsted magnetic anomaly differences (Figure 3.4.B - Figure 3.6.B) that probably are dominated by core field effects, but also may reflect additional lithospheric and external field contributions.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. crust. However, these effects, if they exist, may be reflected in the residuals of Figure

3.7.B that were obtained by subtracting Figure 3.6.B from Figure 3.4.B.

In these residuals, only features with wavelengths greater than about 700 km can

originate from the lithosphere or core field. Figure 3.8. A gives these low-passed filtered

residuals, while Figure 3.8.B shows for completeness the complementary high-passed

filtered noise residuals.

The low-pass filtered residuals (Figure 3.8.A) are relatively well correlated with

the core field differences in Figure 3.3.B (CC = 0.39), but reveal little apparent

sensitivity for lithospheric features. Hence, we offer the regional residuals in Figure

3.8.A as improved estimates of the degree 11-13 core field components. Relative to

the original core field components in Figure 3.3.B, the improved estimates in Figure

3.8. A have been at least reduced for possible magnetic effects of the Antarctic crustal

thickness variations.

To facilitate geologic studies of the Antarctic further, we also processed the Magsat

data for comprehensive lithospheric magnetic anomalies at 400 km altitude. Accord

ingly, Figure 3.9.A and 3.9.B give the Magsat anomalies relative to the degree 11

and 13 components, respectively, of the GSFC 12/83 core field model (Langel and

Estes, 1985). The difference (Figure 3.9.A - Figure 3.9.B) in these anomaly estimates

is shown in Figure 3.10.A that presumably is dominated by strongly intermixed core

field and regional lithospheric components.

A major possible source for the regional lithospheric anomalies can be inferred

from the pseudo magnetic total field effects of the crustal thickness variations given in

Figure 3.11.A. The components of Figure 3.10.A that are phase coherent with these

pseudo magnetic anomalies are given in Figure 3.11.B that can reflect the crustal

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 0°

180°W

0°

180°W

Figure 3.8: A) Possible residual core field effects obtained by low-pass filtering the Antarctic 0rsted scalar total field magnetic anomaly differences in Figure 3.4.B for 1400 km and longer wavelength components. B) Complementary noise in the Antarc tic 0rsted scalar total field magnetic anomaly differences in Figure 3.4.B with wave lengths shorter than about 1400 km. 51

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -15.14 MAX = 31.18 AM = 0.00 ASD = 7.337

BB > 15 BBBS a 12 - 15 HBBHIBBBHl 9-12 6 - 9 □ 3 - 6 IS 0 - 3 laSBESlIsBsSd - 3 - 0 g m -6 - -3 B n BBBB -9 - -6 BBflKB -12 - -9 B B --15 --1 2 HB <-15

(B) MIN := -6 .6 3 3 MAX = 10.55

AM =• 0.00 ASD = 2.64

m > 7.5 BS BH 6 - 7.5 m 4.5 - 6 [i] 3 - 4.5 ED 1.5 - 3 i n 0 - 1.5 m i --1.5 - 0 m -3 — 1.5 CO o t BBBBW I 1 urn -6 — 4.5 m < -6 180°W

Figure 3.9: Antarctic Magsat scalar total field magnetic anomalies (nT) relative to the spherical harmonic core field model GSFC 12/83 (Langel and Estes, 1985) at degrees 11 (A) and 13 (B) at 400 km altitude.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. thickness magnetic effects in the degree 11-13 Magsat anomalies. Combining these

crustal thickness effects with the degree 13 and higher Magsat anomalies in Figure

3.9.B yields the comprehensive lithospheric anomaly map in Figure 3.12.A.

Subtracting the comprehensive lithospheric anomalies (Figure 3.12.A) from the

degree 11-13 differences (Figure 3.10.A) gives the residual anomalies in Figure 3.12.B.

The residual components with wavelengths of the distance to the lithosphere (~ 400

km) and larger contain enhanced estimates of the core field components plus any

additional large scale lithospheric effects. These regional residuals are shown in Figure

3.13.A, whereas the complementary high-pass filter noise residuals are given in Figure

3.13.B.

3.3 Discussion

Comprehensive 0rsted and Magsat magnetic anomalies for the Antarctic litho

sphere were obtained in Figure 3.7.A and 3.12.A, respectively. As suggested by our

comparison of the FVD gravity and related pseudo magnetic total field effects for

the crustal thickness variations in Figure 3.5.B and 3.6.A, respectively, geomagnetic

relationships between lithospheric sources and their scalar magnetic total field ef

fects at satellite altitudes can be greatly distorted by the inclination, declination, and

intensity variations of the polarizing core field. However, these distortions can be

minimized by reducing the scalar magnetic anomalies differentially to the radial pole

(von Frese et al., 1981b; 1998).

Differentially reduced-to-pole (DRTP) magnetic anomalies have induced anomaly

components that are centered over their lithospheric magnetic sources in the same

way that FVD gravity anomalies are centered over their sources of lithospheric mass

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -20.18 MAX = 31.61 AM = 0.00 ASD = 7.881

ifessan Bffl > 15 m 12 - 15 i i 9-12 m i 6-9 □ 3-6 mu 0-3 mu -3-0 m -6 - -3 Ksai -9 - -6 a a -12 - -9 B -15 --12 B <-15

(B) MIN = -356.4 MAX = 205.6 AM = 0.0 ASD = 162.4

> ro o o IB! 150 - 200 m 100 - 150 50 - 100 □ 0 - 50 m -50 - 0 m -100 - -50 r 1 m -150 o o BBIBB -200 - -150 UBS -250 - -200 m -300 - -250 BB <-300

180°W

Figure 3.10: A) Degree 11-13 scalar total field magnetic anomaly differences obtained by subtracting Figure 3.9.B from Figure 3.9.A. B) First vertical derivatives (nGal/m) of the compensated terrain gravity effects of the Antarctic at 400 km altitude.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -402.5 MAX = 193.3 AM = 0.00 ASD = 133.8

EBB s• 200 E B i 150 - 200 BSMi 100 - 150 IwmI 50 - 100 £ □ 0 - 50 ES3 -50 - 0 -100 - -50 -150 --100 ISBB -200 - -150 BHBI -250 - -200 BB -300 - -250 B ffl -350 - -300 < -350

(B) MIN = -12.79 MAX = 15.45 AM = 0.00 ASD = 5.207

n > 10 m 8-10 m 6 - 8 tel 4 - 6 □ 2 - 4 ms 0 - 2 m -2 -0 m -4 - -2 s i -6 - -4 HUB HB -8 - -6 MA -10 - -8 m <-10

180°W

Figure 3.11: A) Total field pseudo magnetic effects (nT) of the compensated terrain gravity effects of the Antarctic at 400 km altitude. B) Total field Magsat anomalies from the Antarctic crustal thickness variations.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. variations. The regional remanent magnetic anomaly components also will be source-

centered in the DRTP anomalies to the degree at least that they follow our presump

tion of being phase-coherent with the the polarizing core field.

Figures 3.14.A and 3.14.B give the DRTP magnetic anomalies derived from the

equivalent point dipole inversion (von Frese et ah, 1981b) of the 0rsted and Magsat

scalar anomalies in Figure 3.7.A and 3.12.A, respectively. The least squares inversion

related the scalar anomalies to a spherical coordinate distribution of point magnetic

susceptibility contrasts subjected to the variable polarizing effects of the core field.

The polarizing core field model used to obtain the 0rsted and Magsat DRTP magnetic

anomalies are shown in Figures 3.15.A and 3.15.B, respectively. The DRTP anomalies

in Figure 3.14. A and 3.14.B were then estimated by evaluating the derived point dipole

models assuming vertical inclination and zero declination of the respective core fields

at all source and observation points, as well as a constant 60,000 nT polarizing field

intensity.

Relative to the scalar total magnetic field anomalies, the DRTP anomalies have

been displaced along geomagnetic declination towards the south magnetic pole. The

DRTP anomalies presumably lie now directly over their lithospheric sources with

the anomaly polarities indicating the polarities of the magnetization contrasts of the

sources. Also the normalization of polarization intensity means that sources of equal

magnetization contrasts across the study area are characterized by equal amplitude

DRTP magnetic anomalies.

The DRTP anomalies are largely consistent between the two missions. Anomaly

discrepancies reflect the large altitude differences between the mission data sets, as

well as the quality of the observations. Relative to the Antarctic 0rsted data, for

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -13.44 MAX = 16.26 AM = 0.00 ASD = 5.301

HH > 10 8-10 ■ 6-8 m 4-6 2-4 m 0-2 -2-0 1 1 1 EBSaa ro CO f T 1 I MB 1 CO CO 1 1 m I

1 1 1 0 BB 00 H <-10

180°W

(B) MIN = -14.71 MAX = 24.32 AM = 0.00 ASD = 5.604

> 15 12 - 15 2 9-12 1 6 - 9 3 - 6 0 - 3 -3-0 -6 - -3 -9 - -6 -12 - -9 <-12

180°W

Figure 3.12: A) Magsat scalar comprehensive magnetic anomalies of the Antarctic at 400 km altitude. B) Antarctic Magsat magnetic anomaly differences (Figure 3.10. A - Figure 3.11.B) that probably are dominated by core field affects but also may reflect additional lithospheric and external field contributions.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -12.94 MAX = 19.74 AM = 0.00 ASD = 5.192

> 15 12 - 15 9-12 6 - 9 si 3 - 6 0 - 3 - 3 - 0 -6 - -3 -9 - -6 -12 - -9 <-12

(B) MIN = -7.38 MAX = 5.63 AM = -0.07 ASD = 2.06

BH > 10 M l 8-10 im 6 - 8 |ijS] 4 - 6 □ 2 - 4 mu 0 - 2 m -2 -0 mu -4 - -2 m -6 - -4 Mi -8 - -6 RH -10 - -8 H <-10

180°W

Figure 3.13: A) Possible residual core field effects obtained by low-pass filtering the Antarctic Magsat scalar total field magnetic anomaly differences in Figure 3.10.A for 400 km and larger wavelength components. B) Complementary noise in the Antarctic Magsat scalar total field magnetic anomaly differences of Figure 3.10.A with wave lengths shorter than about 400 km. 58

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. example, the Magsat data are quite noisy because they were collected during Austral

summer and fall when external field activity was maximum. Also the measurement

errors of the Magsat data are an order of magnitude greater than the errors in the

new satellite magnetometer observations. Hence, anomaly A at the northern tip of

the West Antarctic Peninsula may reflect errors in Magsat data or data processing,

or also locally positive lithospheric magnetization with effects that have died out

at 0rsted altitudes. New magnetometer observations from the recently launched

CHAMP mission will provide a further important test for the veracity of the Magsat

data.

For the most part, however, the DRTP 0rsted and Magsat anomalies are generally

consistent with the large scale geological features of the Antarctic region. The most

prominent positive feature in both satellite data sets, for example, is anomaly B

that overlies the Maud Rise, which was an oceanic hotspot off the Queen Maud

Land coast. This maximum appears to reflect the superposed effects due to enhanced

crustal thickening (Figures 3.6.B and 3.11.B) and higher frequency upper lithospheric

magnetizations (Figures 3.4.A and 3.9.B) that may reflect the westward extension of

strongly magnetized, possibly serpentinized, oceanic crust. Additional maxima overlie

Enderby Land (anomaly C) that appear to be related to regions of enhanced crustal

thickening. The upper lithospheric anomaly for Enderby Land suggests an offshore

extension of highly magnetized, possibly serpentinized, rifted platform crust.

Another prominent positive feature is anomaly D for the region north of the Gam

burtsev Mountains between the Transantractic Mountains and the western margin of

the Aurora Basin. This anomaly overlies roughly the third of East Antarctica that

appears to be underlain by anomalously thin crust (von Frese et al., 1999c). It is

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -6.081 MAX = 7.547 AM = 0.000 ASD = 2.403

> 5 4 - 5 3 - 4 2 - 3

1 - 2

0 - 1

- 1-0

-2 - - 1 -3 - - 2 -4 - - 3 < -4

180°W

(B) MIN = -18.4 MAX = 28.75 AM = 0.00 ASD = 7.12

m > 20 h i 16 - 20 ■ 12 - 16 on 8 - 12 on 4 - 8 m 0 - 4 m -4 - 0 -8 - -4 HI -12 - -8 m -16 --1 2 r a <-16

180°W

Figure 3.14: Antarctic DRTP magnetic anomalies from A) 0rsted (Figure 3.7.A) and B) Magsat (Figure 3.12.A) data. Alphabetically labelled anomaly features are discussed in the text.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. centered on a localized region of relatively thicker crust (Figures 3.6.B and 3.11.B)

that is surrounded by strongly magnetized and thinner, possibly rifted crust.

In the offshore areas, prominent minima such as anomalies E and F overlie the

marine basins off Marie Byrd Land and Wilkes Land. These minima probably map de

magnetization effects related predominantly to crustal thinning, although hydrother

mal alteration of the oceanic crust beneath the basin cover of thermally insulating

sedimentary rock may also contribute (Levi and Riddihough, 1986).

In West Antarctica, the Marie Byrd Land and Thurston Island Microplates (Dalziel

and Elliot, 1982; Ritzwoller and Bentley, 1982) are overlain by the positive regional

anomalies G and H, respectively, in both satellite maps. These anomalies mark the

magnetically enhanced crust of East Marie Byrd Land and Thurston Island that

formed part of the Mesozoic convergent margin of Gondwana which apparently sep

arated during the Early Cretaceous leaving a paleomagnetic imprint on a number of

widely dispersed volcanic outcrops (Grunow et al., 1991).

