Teleseismic Imaging of the Slave Craton and Implications for the Assembly of Cratonic Lithosphere by Chin-Wu Chen

Teleseismic imaging of the Slave craton and implications for the assembly of cratonic lithosphere by Chin-Wu Chen

M.Sc., Geophysics, National Taiwan University, Taipei, Taiwan, 2000 B.Sc., Earth Science, National Central University, Chungli, Taiwan, 1998 Submitted to the Department of Earth, Atmospheric and Planetary Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the

A uthor ...... LIBRA R IES Department of Earth, Atmospheric and Planetary Sciences November 2, 2009

Certified by ...... Stephane Rondenay Associate Professor of Seismology Thesis Supervisor

A ccepted by ...... )...... Maria T. Zuber E.A. Griswold Professor of Geophysics Head, Department of Earth, Atmospheric and Planetary Sciences 2 Teleseismic imaging of the Slave craton and implications for the assembly of cratonic lithosphere by Chin-Wu Chen

Submitted to the Department of Earth, Atmospheric and Planetary Sciences on November 2, 2009, in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Abstract In this dissertation, I investigate the assembly and evolution of the Archean cratonic lithosphere by using two complementary seismological methods to image the lithospheric structure of the Slave craton in Canada. First, I perform surface wave tomographic inversions to constrain the depth dependence of Rayleigh wave phase velocity, shear wave velocity, and azimuthal anisotropy of the Slave cratonic lithosphere. The tomographic images reveal high shear wave velocities associated with a particularly depleted, cold, and unperturbed Archean cratonic lithosphere. Furthermore, the inversions reveal distinct anisotropic domains in the crust, the lithospheric mantle, and the sub-lithospheric mantle. These results reflect the evolutionary history of the cratonic lithosphere. Secondly, I image seismic discontinuities in the lithosphere using receiver-function analysis of converted P-to-S waves. The resulting seismic profile shows a pronounced low velocity discontinuity at -100 km depth beneath the central Slave craton. This seismic discontinuity shows striking spatial correlation with both an electrical conductive anomaly derived from magnetotelluric sounding, as well as a petrologically-defined ultra-depleted layer. The synthesis of coincident seismic, electrical, and petrological evidence supports that this geophysical and petrological boundary represents a compositional interface marked by alteration minerals. I suggest that this mineralization resulted from relict metasomatism associated with an Archean subduction event, which played an important role in the assembly of the Slave craton. Finally, to improve the efficiency and automation of receiver function calculation and data preprocessing workflow, I develop an application of an array-conditioned deconvolution technique for effectively processing large amounts of seismic array data. I demonstrate that this technique is readily applicable to teleseismic array data. This technique is especially effective in turning noisy traces from earthquakes with smaller magnitudes into usable data.

Thesis Supervisor: Stephane Rondenay Title: Associate Professor of Seismology 4 Acknowledgmentst

I used to think that the acknowledgments section was the easiest part of the thesis to write. Reflecting upon the past six years of study and life at MIT, I find myself overwhelmed and humbled by all the help, support, and love I have received; it made this thesis possible and enriched my life so much that I don't know where to start expressing my gratitude. Now I realize writing acknowledgments is no easy task. I am deeply indebted to my advisor, Stephanie Rondenay. He bravely took me under his wing six years ago and convinced me that doing fieldwork is fun, when I wanted to be an armchair seismologist working on the core-mantle boundary. While my physical fitness was intensively challenged, I experienced some of the most memorable and unique moments in my life when in the field. Yes, the all-time favorite was seeing the Christmas-tree-like lights from the GMC Yukon after snowshoeing for hours on the darkened Big Skidder Hill in Washington State. He showed me how doing serious science does not have to conflict with enjoying life, when we relaxed with a beer in the Black Knight Pub in Yellowknife after a long day, sampled whisky in snow-stormed Seattle before embarking on a tough field season, or jumped into the glassy Aegean Sea under the Mediterranean sunshine after rushing through narrow streets on the island of Syros to retrieve a seismic station. I hold these memories dear. Stdphane's encouragement and infinite patience were instrumental in the completion of this dissertation. I would not have come this far without his insight, openness, generosity, and sense of humor. He taught me to be meticulous on details yet always keep the big picture in mind. His enthusiasm is contagious. His deep knowledge of seismological techniques and broad interests in earth sciences led the multi-disciplinary approach in my research. He has more than the characteristics needed to be a successful scientist and mentor; he is a genuinely good guy and good friend. I hope this thesis marks not the end but a continuation of our collaboration and friendship. Sam Bowring was my second general project advisor and my thesis committee chair. He