3.4 Conclusions

A new approach for separating core, lithospheric and external field components

in satellite anomaly measurements has been investigated. It involves using spectral

correlation filters to remove dynamic external field effects from static lithospheric

components in the degree 13 and higher anomalies, as well as from static lithospheric

and core field components in the degree 11 and higher anomalies. Subtracting the

static degree 13+ anomalies from the static degree 11+ anomalies leaves anomaly dif

ferences dominated presumably by strongly intermixed core field and regional litho

spheric anomaly components.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = 28400 MAX = 67130 AM = 52510 ASD = 9599

> 68000 I 64000 - 68000 gfffj 60000 - 64000 F ~ | 56000 - 60000 r ~ | 52000 - 56000 48000 - 52000 H 44000 - 48000 40000 -44000 36000 -40000 32000 -36000 < 32000

180 W

(B) MIN = 28410 MAX = 66470 AM = 52070 ASD = 9440

> 68000 64000 - 68000 60000 -64000 ■I AI 56000 - 60000 [~~1 52000 - 56000 Hill 48000 - 52000 44000 - 48000 | 40000 - 44000 36000 - 40000 32000 -36000 < 32000

180°W

Figure 3.15: Degree 13 core field estimates from A) the 0rsted99c model (Olsen et al. 2000) and B) the Magsat GSFC 12/83 model (Langel and Estes, 1985) at sea level over the Antarctic updated to 1999.0 and 1980.0, respectively. Geomagnetic field intensities are shaded, while inclinations and declinations are marked by thick black and dashed white contours, respectively. 62

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A significant source of these regional lithospheric anomaly components includes

the magnetic effects of Antarctic crustal thickness variations. The thickness vari

ations can be inferred from the spectral correlation of free-air and terrain gravity

effects at satellite altitude. Subtracting the terrain-correlated free-air anomalies from

the terrain gravity effects yields the compensated terrain gravity effects with the

phase properties of the gravity effects of the crustal thickness variations. Evaluating

the first derivatives of the compensated terrain gravity effects in the directions of

geomagnetic polarization reveals the phase properties of the pseudo magnetic effects

of the crustal thickness variations. Inversely transforming the wavenumber compo

nents in the degree 11-13 differences that are positively correlated with wavenumber

components of these pseudo magnetic effects resulted in our estimates of the crustal

thickness effects in the satellite magnetic data.

A relatively comprehensive estimate of the lithospheric components in the satellite

observations is obtained by combining the crustal thickness magnetic effects with the

degree 13+ anomalies. Differentially reducing the lithospheric magnetic anomalies

to the radial pole also facilitates relating the satellite observations to features of the

lithosphere.

The comprehensive lithospheric magnetic anomalies obtained in this investigation

are generally consistent with regional geologic features of the Antarctic. Prominent

magnetic minima are found over marine basins that may be related to crustal thinning

and other demagnetizing effects. Magnetic maxima characterize the Antarctic Penin

sula Microplate, Maud Rise, and Enderby Land as regions of mostly enhanced crustal

thickness. Another positive anomaly, which may reflect a regional variation in crustal

petrology, overlies roughly the third of East Antarctica between the TYansantarctic

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Mountains and 90°E that appears to involve anomalously thin crust. The positive

anomalies over Thurston Island and East Marie Byrd Land may reflect widespread

crustal intrusions associated with the Early Cretaceous separation of the microplates

when they formed part of the Pacific convergent margin of Gondwana.

Further insight on the magnetic properties of the Antarctic lithosphere is becom

ing available from the efforts of the Antarctic Digital Magnetic Anomaly Project

(ADMAP) that is working to compile a near-surface anomaly map from disparate

shipborne, airborne, and terrestrial magnetic survey data. In the next section, we

consider the utility of satellite magnetic anomalies for filling in or augmenting the

regional coverage gaps in the compilation of near-surface magnetic surveys.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 4

UTILITY OF SATELLITE MAGNETIC OBSERVATIONS FOR ESTIMATING NEAR-SURFACE MAGNETIC ANOMALIES

Abstract

Regional to continental scale magnetic anomaly maps are becoming increasingly

available from airborne, shipborne, and terrestrial surveys. Satellite data are com

monly considered to fill the coverage gaps in regional compilations of these near

surface surveys. For the near-surface Antarctic magnetic anomaly map being pro

duced by the Antarctic Digital Magnetic Anomaly Project (ADMAP), we show that

near-surface magnetic anomaly estimation is greatly enhanced by the joint inver

sion of the near-surface data with the 0rsted observations relative to the Magsat

data that have order-of-magnitude greater measurement errors, albeit at much lower

orbital altitudes. CHAMP is observing the geomagnetic field with the same measure

ment accuracy as the 0rsted mission, but at the lower orbital altitudes covered by

Magsat. Hence, additional significant improvement in predicting near-surface mag

netic anomalies can result when the CHAMP data become available. Our analysis

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. also suggests that considerable new insights on the magnetic properties of the litho

sphere may be revealed by a further order-of-magnitude improvement in the accuracy

of the magnetometer measurements at minimum orbital altitude.

4.1 Introduction

Considerable efforts have been made to isolate and verify the crustal magnetic

anomaly field from Earth-orbiting satellite magnetometer measurements (e.g., Regan

et al., 1975; Langel et al., 1982; Arkani-Hamed et al., 1994; Langel, 1990; Alsdorf

et al., 1994; Cohen and Achache, 1990; Ravat et al., 1995). Crustal anomaly maps

at satellite altitudes can be interpreted only for regional geologic features. For in

sight on the smaller scale magnetic geology, satellite altitude anomalies are typically

downward continued to or near the Earth’s surface and/or the near-surface anoma

lies are upward continued to satellite altitude for comparison. Upward continuation

transforms potential field anomalies in altitude away from the sources as a stable op

eration, while downward continuation is an unstable, noise-amplifying transformation

of the anomalies towards the sources.

These continuations typically are based on either global or local representations

of the magnetic anomalies. For example, satellite altitude magnetic observations are

often modeled using spherical harmonic expansions (e.g., Arkani-Hamed and Strang

way, 1985; Arkani-Hamed et al., 1985; Whaler, 1994; Parker and Shure, 1982; Achache

et al., 1987). However, these models require global data sets that may incorporate

substantial data errors due to the uneven quality of the data measurements over

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. different regions of the Earth. Hence, the downward continuation of satellite mag

netic anomalies from spherical harmonic expansions can be limited by the substantial

enhancement of these data errors in the near-surface predictions.

Local representations such as by equivalent source models (e.g., Dampney, 1969;

Mayhew, 1982; von Frese et al., 1981b) can more fully account for the local data

qualities and errors in the magnetic observations. For the most part, however, com

parisons of downward continued satellite magnetic anomalies with near-surface sur

vey data, or upward continued near-surface survey anomalies with satellite anomalies

have yielded poorly correlated and largely inconsistent results (Schnetzler et al., 1985;

Grauch, 1993; Arkani-Hamed et al., 1995; Pilkington and Roest, 1996; Whaler, 1994;

Ravat et al., 2001).

Figure 4.1 shows an example of the inconsistencies of continuing individual anomaly

fields over great altitude differences. This example considers independently mapped

aeromagnetic data low-passed filtered for roughly 400 km and larger anomalies and

Magsat magnetic anomalies over Kursk, Russia that are associated with massive

quartz iron-ore formations (Taylor et al., 2000). Downward continuation of the

Magsat anomalies (B) over roughly 400 km yields the relatively unstable results in

Panel C that have a correlation coefficient of only 0.3 with the aeromagnetic anoma

lies (A). Upward continuation of the regional aeromagnetic anomalies (A) through

about 400 km, on the other hand, gives the more stable results in Panel D that have

a 0.6 correlation with the Magsat anomalies (B). In general, however, both continu

ations are relatively marginal in representing the amplitude and phase properties of

the observed magnetic anomalies.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. i f Sfltik 36°E 40°E 44°E 36°E 40°E 44°E ______28°E 32°E

28°E 32°E 52°N 48°N 48°N AR = (-5.5, 10.2); AM = 1.8; STD = 3.2; Cl = 1 Cl 3.2; = STD = 1.8; AM (-5.5, 10.2); = AR 44°N AR = (-5.6. 9.21: AM = 1.3: STD = 3.9: Cl = 1 Cl 3.9: = STD = 1.3: AM 9.21: (-5.6. = AR 44°N 36°E 40°E 44°E 36°E 40°E 44°E ______28°E 32°E

28°E 32°E 52°N ° n 52°N 48 N 48 N 44 44°N AR = (-826. 13861: AM = 52.1: STD = 39: Cl = 200 = Cl 39: = STD 52.1: = AM 13861: (-826. = AR AR = (-720,1061); AM = -7.2; STD = 211; Cl 200 = Cl 211; = STD -7.2; = AM (-720,1061); = AR Figure 4.1: Comparison of single-field continuations of regional (A) aeromagnetic and (B) Magsat magnetic anomalies at respective altitudesdownward of 2 continuekm andthecoordinates. Magsat400 km data centered to 2 km on andKursk, (D) upward Russia. continue Equivalentthe aeromagnetic point datadipole to inversion400 km in wasspherical used Earth to (C) Ci oo

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The mismatch in the continuations of independently observed magnetic anomaly

data sets can reflect a variety of factors including data measurement and reduction

errors. The different biases of the data sets for the disparate anomaly interference

effects also may contribute that result from the great altitude differences between

the data sets in combination with the limited precision of the measurements. The

geologic interpretations of individually continued satellite and near-surface magnetic

anomaly fields are further complicated by their apparent lack of spectral overlap over

the 400 - 900 km range of anomaly wavelengths (Grauch, 1993; Hildenbrand et al.,

1996; Whaler, 1994; Langel and Hinze, 1999).

Hence, in the absence of measurements for the inbetweeen altitudes that better

constrain the nonunique continuation estimates, there seems to be little recourse but

to consider the continuations of these anomalies in the context of anomaly models

which jointly satisfy both the satellite and near-surface measurements. This joint

inversion approach was initially implemented by Ravat et al. (1998) to demonstrate

the geologic utility of combining satellite and near-surface magnetic survey data.

In this chapter, we investigate the utility of the joint inversion of satellite and

available near-surface magnetic data for augmenting regional gaps in coverage in

near-surface anomaly compilations of terrestrial, airborne, and shipborne magnetic

surveys. The nature of the problem is shown by the near-surface magnetic anomalies

that were compiled by the Antarctic Digital Magnetic Anomaly Project (ADMAP) in

Figure 4.2 (Golynsky et al., 2001). Only satellite magnetic observations are available

at present to constrain anomaly estimates in these coverage gaps.

The available satellite magnetic data reflect disparate measurement accuracies

and mission parameters that affect the utility of the various missions for augmenting

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. near-surface magnetic survey data. The Magsat mission, for example, obtained data

over 350 - 550 km altitudes with roughly 3 - 6 nT measurement accuracies (Langel

and Hinze, 1999). However, this short 7 month mission was flown over Austral sum

mer and fall when external field activity over the Antarctic was relatively severe and

disruptive of the core and lithospheric geomagnetic components. The new multi-year

0rsted and CHAMP missions, by contrast, include minimally disturbed Austral win

ter observations with an order-of-magnitude improvement in measurement accuracy

(~ 0.3 nT). However, the observational altitudes of the new generation missions are

vastly different, with 0rsted operating over 650 - 850 km orbits while CHAMP is

providing data over 300 - 450 km elevations.

In this sections below, we develop an approach for using the satellite magnetic

data to fill-in coverage gaps in the near-surface data based on the joint inversion of

the two data sets. Specifically, by the use of simulations we study the relative utilities

of the Magsat, 0rsted, and CHAMP observations for this application. Our analysis

finds that the CHAMP data will be particularly suitable because the measurement

accuracy is an order-of-magnitude better than Magsat’s and the orbital altitudes

are much lower than 0rsted’s. We also develop and contrast magnetic anomaly

estimates for the ADMAP coverage gaps (Figure 4.2) from the 0rsted comprehensive

lithospheric magnetic anomalies of the previous chapter.

4.2 Magnetic Anomaly Inversion

Effective inversion requires an appropriate representation or forward model for

the regional magnetic anomalies. Accordingly, we consider the regional anomalies

in terms of the magnetic effects of crustal prisms using Gauss-Legendre quadrature

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. MAGNETIC ANOMALY MAP OF ANTARCTICA

Figure 4.2: The near-surface ADMAP anomalies over the Antarctic.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. integration (von Frese et al., 1981a). Specifically, the total magnetic effect (AT)

in spherical coordinates (^>,

susceptibility contrast As may be evaluated by:

nl nj ni , / 1 \ 1 A T ( 0 ,6, r) * A*, £ { A«; £(A r; £ [-u • V u' • V' - ) A s]^)^}4, (4.1) (=1 j= 1 t= l L \-tt/ )

where R is the distance or displacement between the source-point coordinates (primed)

and observation-point coordinates (unprimed), u is the unit vector in source coordi

nates (r ,Q' u is the unit vector in observation coordinates (r, 0, $); (V',V) are

the gradient operators in source and observation point coordinates, respectively; and

(Ai,Aj,A[) are the Gauss-Legendre quadrature weights (Stroud and Secrest, 1966).

In addition, A$ = [(4 - 4)/2], A^ = [(9'ja - 6'jb)/ 2], and Ar^ = [(A 4 - A4)/2],

where (4i4)> (4> ^)> and (4 ,4 ) are the lower (a) and upper ( b) boundaries of

the prism, respectively, in the Z-th coordinate of longitude ( 4i), the j-th coordinate of

co-latitude (9), and i-th radial coordinate (r).

By Equation (4.1), the magnetic anomaly due to a spherical prism is evaluated by

summing at each observation point the anomalous effects of nk x nj x ni equivalent

point dipoles (von Frese et al., 1981b; 1998), where each differential point source

anomaly is appropriately weighted by Gauss-Legendre quadrature coefficients and

the volume coordinate limits of the anomalous body being modeled. The accuracy of

the solution depends on the number of nodes or point sources within the prism used

in the integration. In particular, maximum accuracy is obtained when the distance

between the point sources (i.e., the node spacing) is smaller than the distance to the

observation point (Ku, 1977; von Frese et al., 1981a).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Now, the linear least squares inversion problem for regional magnetic anomalies

can be generalized in matrix notation by:

A X = B. (4.2)

Here, the n xm coefficients of the design matrix A for any specified distribution of

crustal prisms are completely determined in Equation 4.1 by setting As = 1, while X

is the m x 1 column matrix containing the solution of susceptibilities for the prisms,

and B is an n x 1 column matrix of the magnetic anomaly observations. Hence, the

least squares solution of the Equation 4.2 can be simply calculated by:

X = [ATA ]"1ATB. (4.3)

In practice, errors in computing the coefficients in A due to limitations of the for

ward modeling algorithm and the computer’s working precision may yield an unstable

solution X with large and erratic values, and hence large variance. In this case, the

solution can be useless for predicting anything other than the original observations

in B. To obtain a more stable and better performing solution, we commonly evaluate

the system (Equation 4.2) for the damped least squares solution given by:

X = [ATA + (EV)I]_1A TB, (4.4)

where I is the identity matrix, and the scalar EV is variously called the damping

factor, the Marquardt parameter, or the error variance (e.g., von Frese et al., 1988).

The damped least squares approach requires choosing an EV-value that is just large

enough to stabilize the solution for meaningful predictions (e.g., anomaly continua

tion, interpolation, etc.), yet still small enough to maintain an acceptable match to

the observations B. To determine an “optimal” EV-value for any application, trade-off

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. diagrams can be very effective that compare the statistical properties of the predic

tions against the errors of fit to the observations for solutions obtained from a range

of EV-values (von Frese et al., 1988; Ravat et al., 1991).

4.3 Near-Surface Magnetic Anomaly Simulations

For our analysis, we considered the ADMAP near-surface magnetic anomalies

of the Weddell Sea sector shown in Figure 4.3.A. These anomalies were low-pass

filtered in Figure 4.4.A for the 400 km and larger wavelengths that are likely to be

detected at satellite altitudes of 400 km and higher (Ravat et al., 2001). The low-pass

filtered anomalies were then related by damped least squares magnetic inversion to

the volume magnetic susceptibilities of an array of crustal prisms across the study area

using spherical coordinate Gauss-Legendre quadrature integration. For the inversion,

each prism was dimensioned 150 km on a side, and 20 km thick with the top of the

prism at 30 km below sea level. The aeromagnetic effect of each prism was evaluated

at 2 km above sea level using an nk x nj x ni = 32 x 32 x 8 equivalent point

dipole quadrature formula. Figure 4.4.B gives the magnetic anomaly predictions at

5 km altitude from the crustal prisms with magnetic effects that match the input

low-pass magnetic anomalies of Figure 4.4.A with negligible error. The locations of

the regional aeromagnetic observations at 5 km altitude, as well as the crustal prisms

used for the anomaly inversions are shown in Figure 4.3.B.

With this inversion model, we now consider the problem of estimating near-surface

anomaly values within the coverage hole or gap outlined by the white border in Figure

4.4.B at the locations marked by the red dots in Figure 4.3.B. For example, using

a conventional interpolation based on minimum curvature (Briggs, 1974; Smith and

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 4.3: A) ADMAP aeromagnetic anomalies (nT) over the Weddell Sea at 2 km above sea level. The grid interval for these anomalies is 5 km. B) The coordinates of the long wavelength ADMAP aeromagnetic anomalies over the Weddell Sea. The anomaly locations are spaced approximately 200 km in both longitudinal and latitu dinal directions. The red dots delineate a simulated coverage gap and the locations at which we seek effective near-surface magnetic anomaly predictions. The distribution of spherical crustal prisms used for the anomaly inversion is also shown. 75

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

54 W

MIN = -52.67 MAX = 122.9 AM = 4.558 ASD = 30.61 Z = 2 km

> 90 75- 90 60- 75 45- 60 30- 45 15- 30 0- 15 -15- 0 -30--15 -45 - -30 <-45 (B)

54°W

MIN = -51.07 MAX = 121.3 7Sa AM = 4.449 ASD = 30.11 Z = 5 km

> 90 75- 90 60- 75 45- 60 30- 45 15- 30 0-15 -15- 0 -30--15 -45 - -30 <-45 Figure 4.4: A) ADMAP aeromagnetic anomalies (nT) at 2 km altitude over Weddell Sea low-passed filtered for 400 km and larger wavelengths. Listed attributes for the map include the minimum (MIN) and maximum (MAX) amplitudes and amplitude mean (AM), and amplitude standard deviation (ASD). B) ADMAP aeromagnetic anomalies (nT) at 5 km. The anomalies that our simulations seek to estimate are within the white-bordered area. 76

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

54°W

MIN = -47.81 MAX: 119.3 7$ a AM = 5.059 ASD = 28.71

> 90 75- 90 60- 75 45- 60 30- 45 15- 30 0 -1 5 -15- 0 -30--15 -45 - -30 <-45 (B)

54°W 3SV MIN = -42.89 MAX = 51.2 , AM = -0.6109 & ASD = 8.713

EBB > 30 BW 24- 30 gtijBSg 18- 24 [US] 12- 18 1 1 6-12 i^ g a 0 - 6 FH -6-0 BW -12- -6 -18--12 aw -24--18 HSB -30 - -24 no <-30 Figure 4.5: A) Regional ADMAP aeromagnetic anomaly predictions from minimum curvature. B) Minimum curvature prediction errors obtained by subtracting Figure 4.5.A from Figure 4.4.B.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Wessel, 1990), we obtain the results shown in Figure 4.5.A. The interpolated values

compare relatively poorly to the ‘true’ values of Figure 4.4.B. The regional properties

of their prediction errors in the coverage gap are shown in Figure 4.5.B where Figure

4.5.A was subtracted from Figure 4.4.B. Table 4.1 also lists quantitative measures of

the fit that include the root-mean-squared (RMS) difference and correlation coefficient

(CC) between the ‘predicted’ and ‘true’ anomaly values within the gap.

Figure Figure 4.5.A Figure 4.6.A Figure 4.8.B Figure 4.11.A Constraint Minimum Curvature Magsat 0rsted CHAMP RMS (nT) 108.7 98.5 74.5 32.1 CC 0.34 0.51 0.81 0.93

Table 4.1: Performance statistics for using minimum curvature (Figure 4.5.A) and Magsat (Figure 4.6.A), 0rsted (Figure 4.8.B), and CHAMP (Figure 4.11.A) magnetic anomalies to fill a simulated gap in aeromagnetic anomaly coverage. The prediction statistics include the root-mean-square (RMS) difference in nT and the correlation coefficient (CC).

To investigate the role of satellite magnetic observations for enhancing the near

surface anomaly predictions within the coverage gap, we evaluated our crustal prism

model for simulated satellite anomalies using the disparate observation parameters of

the these missions. Figure 4.6.A, for example, gives our simulated Magsat anomalies

at 400 km altitude that we evaluated from the inversion model to the nearest 3 nT to

simulate the magnetometer measurement errors. Maximum accuracy in modeling the

satellite altitude magnetic effects was achieved by using an nk x nj x ni = 4 x 4 x 4

equivalent point dipole quadrature formula to represent each crustal prism. We then

obtained gap predictions by joint inversion of the simulated satellite anomalies with

the near-surface anomalies outside the gap located at the positions marked by the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. black dots in Figure 4.3.B. Details for the joint inversion of these magnetic anomalies

are given in the next section.

4.3.1 Joint inversion of magnetic anomalies

The term “joint inversion” in geophysics refers to the inversions of independently

observed data sets (e.g., Li and Oldenburg, 1999; Ravat et al., 1998; von Frese et al.,

1999c). For this application, the design matrix is given by

A [Aaero ■^■satellitel

B = [Baer0 B satellite] > (4-5)

where Aaero and A satellite are submatrics of respective orders ( na x m) and ( ns x m )

that reflect the geometric relationships between the crustal prism source coordinates

and the aeromagnetic observation coordinates, respectively. Additionally, the new

observation vector B = [Baero B sateiuteT includes the subvectors B aero and B sateiiite

of respective orders (na x 1) and (ns x 1) that represent the aeromagnetic and satellite

magnetic anomaly observations, respectively.

The accuracy of the anomaly estimates for the coverage gaps obtained by joint in

version from Equations 4.4 and 4.5 is controlled by the accuracy of the input anomaly

data and the choice of the error variance (EV). The input magnetic anomaly errors

largely reflect data measurement and reduction errors. For these simulations, we

evaluated all input anomalies to the nearest nT that each of the satellite magnetome

ters provides. However, trade-off diagrams must be established to find the “optimal”

EV-value for a solution X that yields effective anomaly predictions in the coverage

gap.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

54°W

MIN = -3.432 MAX = 10.7 AM = 1.195 ASD = 2.798

8-10

0-2

(B)

54°W

MIN = -50.83 MAX = 119.1 AM = 4.276 ASD = 28.8

> 90 75- 90 60- 75 45- 60 30- 45 15- 30 0 - 15 -15- 0 -30--15 -45 - -30 <-45 Figure 4.6: A) Simulated Magsat anomalies at 400 km altitude with 3 nT errors. B) Near-surface magnetic anomaly estimates at 5 km altitude for the coverage gap (white bordered area) by joint inversion of simulated near-surface anomaly data outside the gap and Magsat anomaly simulations at 400 km altitude.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A. 0.6

0.9 V icinity f .

/Hole 0.8

O 0.4 O 0.7

I tori m i iil Ka -r w i ui » i-ran ui t » -V ■ 0.6

0.2 0.5

log (EV) B.

C/> s a: - \ Hole

V icinity .

-3 -2 1 0 1 2 3 4 5 6 7

Figure 4.7: A) Magsat trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the correlation coefficient (CC) and RMS difference of the predictions within the hole (dashed blue lines referring to the left vertical axes) and the surrounding vicinity (solid green lines referring to the right vertical axes).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) 54°W 36% MIN = -38.1 MAX = 48 AM = 0.1723 ASD = 8.61

24- 30 18- 24 12- 18 6-12

1 2 - - 6

-24- -18 -30 - -24 <-30 (B)

54°W MIN = -0.9485 MAX = 3.888 /So^ AM = 0.8762 ASD = 1.03

> 4 3.5- 4 3- 3.5 2.5- 3 2- 2.5 1.5- 2 1 - 1.5 0.5- 1 0- 0.5 -0.5- 0 <-0.5 Figure 4.8: A) Gap anomaly differences obtained by subtracting the Magsat-based estimates of Figure 4.6.B from the ‘true’ anomaly values in Figure 4.4.B. B) Simulated 0rsted anomalies at 700 km altitude with 0.3 nT errors.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A.

0.8 Vicinity 0.9

\ Hole 0.8

0.4 0.7

0.2 0.6

0.5 -2

log (EV) B. 5000 200

100

•r 1000

50 cc CC 500 Hole Vicjhity

. 100

Figure 4.9: 0rsted trade-off diagrams for obtaining an “optimal” value of error vari ance (EV) in terms of the correlation coefficient (CC) and RMS difference of the predictions within the hole (dashed blue lines referring to the left vertical axes) and the surrounding vicinity (solid green lines referring to the right vertical axes).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 4.7 illustrates the EV trade-off diagram for obtaining the gap anomaly

estimates in Figure 4.6.B that were derived from the joint inversion of the simulated

near-surface anomalies outside the gap and the Magsat anomaly simulations in Figure

4.6.A. Here, the correlation coefficients (CC) and root-mean-squared (RMS) differ

ences from the predictions of solutions for various EV-values are compared. These

results are plotted for the near-surface anomalies outside and within the gap by the

solid green and dashed blue curves, respectively. In actual applications, we can only

estimate the trade-off diagram for near-surface anomalies outside the gap, but these

results very much mirror the performance of the solution within the gap as suggested

by Figure 4.7. Specifically, the predictions in Figure 4.6.B within minimal RMS dif

ference and maximum CC relative to the near-surface anomalies were obtained using

EV = 106 as indicated by the trade-off diagram in Figure 4.7.

The comparison of the gap predictions in Figure 4.6.B with the ‘true’ gap values

of Figure 4.4.B is given in Table 4.1. These results clearly favor the use of the Magsat

data over minimum curvature for estimating the gap anomalies. The nature of the

errors in the Magsat-based predictions is shown in Figure 4.8.A where Figure 4.6.B

was subtracted from the ‘true’ anomalies in Figure 4.4.B. Relative to the minimum

curvature errors (Figure 4.5.A), the Magsat-based prediction errors are sightly re

duced in amplitude, but higher frequency to reflect the improved phase properties of

the predictions.