I sty (1'*andv Ires1(ea(Irch (Ie suppolted by a;I n MIT i sPreiden t ial Fel lship an(d Nationl Science F(mdatin grants EAR-04090aUd EAR-0544996. broadened my horizons by stimulating me to explore the geological and geochemical aspects of the Slave craton and the evolution of the Earth in general. The breadth and depth of his knowledge is unparalleled. And lie never wears a bug net in the field! He was also a most serious and generous reader, offering insightful comments and discussions along the way, which reshaped my understanding of craton assembly.

I started working with Doug Miller during my 2008 summer internship at Schlumberger- Doll Research, on the deconvolution project that eventually became an important part of my thesis. Working with Doug has been a privilege and a pleasure. His mathematical insight always pointed to the core of a problem. I appreciate very much the many afternoons we spent in front of the computer scratching our heads over the behavior of the wiggles. I also enjoyed the Shakespeare and lute songs lie introduced me to. Doug is a sincere mentor and the ideal of a curious, enthusiastic, and versatile scientist. I look forward to the opportunity of future continued collaboration.

I owe much appreciation to Hugues Djikpesse, whom I first met at the career fair at MIT. He later offered me the internship at SDR and was an effective and caring supervisor, always ready to help. His thoughtful suggestions greatly improved the clarity of our paper. I also thank Jakob Haldorsen for his help with my presentation, insightful discussions on deconvolution, and heartfelt support and encouragement.

Many thanks to Rob van der Hilst for his helpful suggestions that improved my thesis. He always welcomed my questions and gave useful advice. Thanks also go to Alison Malcolm. I benefitted much from her stimulating questions on my work.

David Snyder is the co-leader of our fieldwork in the Slave province. I learned much from his intensive experience in the field and in logistics management. His input to our papers and open-minded discussion of new ideas are much appreciated. I thank Rob Evans for the exciting collaboration on the comparison of receiver function image and the magnetotelluric model. His input was critical to this work. Thanks also go to Wiki Royden for her help on my second general project, Lindy Elkins-Tanton for helpful discussion on melting of basalt, and Nafi Toks6z for his encouragement.

Science becomes more fun when you are not doing it alone. In my first few years of Ph.D. study, I was fortunate to interact with the seismology group at Brown University. Dayanthie Weeraratne and Yingjie Yang generously spent much time helping me understand and run the surface wave inversion. Dayanthie keeps sharing with me her thoughts and experience regarding research and life. Occasional consultation with Donald Forsyth and Aibing Li were also appreciated. In addition, I thank Sonja Aulbach, Max Moorkamp, Fiona Darbyshire for sharing their results with me, John Carson and Peter Holman of Gelogical Survey of Canada for giving me access to the Slave craton radiometric data, and Laura Mackenzie and Zhu Zhang for their help with Antelope and Cascadia dataset.