The magnetometers on the 0rsted satellite provide observations with measure

ment errors that are reduced by roughly an order-of-magnitude relative to 3 nT mea

surement errors of the Magsat data. Hence, we evaluated our crustal inversion model

at 700 km altitude to the nearest 0.3 nT for the simulated 0rsted total field magnetic

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

54°W

MIN = -50.98 MAX = 120.8 AM = 4.885 ASD = 29.12

75- 90 60- 75 45- 60 30- 45 15- 30 0- 15

-45 - -30 <-45 (B) 54°W 3SV MIN = -39.03 MAX = 47.55 AM = -0.4361 ASD = 8.415

BBHB HMB > 30 I 24 CO o 18- 24 12- 18 i i 6'- 12 m 0- 6 m -6 - 0 H -12 - -6 -18 --12

m -24 --18 -30--24 HB| <-30 Figure 4.10: A) Near-surface magnetic anomaly estimates at 5 km altitude for a cov erage gap (white bordered area) by joint inversion of simulated near-surface anomaly data outside the gap and 0rsted anomaly simulations at 700 km altitude. B) Gap anomaly differences obtained by subtracting the 0rsted-based estimates of Figure 4.10.B from the ‘true’ anomaly values in Figure 4.4.B.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

54°W

MIN = -4.548 MAX =13.46 /fi-Q AM = 1.26 ASD = 3.474

HEHB B >12.5 essBSB 10-12.5 7.5- 10 5 - 7.5 2.5- 5 mu 0 - 2.5 Bi -2.5- 0 M i <-2.5

(B)

54 W 3g> MIN = -50.99 MAX = 120.8 7&ci AM = 4.538 ^ ASD = 29.34

> 90 75- 90 60- 75 45- 60 30- 45 15- 30 0 - 15 -15- 0 -30--15 -45 - -30 <-45 Figure 4.11: A) Simulated CHAMP anomalies at 350 km altitude with 0.3 nT er rors. B) Near-surface magnetic anomaly estimates at 5 km altitude for a coverage gap (white bordered area) by joint inversion of simulated near-surface anomaly data outside the gap and CHAMP anomaly simulations at 350 km altitude.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. anomalies in Figure 4.8.B. The simulated satellite anomalies were then combined with

the near-surface anomalies outside the gap by EV-optimized joint inversion using the

trade-off diagrams in Figure 4.9.

From the trade-off diagrams, an ‘optimal’ EV = 104 was selected for a solution

that yielded the improved gap predictions shown in Figure 4.10.A. Table 4.1 compares

these gap predictions with the ‘true’ gap values of Figure 4.4.B. The results strongly

favor the use of the 0rsted data over the Magsat data and minimum curvature for

filling in regional gaps in near-surface survey coverage. The differences between the

0rsted predictions and ‘true’ anomaly values in Figure 4.4.B are explicitly given in

Figure 4.10.B.

0rsted’s magnetometers are also being carried by the CHAMP satellite, but at

significantly lower altitudes. Accordingly, Figure 4.11.A gives the simulated CHAMP

anomalies evaluated to the nearest 0.3 nT at 350 km altitude from our crustal model.

These CHAMP anomalies were combined with near-surface anomalies outside the gap

by EV-optimized joint inversion based on the trade-off diagrams in Figure 4.12.

An “optimal” EV = 105 was chosen from the trade-off diagrams for a solution

that gives the significantly improved gap predictions in Figure 4.11.B. Table 4.1 com

pares these gap estimates with the ‘true’ gap values from Figure 4.4.B. These results

suggest that the CHAMP data will be particularly well suited for estimating near

surface anomalies because measurement accuracy is an order-of-magnitude greater

than Magsat’s and the orbital altitudes are much lower than 0rsted’s. Figure 4.13

gives additional details on the differences between the CHAMP predictions and the

‘true’ anomaly values in Figure 4.4.B.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A.

0.95

H ole 0.9

0.8 V icinjty 0.94 w'

0.6

0.93 0.5

0.4

0.92 -2

log(EV) B. 200

150

26 c

H ole

50 - Vicinity. >

20 -2

Figure 4.12: CHAMP trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the correlation coefficient (CC) and RMS difference of the predictions within the hole (dashed blue lines referring to the left vertical axes) and the surrounding vicinity (solid green lines referring to the right vertical axes).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 54°W 3 e v MIN = -24.25 MAX = 28.01 / < S > o AM = -0.0891 & ASD = 4.989

> 30 24- 30 18- 24 12- 18 6 - 12

0 - 6

- 6-0 -12- -6 -18--12 -24 - -18 -30 - -24 <-30

Figure 4.13: Gap anomaly differences obtained by subtracting the CHAMP-based estimates of Figure 4.11.B from the ‘true’ anomaly values in Figure 4.4.B.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4.4 ADMAP Coverage Gap Predictions

To date, we have processed only the Magsat and 0rsted mission data fully for

magnetic anomalies of the Antarctic lithosphere. For filling in coverage gaps in the

ADMAP compilation of near-surface magnetic surveys shown in Figure 4.2, the above

simulations clearly favor the use of 0rsted data over the Magsat observations. Hence,

in the present section, we develop near-surface anomaly estimates for the ADMAP

coverage gaps from the joint inversion of the available near-surface anomalies and the

comprehensive 0rsted lithospheric magnetic anomalies shown in Figure 4.14.

For the joint inversion, we considered the ADMAP anomalies low-pass filtered for

400 km and larger wavelengths shown in Figure 4.15 that are likely to be detected at

satellite altitudes of 400 km and higher. The regional ADMAP anomalies were then

resampled at roughly a 200 km interval at the coordinates given in Figure 4.16. The

resampling greatly reduced the number of ADMAP data that needed to be considered

in the analysis with essentially no loss of regional anomaly detail.

Large areas are numerically labeled in Figure 4.16 where magnetic surveys are

lacking. These coverage gaps are located in on- and off-shore Marie Byrd Land (#1)

and off-shore Thurston Island (#3) in West Antarctica, and east of the Shackleton

Range (#2), Aurora Subglacial Basin (#4) and vicinity of Wilkes Land (#5 and

#6) in East Antarctica. The central void south of 83 °S was not considered in our

analysis because it lacks satellite coverage due to the 83° inclination of the 0rsted

mission orbits.

Each gap was modeled separately for a set of anomaly predictions. In each case,

the inversion model consisted of 20 km thick spherical prisms with sides 150 km and

MIN = -4.919 MAX = 6.243 9 eg 9 o o 00 O O) O)

n ^ w in I I A Mo06 M C O C O 90°E M C O C I I 91 o I o I O C M C I I O C M C I I ■o I ■O' I

Figure 4.14: 0rsted comprehensive lithospheric magnetic anomalies at 700 km from Chapter 3 (Figure 3.7.A).

0-50 <-200 > > 300 5 0 - 100 - 5 0 - 0 1 50- 200 1 00- 150 2 0 0 - 250 2 5 0 - 300 -1 0 0 - -50 -1 5 0 --1 0 0 -200 --1 5 0 MAX = 375.5 MAX MIN = -200.9MIN AM = 63.85 AM ASD = 62.76 180°W Figure 4.15: ADMAP magnetic anomalies at 5 km altitude low-passed filtered for 400 km and longer wavelengths. to co

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. tops at 30 km below sea level over the study area. The effects of these prisms for

each inversion were modeled by Gauss-Legendre quadrature integration.

Figure 4.17 gives the “optimal” EV-values that were selected for developing the

best anomaly predictions in each coverage gap. In each case, the “optimal” EV-

value maximized the correlation coefficient between the inversion predictions and the

observed anomaly values around the coverage gaps. Figure 4.18 gives the regional

ADMAP magnetic anomalies where the coverage gaps were filled by joint inversion

using the 0rsted lithospheric anomaly data (Figure 4.14). For comparison, Figure

4.19 shows the gridded regional ADMAP anomalies with the coverage gaps filled in

by minimum curvature without regard to the 0rsted data. The differences between

the two sets of predictions in the gaps are given in Figure 4.20.

The most prominent differences between the predictions are observed for gap #6

over Wilkes Land in East Antarctica. The geological implications of the differences

are difficult to assess because the region is covered by an ice sheet up to roughly

3 km thick (e.g., von Frese et al., 1999c). Radar sounding data suggest that the

Wilkes Subglacial Basin and its salients may constitute a major intracratonic zone

of sedimentation where the edge of the basin probably marks the limit of the oro-

genic activity responsible for the Transantarctic Mountains (Steed and Drewry, 1982).

These results also identify a probable major fault block running along longitude 135°E

that correlates with a magnetic positive which is relatively subdued and broken up

towards the coastline in the 0rsted-based predictions of Figure 4.18. However, the

positive anomaly in the 0rsted-based predictions tends to resolve relatively well the

subglacial plateau at Dome C (75°S, 127°E). Originally discovered from seismic re

fraction and gravity data, the plateau is composed of crystalline bedrock covered by

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A 180°W Figure 4.16: Distributiontudinal andof regionalinversionlatitudinal ADMAPof the 0rsteddirections. anomalies ofand Figure Numbersavailable 4.15 regionalresampledmark ADMAP theapproximately data. regional coverage 200 kmgaps in whereboth estimateslongi were developed by joint 4^ CO

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. — h o le l — hole 2 — hole 3 — hole 4 — hole 5 — hole 6

0.5

O O

•20 2 4 6 8 10 12 log (EV)

Figure 4.17: Error variance (EV) spectra for the ADMAP coverage gaps or holes. For each hole, a cross marks the ‘optimal’ EV-value for developing the best anomaly predictions from the joint inversion of the 0rsted and regional ADMAP anomalies

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. little or no sedimentary rock (Bentley et al., 1983). Modeling of the ground-based

magnetic measurements also showed that the plateau is characterized by strong posi

tive magnetization with a Koenigsberger ratio greater than unity and hence minimal

apparent remanence (Bentley et al., 1983).

Other gaps where the two sets of predictions are conspicuously different include

gap #2 east of the Shackleton Range and gap #1 off of the Marie Byrd Land coast.

For gap #2, the minimum curvature predictions suggest a deep closed minimum rel

ative to the smaller amplitude minimum that trends SW/NE through the gap in the

Orsted-based predictions. The minimum curvature predictions reflect the straightfor

ward extrapolation of boundary observations into the gap, whereas the Orsted-based

predictions tend to honor the regional SW trend of minima outside the gap that ex

tends to a slightly positive magnetic anomaly over the high-grade metamorphic rocks

of the Shackleton Range (Hunter et al., 1996; Kleinschmidt and Buggisch, 1994). Ad

ditional geological implications for these predictions are difficult to develop because

of the gap’s ubiquitous cover of snow and ice.

Similarly, the geological implications for the predictions in gap #1 are difficult

to ascertain because the gap is covered by sea water. Here, however, the minimum

curvature predictions appear to be more strongly biased to the minima along the

western boundary of gap #1 than are the Orsted-based predictions.

By our Weddell Sea simulations, Figure 4.18, which includes the Orsted-based

gap predictions, is the best representation currently available for the 400 km and

larger ADMAP components. Superposing the higher frequency components with

wavelengths shorter than 400 km on Figure 4.18 yields our best current estimate of

the ADMAP anomalies shown in Figure 4.21. Note however, that when the lower

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 50 -50 150 300 250 200 100 .7 -100 -150 - > > 300 0 - < -2 0 0 5 0 - - 5 0 - 0 1 5 0 - 1 0 0 - 2 5 0 - 2 0 0 - - 1 0 0 - 63.23 - 1 5 0 - -200 = 338.2 = -195= = = 55.99 MIN = MIN MAX AM = AM ASD B u r n s M 1 1 1 M liRSrl IM B. m B B B 180°W 5 Figure 4.18: Regional ADMAP magnetic anomaly grid with coverage gaps filled in by joint inversion using 0rsted lithospheric anomalies at 700 km altitude. CO

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

0-50 > > 300 < -2 0 0 5 0 - 100 - 5 0 - 0 100- 150 150- 200 2 5 0 - 300 2 0 0 - 250 -1 0 0 - -50 -2 0 0 - -150 -1 5 0 --1 0 0 MIN = -200.9MIN = 375.5 MAX AM = 63.85 AM ASD = 62.76 ISSI 180 W Figure 4.19: Regional ADMAP magnetic anomaly grid with coverage gaps filled in by minimum curvature. co oo

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. altitude lithospheric anomalies from CHAMP become available, further significant

improvements in the gap predictions may be possible according to our simulations.

The gap predictions are clearly not unique because they are based on highly simpli

fied crustal models and imperfectly distributed and measured anomaly observations.

Hence, our predictions must be used with caution for geological interpretation because

they can only be as good as the data and assumptions used in deriving them.

4.5 Summary and Conclusions

The simulations show the important role that satellite magnetic observations can

play in estimating magnetic anomalies in the near-surface altitude field. The joint in

version of near-surface and satellite data yields near-surface anomaly predictions that

are far superior to estimates based on the error prone continuations of the individual

sets of anomaly observations.

Of the Magsat and 0rsted observations that represent the satellite data currently

available for supplementing coverage gaps in the ADMAP compilation, our results

clearly favor the use of the higher altitude Orsted data because of their greatly im

proved measurement accuracy. Hence, our best current estimate of the near-surface

magnetic anomaly field for the Antarctic is given in Figure 4.21. This estimate was

obtained by the joint inversion of the Orsted lithospheric anomalies (Figure 4.14) and

regional ADMAP components (Figure 4.15) upon which we superposed the shorter

wavelength ADMAP components.

However, further significant improvements in these near-surface estimates are

likely to result when the lower altitude CHAMP data become available. By the

results in Table 4.1, for example, gap predictions using CHAMP data can have noise

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

- > > 100 0 -2 0 < < -80 20 6 0 - 80 2 0 - 40 4 0 - 60 8 0 - 100 - - 4 0 - -20 - 6 0 - -40 - 8 0 - -60 -91 = 256.2 = -2.159 21.98 = 0 ° 180°W Figure 4.20: Differences in the gap anomaly predictions obtained by subtracting Figure 4.18 from Figure 4.19.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 4.21: predictions Antarctic of Figure magnetic 4.18 and anomaly the high-pass map filtered at (< 5 400 kmkm) altitudeADMAP anomalies. that includes the superposition of the 0rsted-based

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. levels reduced by 72% relative to Magsat-based predictions simply by virtue of the

order-of-magnitude increase in measurement accuracy reflected by the CHAMP data.