The big family of students and postdocs in EAPS is an amazing source of support and friendship. In particular, I thank Huajian Yao for his last minute and generous help with the neighborhood algorithm and many discussions on surface waves, Fred Pearce for sharing stimulating ideas and his codes, Lili Xu for helping me get started with receiver functions, Jenny Suckale for the wonderful adventure in Greece, Xander Campman for his pleasant com- pany in the Slave, Ping Wang and Chang Li for many discussions and help on seismology in general, and everyone above for numerous fun conversations. I thank Einat Lev, Erwan Mazarico, James Dennedy-Frank, Sarah Johnson, and Krystle Catalli for being awesome officemates at the original 521 and 610, Christy Till and Jay Barr for being my petrology consultants, and Danny MacPhee and Becky Flowers for advice on my second general project. In addition, I thank Maureen Long, Emily van Ark, Kyle Straub, Florian Bleibinhaus, Caro- line Beghein, Rongrong Lu, Samantha Grandi, Darrell Coles, Burke Minsley, Andrea Llenos, Noah McLean, Scott Burdick, David Forney, Qin Cao, Kang Hyeun Ji, Aleksandra Hosa, Jiangning Lii, Shane McGary, Neil de Laplante, Andrey Shabelansky, Mike Krawczynski, Min Chen, Gabi Melo, Junlun Li, Alejandra Quintanilla Terminel, Xin Zhan, Hui Huang, Bongani Mashele, Xuefeng Shang, Haijiang Zhang, Tim Seher, and Sadi Kuleli for being part of the family that I am very fond of. Furthermore, the administrative team Terri Macloon, Beth MacEachran, Carol Sprague, Roberta Allard, Jacqui Taylor, Sue Turbak, and Vicki Mackenna make EAPS such a pleasant and orderly place to work and study. Joe Hankins is the nicest and most helpful librarian. Linda Meinke and Scott Blomquist are the IT squad without whom my computer would probably not listen to me. I appreciate Debbie Levey and her family for warmly welcoming me to join them at various holiday occasions over the years in which I learned about American culture and tradition. I also thank the instructors in the MIT Writing Center, especially Susan Spilecki, for their help on my thesis writing. Special thanks are due to two of my true comrades and best friends, Yang Zhang and Taiwei Wang. Yang is a fellow student in EAPS and my housemate for the past five years. We shared all the ups and downs in research and in life. We cheered each other up and also procrastinated together. He is a man of integrity. Yang, I know you will be great in whatever you choose to do. When you get rich, don't forget to buy me a winery in Napa! Taiwei is a sincere and caring person, and was courageous enough to pursue his dreams of travel in the middle of his doctoral study. Now he has become a successful manager in the travel business. Taiwei, when I need to go check out my winery, give me free flight tickets! Outside EAPS, I have been fortunate to have a big group of great friends, collectively known as 'Salon Chaos,' with whom I share laughs and worries, thoughts and nonsense. Without them my life in Boston would not have been so full and so memorable that leaving becomes so difficult; they are: I-Fan Lin, Mo-Han Hsieh, Chia-Ling Pai, Ling Chao, Lisa Kang, I-Tsung Tsai, Hung-Chi Miao, Tsung-Ren Huang, Li-Yuan Wang, Shwu-Jen You, Hsien-Ho Lin, Ching-An Meng, Pei-Ru Jian, Ching-Ting Pi, Chia-Hui Chen, Jiao Guo, Liang-Yu Chen, Wan-Yi Huang, Chih-Chao Chuang, Yu-Chen Yeh, Hsin-Pei Shi, Carl Hu, Su Wu, Hsiao-Shan Lin, Iris Chang, Wei-Chuan Shi, Wan-Chen Lin, and Bernard Yen. I cherish our friendships and hope to continue sharing with you my journey of life. I am also grateful for the encouragements from my former advisors Ban-Yuan Kuo and Ling-Yun Chiao, and colleagues Shu-Chuan Lin, Wen-Tzong Liang, Teh-Ru Song, and Wen-Che Yu. My deepest gratitude is due to my family. My treasured parents Ching-Ying Cheng and Roger Chen, my brother Chin-Hsueh, Aunt Angela, and my grandmother Su-Jen Hsii are always there looking after me and support my choices although they did wonder what was taking me so long. I also extend my thanks to Yow-Sheue Chen. Finally, my great appreciation to Julia Shu-Huah Wang, my anchor and my muse, whose love, kindness, funniness and silliness make my life full of joy and hope. Contents

1 Introduction 17 1.1 Archean cratons and the cratonic lithosphere ...... 17 1.2 The Slave craton and the POLARIS-MIT portable seismic array ...... 19 1.3 Outline of the thesis ...... 20 1.4 F igure ...... 21

2 Upper mantle seismic structure beneath the Slave craton from Rayleigh wave tomography 23 2.1 Introduction ...... 24 2.2 Data and processing ...... 28 2.3 Phase velocity inversion ...... 30 2.4 Phase velocity results ...... 32 2.4.1 Inversion for 1-D phase velocities ...... 33 2.4.2 2-D phase velocity maps ...... 33 2.5 Anisotropy Results ...... 34 2.6 1-D S-wave velocity structure ...... 35 2.6.1 1-D velocity profile ...... 35 2.6.2 Synthetic tests of 1-D models ...... 36 2.6.3 Damping parameter ...... 37 2.7 Inversion for 3-D S-wave velocity structure ...... 37 2.7.1 The NA optimization ...... 37 2.7.2 S-wave velocity lateral variations ...... 39 2.8 D iscussion ...... 2.8.1 The cold cratonic block in the central Slave craton 2.8.2 The thickness of the cratonic lithosphere ...... 40 2.8.3 Depth-dependent azimuthal anisotropy ...... 42 2.8.4 A mid-lithosphere low velocity layer? ...... 45 2.9 Conclusions ...... 45 2.10 T ab le ...... 46 2.11 Figures ...... 47

3 Geophysical detection of relict metasomatism from an Archean (ca 3.5 Ga) subduction zone 63 3.1 Introduction ...... 64 3.2 Seismic observations ...... 65 3.2.1 D ata ...... 65 3.2.2 M ethod ...... 3.2.3 Single station profiles ...... 3.2.4 Common-conversion-point (CCP) profiles .. .. 3.2.5 The robustness and nature of the central Slave seismic discontinuity 3.3 Seismic vs. magnetotelluric observations ...... 3.3.1 The magnetotelluric observations ...... 3.3.2 Coincident seismic and electrical anomalies . .. 3.4 Causes of the seismic and electrical anomalies ..... 3.4.1 Thermal anomaly ...... 73 3.4.2 Compositional anomaly ...... 74 3.4.3 W ater ...... 76 3.4.4 Partial m elt ...... 77 3.4.5 Sum m ary ...... 77 3.5 Implications for the assembly of cratonic lithosphere ...... 78 3.6 Conclusions ...... 80 3.7 Table ...... 81 3.8 Figures ...... 81

4 Array-conditioned deconvolution of multiple component teleseismic recordings 91 4.1 Introduction ...... 92 4.2 Methodologies ...... 93 4.2.1 Waterlevel deconvolution ...... 93 4.2.2 Array-conditioned deconvolution...... 95 4.3 Synthetic experiments ...... 97 4.4 Application to the Slave craton array data ...... 100 4.5 Conclusions ...... 102 4.6 Figures ...... 104

5 Conclusions 113

A Tables 117

Bibliography 123 12 List of Figures

1.1 A simplified geological map of the Slave craton.

2.1 Simplified geological map of the Slave craton ...... 47 2.2 Receivers and earthquakes ...... 4 8 2.3 Great circle paths at 50 s ...... 4 8 2.4 Example of dispersion data ...... 4 9 2.5 Rayleigh wave vertical sensitivity ...... 5 0 2.6 Model parameterization ...... 5 0 2.7 1-D dispersion curves ...... 5 1 2.8 2-D Phase velocity maps 22-40 s ...... 5 2 2.9 2-D Phase velocity maps 50-100 s ...... 5 3 2.10 2-D Phase velocity perturbations ...... 54 2.11 Anisotropy results ...... 5 5 2.12 1-D S-wave velocity profile ...... 5 6 2.13 Synthetic tests of 1-D S-wave velocity m odels ...... 57 2.14 Synthetic tests of damping parameters ...... 5 8 2.15 S-wave velocity models from NA ...... 5 9 2.16 1-D marginals from NA ...... 6 0 2.17 S-wave velocity lateral variations ...... 6 1