Indeed, our analysis suggests that increasing the measurement accuracy in the mag

netic observations a further order-of-magnitude (i.e., 0.03 nT) could reduce noise

levels by nearly 99% relative to the Magsat-based predictions.

Of course these results are limited in practice by the errors in reducing mag

netic observations for their lithospheric components. These reduction errors can be

especially severe in the polar regions where strong and highly dynamic external mag

netic fields operate. However, improving measurement accuracy can greatly facili

tate the reduction of magnetic observations for non-lithospheric effects because of

the enhanced correlation that can result between near-surface and satellite magnetic

anomalies of the lithosphere.

102

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 5

CRUSTAL ANALYSIS OF MAUD RISE FROM COMBINED SATELLITE AND NEAR-SURFACE MAGNETIC SURVEY DATA

Abstract

We produced a crustal magnetization model for the Maud Rise in the southwest

Indian Ocean off the coast of East Antarctica using magnetic observations from the

0rsted satellite and near-surface surveys complied by the Antarctic Digital Magnetic

Anomaly Project (ADMAP). Joint inversion modeling of the two anomaly fields sug

gests that the magnetic effects due to crustal thickness variations and remanence

involving the normal polarity Cretaceous Quiet Zone (I

altitude 700 km). The crustal thickness effects were modeled in the 0rsted data

using crustal thickness variations derived from satellite altitude gravity data. Model

ing of the residual 0rsted and near-surface magnetic anomalies supports extending the

KQZ eastwards to the Astrid Ridge. The remaining near-surface anomalies involve

crustal features with relatively high frequency effects that are strongly attenuated at

satellite altitudes. The crustal modeling can be extended by the satellite magnetic

anomalies across the Indian Ocean Ridge for insight on the crustal properties of the

103

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. conjugate Agulhas Plateau. The modeling supports the Jurrasic reconstruction of

Gondwana when the African Limpopo-Zambezi and East Antarctic Princess Astrid

coasts were connected as part of a relatively demagnetized crustal block.

5.1 Introduction

Continents are compiled of crustal blocks with different ages, compositions, tec

tonic histories, and contrasting magnetic properties dominated mostly by induction

(Hinze and Zietz, 1985) with effects that can be detected at satellite altitude (e.g.,

Ravat et ah, 1992; Taylor and Frawley, 1987; von Frese et ah, 1986). Oceanic crust,

on the other hand, is compositionally more homogeneous, but predominantly mag

netized by the remanent effects of seafloor spreading. For the most part, the al

ternating stripped seafloor spreading anomalies are narrowly-formed so that their

effects are generally canceled and strongly attenuated at satellite altitude (Toft and

Arkani-Hamed, 1993; LeBrecque and Raymond, 1985; Hinze et ah, 1991).

There are exceptions, however, such as the magnetic anomalies from the seafloor

created during the Cretaceous in a long 35 Ma span of normal geomagnetic polarity.

Cretaceous Quiet Zone (KQZ) anomalies are typically visible at the satellite altitude

so that their natural remanent magnetization effects can be resolved (LaBrecque and

Raymond, 1985; Thomas, 1987; Arkani-Hamed, 1988; Harrison et ah, 1986; Hayling,

1991; Toft and Arkani-Hamed, 1992; Fullerton et ah, 1994; Dyment and Arkani-

Hamed, 1998).

Magsat satellite data have also been revealed over oceanic plateaux, rises and

subduction zones that are generally interpreted for induced magnetization effects

(Johnson, 1985; Bradley and Frey, 1988; Frey, 1985; Toft and Arkani-Hamed, 1992;

104

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Fullerton et al., 1989). These anomalies commonly are positively correlated to bathy

metric features with anomaly maxima over plateaux and rises, and minima over the

basins (Frey, 1982; Hinze et al., 1991).

The most prominent satellite anomaly of the Antarctic is the NE trending max

imum over the Maud Rise (Figure 2.10.A). As shown in Figure 5.1, Maud Rise lies

off the southwest Indian Ocean coast of East Antarctica between the Weddell Sea

Embayment (WSE) and the Astrid Ridge (AR). It is related to the tectonic evolution

of the Southern Atlantic Ocean (Schandl et al., 1990). Maud Rise and the Agulhas

Plateau probably separated during a ridge jump at 93 Ma (Martine and Hartnady,

1986; Fullerton et al., 1994). These features form part of the Cretaceous Quiet Zone

that extends from southern Africa to the northeastern Weddell Sea Embayment across

the southwest Indian Ocean (Marks and Tikku, 2001; Fullerton et al., 1994; Purucker

et al., 1998; 99). Magsat modeling of this KQZ has focused mostly on the remanent

properties with relatively minor consideration of the inductive components due to

crustal structural and compositional variations (e.g., Fullerton et al., 1994).

In the Magsat data, for example, the inductive magnetic difference between oceans

and continents due to their crustal thickness variations has been difficult to resolve

in both regional (Harrison et al., 1986; Bradley and Frey, 1988, 91; Hinze et al.,

1991) and global (Cain et al., 1984; Arkani-Hamed and Strangway, 1985) anomaly

maps. However, as suggested in Chapter 3, this effect, which should be evident in

the vicinity of Maud Rise, probably was erroneously incorporated in the core field

estimates that were removed in the production of the anomalies (e.g., Meyer et al.,

1983; Counil et al., 1991; Hayling, 1991).

105

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 5.1: Stereographically projected bathymetry of the southwest Indian Ocean from the NOAA/NGDC 5 arc minute digital terrain model. The thin white bathy metric contours are at 1000 m intervals. The thick black border delineates the study area. Annotated features include AG (Agulhas Plateau); AP (Antarctic Peninsula); AR (Astrid Ridge); CL (Coats Land); CR (Conrad Rise); DML (Dronning Maud Land); EE (Explora Escarpment); EL (Enderby Land); GR (Gunnerus Ridge); KS (Kainanmaru Seamount); MAR (Madagascar Ridge); MB (Mozambique Basin); MOZ (Mozambique Ridge); MP (Mozambique Plateau); MR (Maud Rise); RLS (Riiser- Larsen Sea); SF (Sveshjfella); SOI (South Oakney Islands); SWIOR (Southwest In dian Ocean Ridge); and WSE (Weddell Sea Embayment).

106

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hence, in this study, we use our 0rsted lithospheric anomalies (Figure 5.2.A) in

combination with the ADMAP near-surface anomalies (Figure 5.2.B) to develop a

comprehensive crustal model of the induced and remanent magnetization properties

for the Maud Rise. We also investigate the role of the satellite magnetic anomalies to

extrapolate our results for the crustal properties of the conjugate Agulhas Plateau,

as well as the testing of the Jurassic fit of the South African coast to East Antarctica.

5.2 Magnetic Modeling of the Crust

Figures 5.2.A and 5.2.B give the degree 13+ 0rsted and regional near-surface

total magnetic field anomalies that we used for modeling the crustal magnetizations

of Maud Rise area. The low correlation (CC = 0.1) between the two maps indicates

the great disparity in source effects that the large altitude variations introduce in our

application. Of course, data measurement and reduction errors also can contribute

to lower the correlation of the maps, but we presume these errors may be neglected

in our efforts to produce a crustal magnetization model for the two anomaly fields in

Figure 5.2.

As a modeling strategy, we focused on first modeling the regional anomaly com

ponents. We then subtracted the modeled effects from the data for the next round of

modeling. This process was continued until an acceptable crustal model was obtained.

The crustal magnetizations were obtained by joint inversion so that their effects si

multaneously matched the 0rsted and regional near-surface ADMAP anomalies.

Using this strategy, we first accounted for the inductive anomaly effects due to

thickness variations of the crust. We then modeled the residual anomalies for the

107

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -1.762 MAX = 1.944 AM = 0.0 ASD = 0.7014

B8 -1.5 - -1.2 SB < -1-5

(B)

MIN = -107.1 MAX = 127.5 AM = 0.0 ASD = 40.81

> 60 45 - 60 30 - 45 □ 15 - 30 0-15 -15 - 0 -30 --1 5 -45 --3 0 <-45 Figure 5.2: Magnetic anomalies in nT over the study area from A) 0rsted (Figure 2.10.A) and B) near-surface ADMAP observations. The ADMAP anomalies were low-pass filtered for 500 km and larger wavelengths.

108

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. remanent effects of the KQZ, and in subsequent residuals the effects for other inductive

and remanent crustal magnetic features.

5.2.1 0rsted anomaly modeling

The 0rsted anomalies (Figure 5.2.A) appear to reflect mostly the superposed

effects of the continent-ocean crustal edge and the crustal remanence of the KQZ.

To model the edge effect anomalies, we used the crustal thickness model in Figure

5.3.A that von Frese et al. (1999c) obtained from the spectral correlation analysis of

free-air and computed terrain gravity effects at satellite altitude. For the inversions,

we represented the crustal thickness variations by the distribution of crustal prisms

shown in Figure 5.3.B.

These crustal prisms, each 150 km on a side, were modeled for their magnetic

effects in spherical Earth coordinates by Gauss-Legendre quadrature integration (von

Frese et al., 1981a). Our crustal thickness modeling assumed the mantle is relatively

non-magnetic (Wasilewski et al., 1979; Wasilewski and Mayhew, 1992) and the Curie

isotherm is everywhere deeper than the Moho.

For the modeling, we broadly differentiated the magnetic properties between the

oceanic and continental regions. The continental crust of the study region includes

granitic Archean basement of the Grunehogna Province (Groenewald et al., 1995).

Hence, for modeling the crustal prisms of the continent, we used an average suscep

tibility of 0.01 SI that is consistent with the broad range of susceptibilities measured

for continental granite (e.g., Clark and Emerson, 1991).

For the oceanic crust, we adopted an average susceptibility of 0.03 SI (e.g.,

Thomas, 1987) that is also consistent with the induced magnetic chractersitics of

109

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = 2.194 MAX = 39.97 AM = 16.18 ASD = 12.55

>35 3 2-3 5 29-3 2 ma&> 26-2 9 2 3-2 6 2 0-2 3 17-20 14-17 11 - 14 8-11

(B)

Figure 5.3: A) Crustal thickness data from von Frese et al. (1999) used by our inversions with the study area outlined. B) Distribution of spherical crustal prisms used for the anomaly inversions. Blue-colored oceanic prisms were modeled with a 0.03 SI susceptibility, while the red-colored continental prisms were modeled with a 0.01 SI susceptibility.

110

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. oceanic layer 2 (e.g., Roeser et al., 1996). This value compares well with the 0.033 -

0.038 SI range of susceptibilities inferred by Fullerton et al. (1994) for the induced

and viscous remanent magnetizations of Maud Rise based on geochemical data from

alkali basalt at Site 690 of the Ocean Drilling Program (Schandl et al., 1990).

Figure 5.4.A gives the total magnetic field anomalies estimated from this model

at 700 km altitude. The correlation coefficient is 0.4 between the predictions and the

Orsted lithospheric anomalies in Figure 5.2.A. This result supports the notion that

substantial crustal thickness effects may be contained in the degree 13+ components

of the Orsted magnetic data. The modeling tends to account for significant portions of

the positive anomalies over the eastern Grunnus Ridge (GR) and Explora Escarpment

(EE). The elongate SW-trending minimum in the modeled anomalies also reflects

well the affinity of the South Orkney Islands (SOI) crustal block with the Antarctic

Peninsula (Harrington et al., 1972; G arrett, 1991) off the western margin of the study

area. Furthermore, the modeling tends to account for the magnetic anomaly minimum

over Sveshjfella (SF) in western Dronning Maud Land (DML).

However, the Orsted anomalies reflect other crustal magnetization effects than

just the effects of the crustal thickness variations. These other crustal effects may

be brought out by subtracting the crustal thickness magnetic effects (Figure 5.4.A)

from the Orsted anomalies (Figure 5.2.A) for the residual anomalies shown in Figure

5.4.B.

The residual anomalies include a prominent maximum over Maud Rise and the

KQZ that was created during a 35 Ma interval of normal geomagnetic polarity in the

Cretaceous (LaBrecque and Raymond, 1985; Thomas, 1987; Arkani-Hamed, 1988;

Harrison et al., 1986; Hayling, 1991; Toft and Arkani-Hamed, 1992; Fullerton et a l,

111

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -1.81 MAX = 2.2 AM = 0.0 ASD = 0.7757

Z = 700 km

BB > 1.5

■KEBSi 1.2 - 1.5 m 0.9 - 1.2 0.6 - 0.9 □ 0.3 - 0.6 mi 0.0 - 0.3 tas -0.3 - 0.0 mstPBaaW -0.6 - -0.3 BBSrag -0.9 - -0.6 r a -1.2 - -0.9 m -1.5 - -1.2 m < -1.5

(B)

MIN = -1.92 MAX = 1.75 AM = 0.00 ASD = 0.8007

Z = 700 km

^ HMh > 1.5 1.2 - 1.5 m 0.9 - 1.2 H5H 0.6 - 0.9 □ 0.3 - 0.6 M i 0.0 - 0.3 n -0.3 - 0.0 m -0.6 - -0.3 u s -0.9 - -0.6 B9 -1.2 - -0.9 » -1.5 - -1.2 SB < -1.5

Figure 5.4: A) Predicted scalar total field magnetic effects of crustal thickness varia tions at 700 km altitude. B) Residual anomalies obtained by subtracting the magnetic crustal thickness effects (Figure 5.4.A) from the observed 0rsted anomalies (Figure 5.2.A). 112

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -80 MAX = -63

(B)

MIN = -11 MAX = 42

i m °\N 0° m °c AU =deg

> 40 35 - 40 30 - 35 25 - 30 20 - 25 15 - 20 10 - 15 5-10 0 -5 -5-0 < -5

Figure 5.5: Paleopolarization (A) inclinations and (B) declinations in degrees used in modeling magnetization contrasts in the oceans. Note that in the blank areas off coastlines, we used the core field attitudes (Figure 5.13) because of the lack of paleoattitude data. ^

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1994; Dyment and Arkani-Hamed, 1998). Magnetic anomaly minima over the oceanic

southern flank of the Maud Rise maximum may reflect relatively demagnetized crust

of sediment filled basins. In the Riiser-Larsen Sea (RLS), for example, sediment thick

nesses up to 5 km have been reported (Hinz and Krause, 1982; Ishihara et al., 1999;

Leitchenkov et al., 1996). Hydrothermal alternation of the oceanic crust beneath the

thermally insulating cover of sediments also may have reduced crustal magnetizations

(Levi and Riddihough, 1986).