2.18 S-wave velocity perturbation profile ...... 6 2

3.1 Synthesized map of the Slave craton ...... 3.2 RF profiles of station ACKN ...... 83 3.3 RF profiles of station EKTN ...... 84 3.4 Ps conversion points at 100 km depth ...... 85 3.5 Receiver-function and magnetotelluric profiles ...... 86 3.6 SV- and SH-component receiver-function profiles ...... 87 3.7 Depth and amplitude uncertainty of the central Slave seismic discontinuity . 88 3.8 Subduction m odels ...... 89

4.1 An example earthquake array data and the deconvolved results ...... 104 4.2 Construction of synthetics ...... 105 4.3 Summary of synthetic experiments ...... 106 4.4 Comparison of amplitude spectra of deconvolved traces ...... 107 4.5 Earthquakes and receivers ...... 108 4.6 The raw data of four example earthquakes ...... 109 4.7 The deconvolved data of the four example earthquakes ...... 110 4.8 The deconvolved data sections of five receivers ...... 111 4.9 The deconvolved data plotted as a function of earthquake magnitude .. .. 112 List of Tables

2.1 Model parameters used in the Neighborhood Algorithm ...... 46

3.1 End-member velocity models for uncertainty evaluation ...... 81

A. 1 Seismic stations used in this thesis ...... 118 A.2 Events used in the surface wave tomography ...... 119 A.3 Events used in the receiver-function analysis and the array-conditioned deconvolution ...... 120 A.4 Events used in the receiver-function analysis and the array-conditioned deconvolution (continued) ...... A.5 Events used in the receiver-function analysis and the array-conditioned deconvolution (continued) ...... 122 16 Chapter 1

Introduction

1.1 Archean cratons and the cratonic lithosphere

Archean cratons form the nuclei of Precambrian continental shields and represent remnant fragments of the oldest continents that once existed on the Earth's surface [Bleeker, 2001]. These cratons have remained stable for billions of years; therefore, they provide a unique window into the tectonic processes that took place when they were formed. The investigation of the formation of cratons is thus pertinent to address questions such as whether plate tectonics as we see it today played a role in the assembly of the earliest continents, and if so, when it started operating. Archean cratons possess a number of unique characteristics that are thought to be associated with their longevity. These characteristics include low surface heat flows [Pollack et al., 1993], cold and thick underlying continental 'roots' comprising melt- depleted mantle lithosphere [Jordan, 1978, 1988; Grand, 1994; Griffin et al., 1999], and the protection afforded by surrounding Proterozoic orogens [Lenardic et al., 2000]. In particular, the unique combination of thermal and compositional structure of the cratonic lithosphere is viewed as the chief factor contributing to the long-term stability and preservation of Archean cratons. The most long-standing and influential hypothesis relating these effects to the stability of cratonic lithosphere is the 'tectosphere' proposed by Jordan [1978]. This hypothesis invokes that the negative buoyancy resulting from the cooler thermal state of the old cratonic roots is exactly compensated by the intrinsic compositional buoyancy imposed by the low densities of iron-depleted peridotites which made up the roots.