To model the remanent effects of the KQZ, the polarization of the crust at the

time of its formation must be considered that is quite different from its present day

polarization by the core field. Accordingly, for our analysis we incorporated the pa-

leopolarization inclinations and declinations from Dyment and Arkani-Hamed (1998)

for the KQZ between the 83 and 118 Ma isochrons (Harland et al., 1989) that were

inferred from the age map of the oceanic crust (Mueller et al., 1993). Figures 5.5.A

and 5.5.B give these respective remanent inclinations and declinations that we used

for this study.

Using the remanent polarization attitudes in Figure 5.5 for the oceanic crustal

prisms, the induced polarization attitudes of the 0rsted99c (Olsen et al., 2000) core

field model updated to 1999.0 for the continental prisms, and a constant field intensity

of 40,200 nT, we obtained the magnetization contrast model in Figure 5.6.A from the

residual Orsted anomalies (Figure 5.4.B) by least squares inversion. Within the KQZ,

the maximum remanent magnetization contrasts is 2.1 A/m, while for the Riiser-

Larsen Sea (RLS) negative magnetization contrasts down to -1.7 A/m are inferred.

Combining the effects of the crustal thickness variations (Figure 5.4.A) with the

effects of these induced and remanent magnetization contrasts yields the anomalies

114

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MAX = 2.111 AM = 0.09777 ASD = 0.6363

I I °-5~ 1 0- 0.5

-0.5 - 0

(B) MIN = -1.86 MAX = 1.80 AM = 0.00 ASD = 0.72 Z = 700 km

HB > 1.5 BBS 1.2 - 1.5 H i 0.9 - 1.2 m 0.6 - 0.9 □ 0.3 - 0.6 H i 0.0 - 0.3 B -0.3 - 0.0 m -0.6 - -0.3 ^3 -0.9 - -0.6 BBEBBBS -1.2 - -0.9 HI -1.5 - -1.2 H i < -1.5

Figure 5.6: A) Magnetization contrast model for the residual 0rsted anomalies of Figure 5.4.B contoured at 0.5 A/m intervals. The black dashed line delineates the KQZ boundary. B) Scalar total magnetic anomalies in nT modeled by the superposi tion of the crustal thickness effects and the effects of the magnetization contrasts in Figure 5.6.A at 700 km altitude.

115

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. predicted at 700 km altitude in Figure 5.6.B. The model predictions match the 0rsted

anomalies (Figure 5.2.A) with a correlation coefficient 0.93 and the differences shown

in Figure 5.7.A. These results suggest that the 0rsted magnetic anomalies are domi

nated by induced magnetization effects related to crustal thickness variations and the

altered crust of the oceanic basins, as well as the regional remanent magnetizations

of the KQZ.

5.2.2 Modeling the near-surface magnetic anomalies

In this section, we update the 0rsted magnetization model responsible for the

anomaly estimates of Figure 5.6.B for magnetization contrasts with effects that satisfy

the regional near-surface magnetic anomalies of Figure 5.2.B and the minor 0rsted

residuals in Figure 5.7.A. For the analysis, we consider only the regional near-surface

ADMAP anomaly data low-pass filtered for 500 km and longer wavelengths. Our

testing and those of others (e.g., Ravat et al., 2001; Pilkington and Hildebrand, 2000)

indicates that the shorter wavelength near-surface anomalies can only be marginally

represented at satellite altitudes.

Because of the disparities between the two observed anomaly fields in Figure

5.2, we have little recourse but to consider them as the boundary values for our

magnetization modeling. To develop crustal magnetizations that jointly satisfy both

these boundary conditions, we consider the near-surface anomaly predictions in Figure

5.7.B from the regional magnetization model that accounts for the modeled 0rsted

anomalies in Figure 5.6.B. Subtracting the predictions in Figure 5.7.B from the near

surface anomalies in Figure 5.2.B yields the residual effects in Figure 5.8.A. By the

joint inversion of these near-surface residual effects together with the 0rsted residuals

116

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -0.53 MAX = 0.82 AM = 0.0 ASD = 0.18

Qg -1.5 - -1.2

BH < - 1.5

(B) MIN = -118.1 MAX = 142.9 AM = 0.0

Figure 5.7: A) Unmodeled 0rsted anomalies in nT obtained by subtracting Figure 5.6.B from Figure 5.2.A. B) The scalar magnetic anomaly predictions at 5 km altitude from the induced and the remanent magnetizations of the 0rsted anomalies modeled in Figure 5.6.B.

117

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -188.3 MAX = 218.4 AM = 0.0 ASD = 50.13

> 150 120 - 150 90 - 120 60 - 90 30 - 60 0-30 -30 - 0 -60 - -30 -90 - -60 -120 - -90 <-120

(B) MIN = -1.534 •m°MM 0° m°c MAX = 2.01 AM = -0.02042 ASD = 0.4772

> 1.5 1.2- 1.5 1 0 CO 1.2 0.6- 0.9 CO o I 0.6 0 - 0.3 -0.3- 0 1 1 0 b> -0.3 i t o <0 -0.6 -1 .2 - -0.9

< - 1.2 Figure 5.8: A) Residual near-surface magnetic anomaly differences obtained by sub tracting the anomaly predictions in Figure 5.7.B from Figure 5.2.B. B) Magnetization contrasts contoured at 0.3 A/m intervals as obtained from the joint inversion of Figure 5.7.A and 5.8.A. The black dashed line delineates the KQZ boundary. 118

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. in Figure 5.7.A, we updated the regional 0rsted magnetization model with effects

given by Figure 5.6.B for the magnetization contrasts shown in Figure 5.8.B.

In obtaining the magnetization contrasts of Figure 5.8.B, we assumed the oceanic

crustal prisms were remanently magnetized according to the paleoinclinations and

paleodeclinations given in Figure 5.5 from Dyment and Arkani-Hamed (1998). We

also assumed a paleointensity of 40,200 nT for the oceanic prisms that is the mean core

field intensity over the study region. For the continental crustal prisms, inductively

magnetized effects were assumed according to the polarization characteristics of the

0rsted99 core field model updated to 1999.0, but with the mean intensity for the

study area.

By joint inversion, we obtained susceptibility contrasts according to the damped

least squares solution given in Equation 4.5. For the inversion, we used the trade-off

diagram in Figure 5.9 to establish an ‘optimal’ error variance (EV) in the sense that

the solution (1) modeled both sets of observed anomalies with negligible errors and

(2) yielded geologically reasonable magnetization variations for the continental and

the oceanic crustal prisms. The first condition was studied by plotting the change

in the deviation of the correlation coefficient (CC) from 1 (i.e., 1 - CC) between

the observed data sets and the model predictions for various EV-values as shown by

the black curves in Figure 5.9. The second condition was established by plotting

the change in the standard deviations for the continental and oceanic crustal prisms

susceptibility contrasts (As) for various EV-values as shown in by the red curves in

Figure 5.9.

From the trade-off diagram, we chose EV = 104 to minimize the deviation between

observed and predicted anomalies and constrain the magnetization contrasts to range

119

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 0.25

0.8 0.2 ocean (As}

0 0.6 Satellite Anomalies 0.15 0

0.4

0.2 0.05

Near-Surfacse Anomalies

loa (EV)

Figure 5.9: Trade-off diagrams for obtaining an “optimal” value of error variance (EV) in terms of the compliment to the correlation coefficient (1 - CC) and the standard deviations (SD) of the solution susceptibility contrasts (As) in SI. The curves are color coded to the vertical axes of the plot.

120

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. over only a few A/m or less as suggested by studies of the magnetic rocks of the oceans

and continents (e.g., Hinze et al., 1991). The magnetization contrasts obtained by

this optimal EV predict magnetic effects that correlate at 0.94 and 0.95 with the

residual 0rsted (Figure 5.7.A) and near-surface ADMAP (Figure 5.8.B) data. These

magnetization contrasts range between -1.5 to 2.1 A/m with a standard deviation of

0.47 A/m in good agreement with the crustal magnetic properties inferred by other

magnetic investigations of the study region (Fullerton et al., 1994; Ghidella et al.,

1991; Purucker et al., 1999).

5.3 Integrated Magnetization Contrasts

Magnetization contrasts integrate by superposition into more comprehensive mod

els just like the components of an anomaly may be summed for the complete anomaly.

A 2-D crustal anomaly simulation illustrating the principle is given in Figure 5.10 that

was developed using GM-SYS code (Northwest Geophysical Associates, 2000). The

top panel shows the magnetic effects at 1 km altitude (right) for a 4-body crustal

model (left) subjected to polarization inclination and declination of 90° and 0°, re

spectively. The magnetizations listed for the crustal bodies in Figure 5.11 correspond

to a polarization intensity of 56,000 nT.

The middle panel of Figure 5.10 gives the more regional effects (right) that corre

spond to the 2 regional crustal sources (left). Subtracting these regional effects from

the total effects in the top panel reveals the residual shorter wavelength anomaly

effects in the bottom panel (right) that correspond to the 2 smaller crustal magnetic

sources (left). Clearly, the anomaly effects and corresponding crustal magnetizations

121

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. poue ih emiso o h cprgt we. ute erdcin rhbtd ihu pr ission. perm without prohibited reproduction Further owner. copyright the of ission perm with eproduced R Figure 5.10: Superposition of crustal magnetizations (left panels) as well as their their as well as panels). (right panels) (left altitude km 1 at magnetizations anomalies crustal of magnetic corresponding Superposition 5.10: Figure

depth (km) depth (km) depth (km) 12 12 9 6 9 6 3 0 0 3 10 7 0 -70 •140 -140 -140 ■ T"" " T i Suc j1 Source j i :Am,=9.5A/m ...... Ann,=2.8 A/m Am,= 6.7 A/m f I 1 1 |KgSS!>K| mmmm 1 . 1| Source 1 Source Source ;1Source ------aeet1 ; 1 Basement -70 7 0 -70 ; Am, A/m-= 0,0 aeet1 ; 1 Basement ...... km km km ; ...... ---- ' Basement 2 Basement ' m .Am ’ Am,=0.OA/m Anv=4.5A/m Any) 4. m, — 4 ,^ /m A .5 .4 )= y n .A aeet ; ■ ; Basement2 0 140 70 Am-5.6 ,=A/m 0 140 70 m=45/ I Am,=4.5A/m Am?= -1.1A/m; ...... ; . : r : f - t Source 2 Source ; Source 2 ;Source ...... Source 2 Source 4 -140 140 r : 122 -140 -140 -70 -70 -70 km km km 0 0 0 70 70 70 140 140 140 -200 200 400 200 -200 -200 400 400 200 in the bottom two panels integrate to account completely for the anomaly and mag

netizations in the top panel.

In Figures 5.11.A and 5.11.B, we synthesized the remanent and induced intensities

of magnetization, respectively. The remanent intensities (Figure 5.11.A) represent

the superposition of the remanent intensities from Figure 5.6.A and the remanent

components from Figure 5.8.B. Polarizing these magnetizations with the remanent

inclinations and declinations in Figure 5.5 yields the remanent total field anomaly

components at 700 km and 5 km altitudes shown in Figures 5.12.A and 5.12.B, re

spectively.

Similar^, the induced intensities of magnetization (Figure 5.11.B) were obtained

by the superposition of the induced crustal thickness magnetizations and the induced

components of Figure 5.8.B. Polarizing these magnetizations with the induction in

clinations and declinations from the 0rsted99c core field model in Figure 5.13 yields

the total field anomalies at 0rsted and ADMAP altitudes given in Figures 5.14.A

and 5.14.B, respectively.

Our model Orsted and regional near-surface ADMAP anomaly effects are given in

Figure 5.15, where we superposed the respective remanent and induced anomaly es

timates from Figures 5.12 and 5.14. The modeled and observed Orsted and ADMAP

anomalies have correlation coefficients of 0.95 and 0.94, respectively. Subtracting the

modeled anomalies from the corresponding anomaly observations in Figure 5.2 yields

the residual Orsted and near-surface anomalies in Figure 5.16 that are not accounted

for by our magnetization modeling. These residuals are relatively marginal compo

nents of the original Orsted and regional near-surface ADMAP magnetic anomalies

in Figure 5.2.

123

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -2.444 MAX = 3.506 AM = 0.08986 ASD = 0.8073

0 - 0.5

(B)

MIN = -0.8383 MAX = 1.555 AM = 0.9782 ASD = 0.4347

warnMM > 1 m 0.8- 1 p u i 0.6 - 0.8 o ''fr o CD psi 1 I . i 0.2 - 0.4 n a n 0 - 0.2

- 0.2 - 0

-0.4 - -0.2

-0.6 - -0.4

- 0.8 - - 0.6

< - 0.8 Figure 5.11: A) Integrated remanent intensities of magnetization in A/m from Figure 5.6.A and the remanent components of Figure 5.8.B. B) Integrated induced magne tizations from the crustal thickness magnetizations and the induced components of Figure 5.8.B. 124

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -1.966 MAX = 2.034 AM = 0.0 ASD = 0.9378

> 2 1.6 - 2 i n 1.2 - 1.6 EH 0.8 - 1.2 m 0.4 - 0.8 M 0 - 0.4 BH -0.4 -• 0 BS3HHOI -0.8 - -0.4 m -1.2 - -0.8 m -1.6 - -1.2 m < - 1.6

(B)

MIN = -121.1 MAX = 96.15 AM = 0.0 ASD = 29.9

> 60 45 - 60 30 - 45 □ 15 - 30 0-15 -15 - 0 -30 --1 5 -45 --3 0 <-45

Figure 5.12: Remanent magnetic effects in nT from Figure 5.11.A at A) 700 km and B) 5 km altitudes.

125

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -69.77 MAX = -52.73 AM = -61.93 ASD = 3.206

>-52

-56 - -54

| , | -58 - -56

-60 - -58

-62 - -60

-64 - -62

-66 - -64

-6 8 - -66

<-68

(B)

MIN = -59.05 MAX = 13.77 AM = -21.03 ASD = 17.6

28 - -21

-56 - -49 <-56 Figure 5.13: 0rsted99c core field A) inclinations and B) declinations in degrees for 1999.0.

126

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The comparatively weak residuals of Figure 5.16. A indicate that our crustal mag

netization modeling for thickness variations and the KQZ pretty much account for the

magnetic anomalies at 0rsted altitudes. The relatively more substantial near-surface

residuals in Figure 5.16.B, on the other hand, reveal the possible effects of additional

crustal magnetic sources. However, for any perceived magnetic effect of a new crustal

source in Figures 5.15, 5.16.A and 5.16.B, we can clearly obtain the corresponding

magnetization contrast by joint inversion to update our current magnetization model.

5.4 Regional Geology and Magnetization Variations

The remanent and induced magnetization contrasts in Figures 5.11.A and 5.11.B,

respectively, exhibit considerable spatial variability that is not uncommon for the

crust even at scales as small as a few kilometers (e.g. Smith, 1990). The most promi

nent positive magnetizations clearly involve the KQZ where we obtained a maximum

remanent value of 2.1 A/m over the Maud Rise in Figure 5.6.A. Our magnetizations

for the Maud Rise are roughly 15% to nearly 70% lower than the respective values

obtained by Ghidella et al. (1991) and Fullerton et al. (1994) from analyses of the

region’s Magsat anomalies.

Our results suggest eastward extensions of the KQZ beyond the boundary inferred

from the sea floor ages (Dyment and Arkani-Hamed, 1998) that was used to interpret

the sea floor effects in the Orsted data. These extensions reflect the contributions

of the near-surface anomalies in the joint, inversion for magnetization models. The

advantage of joint inversion for refining lithospheric models for satellite magnetic

anomalies is suggested by the 2-D simulation in Figure 5.17 that we developed using

GM-SYS code (Northwest Geophysical Associates, 2000).

127

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -1.858 MAX = 2.222 AM = 0.0 ASD = 0.8848

H.AWtf ; :v. tU*! vV: t

> 2

1.6 - 2

1.2 - 1.6

0.8 - 1.2 0.4 - 0.8 0 - 0.4 -0.4 - 0 -0.8 - -0.4

- 1.2 - - 0.8

- 1.6 — 1.2

< - 1.6

(B)

MIN = -104.8 MAX = 93.75 AM = 0.0 ASD = 24.08

M > 50 gggj 40 - 50 mi 30 - 40 E l 20 - 30 E l 10 - 20 i n 0-10 EM -10 - 0 n -20 --10 I 1 1 ro o SI CO 0 SSI -40 --30 m <-40 700 km and B) 5 km altitudes.

128

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A) MIN = -1.853 MAX = 1.877 AM = 0.00 ASD = 0.7019 Z = 700 km

^ m > 1.5 jflSBB] 1.2 1.5 M m m m jiaaal 0.9 1.2 IHfl 0.6 0.9 □ 0.3 0.6 Hi 0.0 0.3 ma -0.3 - 0.0 i t HI -0.6 o CO Hi -0.9 - -0.6 m -1.2 -0.9 Bfl -1.5 -1.2 m < -1.5

(B)

MIN = -105 MAX = 127.4 AM = 0.0 ASD = 32.38

60 6999 m 45 - 60 mi 30 - 45 □ 15 - 30 m 0-15 -15 - 0 91 -30 --15 in o CO Hi i 1 1 1 <-45

Figure 5.15: Modeled magnetic anomalies from the induced and remanent magneti zation contrasts at A) 700 km and B) 5 km altitudes.

129

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -0.438 MAX = 0.783 AM = 0.0 ASD = 0.23

B8SI > 1.5 BBSMW 1.2 - 1.5 HI 0.9 - 1.2 0.6 - 0.9 □ 0.3 - 0.6 m i 0.0 - 0.3

Im g n jis j -0.3 - 0.0 o CO o CO H i i 1 1 — -0.9 - -0.6 m -1.2 - -0.9 m -1.5 - -1.2 H < -1.5

(B)

MIN = -95.44 MAX = 96.27 AM = 0.0 ASD = 24.53

nun MM > 60 M 45 - 60 m [M l 30 - 45 □ 15 - 30 m 0 - 15

H -15 - 0 m -30 --1 5 CO o I MB 1 B n -45 ■ <-45

Figure 5.16: Residual anomalies in nT that are not accounted for by our magnetization modeling in the A) 0rsted and B) regional near-surface ADMAP data of Figures 5.2. A and 5.2.B, respectively.

130

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. A. Magnetic anomalies at 100 km altitude B. Crustal magnetic sources 8

6 Am*= -0)7 (A/m)

4 „ -2 Arm = 2.1. (A/m) 2.1 (A/m)

-2 L- -10 L- -150 -75 0 75 150 -150 -75 150 Distance (km) Distance (km)

C. Magnetic anomalies at 1 km altitude D. Near-surface anomaly differences 500 500

400 400

300 300

200 200 £100 £ 100

-100 -100

-200 -200 -300 -300 -150 -75 150 -75 150-150 Distance (km) Distance (km)

Figure 5.17: Adjusting satellite anomaly magnetizations for related near-surface anomaly magnetizations.

131

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Suppose at satellite altitude (100 km) we obtained the magnetic anomaly in Figure

5.17.A due to a 2-D rectangular body shown in red in Figure 5.17.B that is 20 km

wide with a 2.1 A/m magnetization. However, if we are given flawed information that

the width of this body is only 15 km, then we would obtain a 2.8 A/m magnetization

for the narrower body that will effectively model the satellite anomaly with negligible

error as shown in Figure 5.17.A. In other words, at satellite altitude we effectively

cannot distinguish these two crustal bodies.

However, at near-surface altitudes the two anomaly effects are quite distinct as

shown in Figure 5.17.C. Hence, subtracting the effects of our starting model from

the ‘true’ near-surface observations yields the anomaly differences in Figure 5.17.D

that may be interpreted by further inversion for the magnetization contrasts shown

for the 3 shaded bodies in Figure 5.17.B. Integrating these magnetization contrasts

with the properties of our starting blue-bordered model gives of course the ‘true’ red-

bordered body that now satisfies the magnetic anomalies observed at both satellite

and near-surface altitudes.

Bordering the I

netizations that include the Riiser-Larsen Sea (RLS) to the east and Weddell Sea

Embayment (WSE) on the west. The paleoinclinations in Figure 5.5.A indicate no

magnetic reversals to help account for these negative contrasts. However, beneath

these basins the crust is thinner than the Maud Rise crust that was thickened by

hotspot activity away from the southwest Indian Ocean Ridge (Schandl et al., 1990).

Hence, crustal thinning beneath the basins may contribute to these regionally negative

contrasts in magnetization. Furthermore, sediment thicknesses up to 5 km have been

reported for the RLS (Hinz and Krause, 1982; Ishihara et ah, 1999; Leitchenkov et ah,

132

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1996), and the WSE (LaBrecque and Keller, 1982). Thus, additional magnetic prop

erty reductions may have resulted from hydrothermal demagnetization of the oceanic

crust beneath the thermal blanketing sedimentary layers (Levi and Riddihough, 1986;

Ghods, 1994).

5.5 Crustal Magnetic Anomaly Perspectives with Altitude

The crustal magnetizations obtained by the joint inversion of magnetic anomalies

independently observed at 5 km and 700 km altitudes can be analyzed for anomaly

predictions at the intervening altitudes for additional perspectives on the crustal ge

ology (von Frese et al., 1999b). Accordingly, we evaluated our magnetization models

for 8 slices of the geomagnetic anomaly field over altitudes ranging from 5 km to

700 km as shown in Figure 5.18. These perspectives provide insight on how the 5

or 6 satellite altitude anomalies break down with decreasing altitude into a complex

multitude of anomalies at the near-surface. Alternatively, we can obtain insight on

anomaly interference effects with elevation by studying how the near-surface anoma

lies coalesce with increasing altitude into the roughly handful of anomalies that are

observed at satellite altitude.

For example, at the near-surface altitudes (Figures 5.18 A-B) the I

inantly characterized by linear maxima along the margins with relatively well defined

interior minima. Its only at altitudes of about 100 km and greater (Figures 5.18.

E-H) that the strong, regionally positive magnetic character of the KQZ becomes

apparent.

Similarly, the near-surface magnetic minima along the coast of East Antarctica

coalesce at altitudes of 100 km and higher with the Riiser-Larsen Sea minimum. The

133

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (A)

MIN = -105 MAX = 127.4 AM = 0.0 ASD = 32.38

Z = 5 km

> 60 45 - 60 30 - 45 □ 15 - 30 0 - 1 5 -15 - 0 -30 --1 5 -45 --3 0 <-45

(B) MIN = -104.9 MAX = 91.61 AM = 0.0 ASD = 28.05

z = 10 km

BH 1— > 60 BSB 45 - 60 IB] 30 - 45

□ 15 - 30 SI 0 -1 5 m -15 - 0

m -30 --1 5 BSB Baal -45 --3 0

H <-45

Figure 5.18: Magnetic anomaly predictions in nT from the combined magnetization model at altitudes of A) 5 km, B) 10 km, C) 25 km, D) 50 km, E) 100 km, F) 200 km, G) 400 km, and H) 700 km.

134

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (C)

MIN == -117.5 MAX = 65.34 AM = 0.0 ASD = 23.61

25 km

> 50 40 - 50 30 - 40 20 - 30

10 - 20 0-10

-10 - 0

•20 - - 1 0 •30 --2 0 •40 --3 0 <-40

(D)

MIN := -73.63 MAX = 49.1 AM = 0.0 = 17.69

50 km

> 40 30 - 40 20 - 30

10 - 20

0 - 1 0

-10 - 0

•20 --10 •30 --2 0 <-30

Figure 5.18: (continued).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (E)

MIN = -42.33 MAX = 38.93 AM = 0.0 ASD = 11.65

Z = 100 km

> 20 15 - 20 10 - 15 □ 5 - 1 0 0 - 5 - 5 - 0 -10 - -5 -15 --1 0 <-15

(F)

MIN = -19.57 MAX = 20.67 AM = 0.0 ASD = 6.069 ■ ^r>°\A I 0° m°c

Z = 200 km

> 15 12 - 15 9 -1 2 6 - 9 3 - 6 0 - 3 - 3 - 0 -6 - -3 -9 - -6 -12 - -9 < -1 2

Figure 5.18: (continued).

136

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (G)

MIN = -5.019 MAX = 5.531 AM = 0.0 ASD =2.125

Z = 400 km

B■EBB a i > 4 3 - 4 m 2 - 3

□ 1 - 2 § § 0 - 1 IBS - 1 - 0 m -2 --1 w -3 --2 m <-3

(H)

MIN = -1.853 MAX = 1.877 AM = 0.00 ASD = 0.7019

Z = 700 km

m > 1.5 BBS 1.2 1.5 HI 0.9 1.2 0.6 0.9 □ 0.3 0.6 (HI 0.0 0.3 n -0.3 0.0 n -0.6 -0.3 m b -0.9 - -0.6 BBSm -1.2 -0.9 n -1.5 - -1.2 M l < -1.5

Figure 5.18: (continued).

137

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. near-surface continental minima are broken up by a maximum over Sveshjfella (SF)

that dies out at altitudes of nearly 200 km and higher. By then, however, the SF

anomaly appears to connect with a positive anomaly over western Enderby Land

(EL) that may reflect an Archean shield or platform (Bormann et al., 1986). The

EL anomaly is weakly expressed in the near-surface data, but becomes increasingly

prominent with altitude.

The anomaly behavior suggested by joint inversion clearly would not be revealed

in the simple downward continuation of the satellite altitude data nor in the upward

continuation of the near-surface magnetic data. Figure 5.19 shows the bias of the joint

inversion anomaly estimates to the satellite and near-surface anomaly observations in

terms of their coefficients of correlation. According to these results, the predictions

are pretty much dominated down to 200 km altitude by the satellite data and up to

25 km by the near-surface data. Over the intervening altitudes between 25 km and

200 km, the joint inversion provides insight on how the boundary value anomalies

may transition into each other that cannot be deduced by the simple continuation of

each of the data sets by itself.

Unfortunately, like any inversion, the results of our joint inversion are not unique,

and hence do not obviate the need for additional surveys at altitudes inbetween the

altitudes of the bounding data sets. Indeed, considerable uncertainty remains on the

magnetic properties of the crust for our application because it involves patches of near

surface and satellite magnetic data with limited coverage and anomaly accuracies.