The tectosphere hypothesis successfully explains the stable state of cratonic lithosphere. Evidence from a variety of geophysical, petrological, and geochemical investigations has also emerged to support the presence of deep, cold, and iron-depleted roots underlying Archean cratons. However, the mechanisms responsible for the assembly of the cratonic lithosphere remain a topic of ongoing debate. Over the past few decades, a number of notable hypotheses have been proposed to explain the formation of cratonic roots, including (1) advective thickening of collisional orogenesis [Jordan, 1978]; (2) island arc aggregation [Ashwal and Burke, 1989]; and (3) shallow subduction and underplating of oceanic lithosphere [Helm- staedt and Schulze, 1989]. Geological and geochemical investigations can help discriminate between these models, as they provide direct constraints on the composition of crustal and mantle rocks that can help determine their origins, and thus point to how they were formed. On the other hand, deep probing geophysical methods enable us to image the inaccessible lithosphere, allowing us to delineate the structures pertaining to tectonic processes. In particular, seismic imaging has proven to be a powerful tool for this purpose. The primary goal of this thesis work is to use seismic imaging techniques to investigate the structure of the cratonic lithosphere, focusing on three outstanding questions: (1) What is the depth extent of the cratonic lithosphere? (2) Is the mineral/structural fabric of the cratonic lithosphere uniform, or is it distributed in domains that suggest several episodes of assembly and deformation? (3) Is there evidence of seismic discontinuities within the lithosphere and, if so, what is the association of those structures with the tectonic history of the craton?

In the next section, I introduce the subject of my investigation, the Archean Slave craton, which is located in the northwestern Canadian Shield, and then discuss the data I use in this thesis. 1.2 The Slave craton and the POLARIS-MIT portable seismic array

The Slave craton is the ideal natural laboratory in which to conduct multi-disciplinary investigation of the Archean cratonic lithosphere (Fig. 1.1). It is well exposed, has abundant xenolith-bearing kimberlites which provide petrological constraints on the entire lithospheric column, and it has been insulated from plate margin processes for over 2 Ga, due to the protection from adjacent Proterozoic orogens. Its old age and high degree of preservation have long attracted geologists' attention [e.g., Bowring et al., 1990]. Due to the discovery of diamonds in this region in the past decade, the Slave craton has since been under more intense exploration and study. These studies have resulted in an extraordinarily comprehensive petrological and geochemical mapping of its crustal and mantle composition revealing distinct mantle zonation and layering [Griffin et al., 1999; Grutter et al., 1999; Kopylova and Caro, 2004]. Similarly, several reconnaissance seismic investigations have also delineated multiple seismic discontinuities in the lithosphere and revealed varying anisotropic signatures [Bostock, 1998; Bank et al., 2000; Snyder et al., 2002]. However, these results are based on data recorded at single stations, and thus do not provide sufficient constraints on the extent and short wavelength structure of these observed discontinuities.

In light of the need for denser seismic sampling, the POLARIS consortium of Canada (Portable Observatories for Lithospheric Analysis and Research Investigating Seismicity) undertook the deployment of 22 temporary broadband seismic stations on the Slave craton from 1999 to 2006. In the first project described in this thesis, I use data from the POLARIS network to characterize the structure and fabric of the Slave craton using surface wave analysis.

To facilitate the application of high-resolution seismic imaging techniques, we also deployed 10 broadband seismometers from the MIT instrument pool that supplemented the existing POLARIS network. The combined network was deployed for a period of two years, from July 2004 to July 2006, but only resulted in six months' worth of data, due to the expected power shutdown during the Canadian winter and the unexpected failure of the recording device. Nevertheless, the combined POLARIS-MIT array affords an average station spacing of -20 km, providing the dense array data necessary for two other projects described in this thesis: a receiver function analysis of the central Slave craton and the application of a new deconvolution technique to teleseismic array data.