These limitations generally conspire to yield an incomplete picture of the spectral

properties of the crustal anomalies.

138

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 0.9 e - ~ 0rsted 0.8 Anomalies

0.7

0.6

0.4

0.3 ADMAP 0.2 Near-Surface : Anomalies

100 200 300 400 500 600 700 Alitude (km)

Figure 5.19: Bias of joint inversion anomaly estimates to satellite and near-surface magnetic anomalies. These biases are expressed in terms of the correlation coefficients between the predictions and the 0rsted anomalies at 700 km altitude (dashed curve) and the regional near-surface ADMAP anomalies at 5 km (solid curve).

139

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. DNAG Aeromagnetic Magsat Survey Survey

V. 0.5-

0.0 0.08 0.16 0.24 0.28 Wavenumber, radians/km

Figure 5.20: Normalized amplitude spectra from magnetic surveys flown at Magsat (450 km), U2 (20 km), and conventional airborne DNAG (1 km) altitudes (adapted from Hildenbrand et al., 1996).

For example, Figure 5.20 from Hildenbrand et al. (1996) compares the spec

tral properties of Northern American crustal anomalies mapped by Magsat and the

Decade of North American Geology (DNAG) aeromagnetic survey compilation. The

comparison reveals a significant spectral gap between the two data sets with wave

lengths comparable in scale to the major geologic features that can be substantially

recovered by additional surveying with high-altitude U2 aircraft. Similarly, the geo

logic utility of our joint inversion results can be considerably enhanced by additional

data from U2 aircraft up to 20 km, balloon surveys up to 40 km, and space shuttle

tether surveys up to the 300 km and higher altitudes of the conventional satellite

magnetic missions.

140

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 5.6 Tectonic Implications

A classical geological application of magnetics is to relate anomalies to outcrop

geology and then use the anomalies to extend or map the geology beyond the outcrop

into the subsurface. Satellite magnetic anomalies may be similarly used at the plate

tectonic scale to extend the crustal attributes of a plate to any of its conjugate plates

where the crustal geology is not as well understood. Several studies have suggested

the utility of satellite magnetometer observations for investigating the prerift terranes

of Pangea and Gondwana (Prey, 1982; Galdeano, 1983; von Frese et al., 1986; 1987),

as well as earlier supercontinents (von Frese et al., 1997).

In this section, we consider the use of satellite magnetic anomalies for extending

our magnetic crustal modeling of the Maud Rise to the Agulhas Plateau. Analyses of

the sea floor magnetic anomalies suggest that these two rises were conjugate features

during the Cretaceous as rifting separated southern Africa from East Antarctica (e.g.,

Martine and Hartnady, 1986; Schandl et al., 1990; Antoine and Moyes, 1992; Fullerton

et al., 1994). Initially, the Agulhas Plateau was considered foundered continental crust

based on seafloor dredging results (Tucholke et al., 1981). However, this notion is

being questioned by recent seismic data that suggest the plateau is predominantly

oceanic crust (Gohl and Uenzelmann-Neben, 2001). The enhanced thickening of the

crust for these conjugate features probably involves excessive volcanism related to

hotspot activity during separation of the two blocks (Martine and Hartnady, 1986;

Schandl et al., 1990). Hence, the regional magnetic anomalies for these two rises

should share similar characteristics to reflect the common formation history of the

underlying crustal components.

141

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. To test our 0rsted anomalies of the Maud Rise, we upward continued to 700 km the

1° crustal anomaly estimates for the region of the Agulhas Plateau that were obtained

at 400 km from a combined data set of POGO and Magsat magnetic observations

(Arkani-Hamed et al., 1994). We fit these anomalies to an array of crustal dipoles

using least squares matrix inversion to solve for the magnetizations of the dipoles

(von Frese et al., 1981b; 1998). We then evaluated our point dipole model at 700 km

for anomaly estimates over the Agulhas Plateau region to compare with our 0rsted

anomalies over the Maud Rise region.

Figure 5.21 compares the two sets of satellite anomalies on the 93 Ma reconstruc

tion of the tectonic plates from (Martine and Hartnady, 1986). The remarkable fit of

the positive satellite magnetic anomalies in Figure 5.21 suggests that the magnetic

crustal model for the Maud Rise may be readily extended to the Agulhas Plateau.

Indeed, the NE-extension of the prominent positive satellite magnetic anomaly sug

gests that the Maud Rise model of thickened oceanic crust remanently magnetized in

the Cretaceous may also account for the magnetic effects of the Mozambique Plateau

(MP).

Older plate reconstructions may be tested by the continental satellite magnetic

anomalies along the coast lines. For example, in Figure 5.22 the remarkable match

of the South African satellite magnetic minimum along the Zambezi coast with the

Dronning Maud Land (DML) minimum of East Antarctica strongly favors the Juras

sic plate reconstruction of Martine and Hartnady (1986) for these regions. Petrolog

ical and geochronological studies of the igneous and metamorphic rocks from both

regions suggest that the Dronning Maud Land crust may be analogous to the south

ern Mozambique Belt of East Africa (Jacobs et al., 1998). The magnetic minima

142

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 5.21: Comparison of 0rsted magnetic anomalies of the Maud Rise (MR) with the Magsat/POGO anomalies over the Agulhas Plateau (AG) at 700 km altitude. The comparison is made on the 93 Ma plate reconstruction model of Martin and Hartnady (1986) when a triple junction was detaching the Maud Rise and the northern Agulhas Plateau was forming (Tucholke et al., 1981). Double thick lines mark the presumed spreading ridges.

143

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 5.22: Jurassic plate reconstruction of East Antarctica and Africa from Mar tin and Hartnady (1986) with superposed Antarctic 0rsted and South African Magsat/POGO magnetic anomalies at 700 km altitude.

144

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. reflect regional reductions in crustal magnetizations due probably to thermotectonic

activation in the late Precambrian (Bormann et al., 1986).

5.7 Conclusions

The magnetic anomaly signatures for Maud Rise and adjacent Antarctic areas

are marked by prominent maxima due to thickened oceanic crust with a strong ther

moremanent component that was acquired during the Cretaceous. Satellite altitude

magnetic anomalies may be mostly modeled by the inductive effects of continent-

ocean thickness variations and the remanent effects of the NE-SW trending Creta

ceous Quiet Zone that is centered on Maud Rise. These effects must be analyzed

separately, but the resulting induced and remanent magnetizations can be readily

integrated to effectively model the high-precision 0rsted magnetic anomalies at 700

km altitude. Furthermore, this magnetization model can be adjusted by the joint

inversion of the satellite altitude residual and near-surface magnetic anomalies for

crustal magnetizations that simultaneously satisfy the observed anomaly fields at

both altitudes.

Crustal magnetizations obtained by joint inversion provide new insights on the

behavior of crustal anomalies between airborne and satellite altitudes to enhance the

geologic utility of these independently surveyed data. However, the anomaly pre

dictions are not unique in any application because the inversion always involves a

highly simplified mathematical model of reality and thus is always underdetermined.

Hence, the predictions do not obviate the need for supplemental magnetic measure

ments, especially at the intervening altitudes that may be accessed by high-altitude

145

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. aircraft, balloons, and space shuttle tethers, to better define the geologic relationships

in near-surface and satellite altitude magnetic fields.

Satellites sample the magnetic anomalies of the lithosphere on essentially a global

scale. Hence, these anomalies may be compared for insight on the development and

dynamics of Earth’s tectonic plates. For example, the satellite magnetic anomalies of

the Maud Rise vicinity of the Antarctic and the Agulhas Plateau region of southern

Africa are strongly correlated in plate tectonic reconstructions for the Cretaceous.

Accordingly, the crustal magnetic properties that we inferred for the Maud Rise may

well extend to the crust of the Agulhas and Mozambique Plateaux to account for their

regional magnetic effects. Similarly, the correlation of satellite magnetic minima over

Dronning Maud Land in East Antarctica and the Zambezi coast of southeastern Africa

tends to support the plate tectonic fit of these regions in the Jurassic.

A major component of our magnetization modeling involved the use of crustal

thickness data from the analysis of satellite attitude EGM96 free-air gravity anomaly

predictions (von Frese et al., 1999b). The crustal thickness estimates may be limited,

however, because the Antarctic EGM96 predictions are poorly constrained due the

paucity of terrestrial observations. However, the presently orbiting CHAMP satel

lite is collecting magnetic and gravity data that will provide the highest resolution

anomaly fields mapped to date for the Antarctic. These results will soon be comple

mented by improved gravity data from the GRACE and GOCE missions for further

insights on the Antarctic lithosphere.

146

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER 6

SUMMARY AND RECOMMENDATIONS

The 0rsted magnetic data extracted during the 1999 and 2000 austral winters

when polar external effects were minimally disturbed confirms the validity of the

satellite magnetic anomalies of the Antarctic lithosphere mapped 20 years earlier by

Magsat. A considerable decrease of non-lithospheric noise was observed in the higher

altitude (650-865 km) 0rsted observations relative to the lower altitude (350-550 km)

Magsat data that were obtained with greater measurement errors during the austral

summer and fall periods of maximum external field activity. The lithospheric anomaly

correspondences between 0rsted and Magsat are quite robust despite the relatively

large differences in their orbiting altitudes.

The 0rsted mission will continue to operate through at least austral winter 2005.

Hence continued processing of the 0rsted data for the austral winters of 2002, 2003,

2004, and 2005 is recommended to further reduce non-lithospheric noise effects in

the Antarctic measurements. Improving lithospheric anomaly estimates at 0rsted

altitudes also enhances an important new boundary condition for interpreting the ge

ologic components in the lower orbit (300-450 km) CHAMP and near-surface ADMAP

magnetic data.

147

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Antarctic satellite lithospheric anomaly maps that include only the wavelengths

shorter than degree 13 lack significant anomaly components due to the magnetic

effects of the variations in crustal thickness. These effects may be estimated from

the degree 11+ satellite anomalies using the pseudo magnetic effects of the Antarctic

crustal thickness variations that are reflected in the gravity anomalies at satellite

altitudes. Unfortunately, our understanding of the Antarctic gravity field is limited

by the general lack of terrestrial observations. However, the CHAMP mission is

directly mapping the gravity and magnetic fields in near-Earth orbits. Hence, we

recommend the use of the new CHAMP gravity field for enhancing the lithospheric

magnetic components in the 0rsted, Magsat and CHAMP magnetic observations.

Near-surface magnetic anomaly estimates based only on the inversion of satellite

observations can be very problematic. However, the joint inversion of satellite with

available near-surface magnetic data can yield greatly improved anomaly predictions

for near-surface regions where airborne, shipborne, and terrestrial survey coverage is

lacking. Our simulations found that Orsted lithospheric anomalies offer significant

advantage over Magsat anomalies in this application because their measurement errors

are reduced by an order-of-magnitude relative to the Magsat measurements. Hence,

we upgraded the ADMAP compilation with Orsted-based predictions in the coverage

gaps. However, our simulations also revealed significant advantages for the CHAMP

data over the Orsted observations because of their decreased orbital altitudes. Thus,

we recommend ultimately upgrading the ADMAP compilation with the CHAMP

observations collected during the austral winter cycles.

The increasing availability of magnetic anomaly fields at multiple altitudes re

quires a new approach for modeling the related magnetic properties of the underlying

148

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. lithosphere. We developed an approach that uses joint inversion to test multi-altitude

anomalies for possible lithospheric sources inferred from geological and geophysical

observations. Using this approach on the 0rsted and regional near-surface ADMAP

magnetic anomalies for the region of the Maud Rise, we found that the underlying

lithosphere may well involve regional magnetization contrasts due to crustal thick

ness variations, relatively demagnetized oceanic crust for the Riiser-Larsen Sea and

Weddell Sea Embayment, and the thermoremanently magnetized crust of the KQZ.

Studying the effects of our joint inversion model with altitude revealed that the

crustal thickness anomalies are relatively obscured at the near-surface by other crustal

anomalies, but strongly expressed at satellite altitude where the other crustal anoma

lies are greatly attenuated. These results provide insight on the poor correlations that

are commonly observed between satellite and upward continued near-surface anoma

lies, as well as between the near-surface and downward continued satellite anomalies.

The analysis also indicated that the anomaly effects are relatively poorly constrained

by the satellite and near-surface data at altitudes between roughly 25 km to 200 km.

Hence, our modeling results for the Maud Rise region of the Antarctic would be well

served by additional magnetic measurements over this altitude range such as may be

obtained by high-altitude aircraft, balloon, and space shuttle tether surveys.

Comparing our 0rsted anomalies with Magsat/POGO anomalies over southern

Africa and adjacent marine areas revealed excellent satellite anomaly correlations

across the inferred boundary of the African and Antarctic plates in the Cretaceous.

These anomaly correlations also suggest that our model of thickened, remanently

magnetized oceanic crust for the Maud Rise may be extended as a possible model

for the poorly understood conjugate crust of the Agulhas and Mozambique Plateaux.

149

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Our satellite anomalies also strongly support the plate tectonic fit of the Dronning

Maud Land coast of East Antarctica with the Zambezi coast of southeastern Africa

in Jurassic.

These results demonstrate the utility of satellite magnetic anomalies for test

ing plate tectonic reconstructions. This approach is especially powerful where these

anomalies are tied by joint inversion into crustal models that are also constrained

by near-surface magnetic and other geophysical data. In this case, extensions of the

crustal model into more poorly understood regions across plate boundaries can be

considered in the context of the correlations of the satellite anomalies over the con

jugate plates. This approach will be particularly illuminating for crustal studies of

Antarctica, because the crustal properties for the other continental components of

Gondwana and earlier supercontinents are generally better known.

150

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. BIBLIOGRAPHY

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