1.3 Outline of the thesis

Chapter 2 presents the first surface wave tomography of the Slave craton derived from the inversion of Rayleigh wave phase and amplitude data recorded by the POLARIS network. I employ a recently-developed array inversion technique to take into account multipathing and finite frequency effects of surface waves to better constrain the lateral variations and depth dependence of the Rayleigh wave phase velocity and shear wave velocity structures, as well as azimuthal anisotropic signatures in the lithosphere. In Chapter 3, I construct a seismic receiver function image of the central Slave craton using data from the combined POLARIS-MIT array. I synthesize the seismic image with results from magnetotelluric, petrological, and geological analyses to explain the nature of a coincident seismic and electrical conductive anomaly beneath the central Slave craton. I then use these results to discuss possible models of assembly of the Slave craton. An essential data processing step in the receiver-function analysis of Chapter 3 consists in source-normalizing the signals so that they may be stacked. This processing step involves the deconvolution of the recorded data by the source wavelet estimate, an operation that requires searching for an optimal regularization parameter and thus requires extensive user input. This limitation motivates the work in Chapter 4, in which I introduce an array-conditioned deconvolution method that constructs a deconvolution operator based on the noise present in the seismic array data, and therefore relaxes the ad-hoc choice of regularization. I investigate the applicability of the method to teleseismic data processing, and discuss the performance of the method using synthetic data and field data from the Slave craton. This array-conditioned deconvolution presents an effective and automatic means for processing large amounts of array data. Finally, Chapter 5 summarizes the key results of the work in this dissertation...... ----- = M NW W M M 6 M6 -

1.4 Figure

-115 -110

64 6

62 6

-115 -110 * kimberlite pipes central Slave basement complex * Slave craton, juvenile crust

Figure 1.1: Simplified geological map of the Slave craton and surrounding regions. The distribution of major kimberlite fields are shown. The mantle geochemical boundaries are indicated by the dashed blue lines [Griitter et al., 1999]. 22 Chapter 2

Upper mantle seismic structure beneath the Slave craton from Rayleigh wave tomography

Abstract

The knowledge of seismic structure and depth extent of cratonic lithosphere is crucial to understanding the dynamics of cratonic evolution and stabilization, a subject that remains poorly understood. In this study, Rayleigh wave phase and amplitude data are analyzed to constrain velocity structure in the upper mantle beneath the Slave craton, in the northwestern Canadian Shield. We measure phase velocities at periods between 20 s - 142 s (with greatest sensitivity at depths of 28 - 200 km) using crossing ray paths from events recorded by the POLARIS broadband seismic network and the Yellowknife array. To resolve lateral velocity variations under this region, we employ a recently-developed inversion technique that takes into account the multipathing and finite frequency scattering effects of surface waves. The inversion also solves for azimuthal anisotropy at each period, thus providing constraints on the depth of anisotropic characteristics that other methodologies such as shear-wave split-

t Parts of this chapter were published as: Chen, C.-W, S. Rondenay, D. S. Weeraratne, and D. B. Snyder, New constraints on the upper mantle structure of the Slave craton from Rayleigh wave inversion, Geophys. Res. Lett., 34, L10301, doi:10.1029/2007GL029535, 2007. ting cannot recover. Phase velocities obtained for the Slave province are comparable to those from other cratons at shorter periods, but exceed global average by ~2% at periods above 60 s (-80 km depth). The one-dimensional inversion of phase velocities yields high shear wave velocities (4.72+0.2 km/s) representative of cratonic lithosphere between 50 - 200 km depths, which are attributed to the cratonic lithosphere. The depth to the base of the cratonic lithosphere is estimated at 220±65 km. Two-dimensional phase velocity maps indicate noticeable lateral variations. In the western part of the Slave province, which corresponds to the oldest portion of the craton, high velocities are consistently observed from 25 to 33 s (~33 - 44 km). In contrast, lower velocities are present at periods up to 50 s (-70 km) in the central part of the craton, a region that hosts a cluster of kimberlite pipes. Inversion for anisotropy reveals two robust, distinct trends in the Slave lithosphere. First, at shortest periods 20 s - 25 s (~28 - 33 km), the fast direction is N1O0 E ± 200, corresponding mainly to crustal structure. Second, at periods 29 s - 100 s (-37 - 150 km), the fast direction averages N590 E ± 200, which is consistent with shear-wave splitting measurements and is attributed to fossil lithospheric fabric. Anisotropy is also detected at longer periods corresponding to sub-lithospheric depth, but its strength and principal direction are poorly resolved. These anisotropic characteristics indicate that anisotropy exists at all levels in the cratonic lithosphere from crust to lithospheric mantle, as well as in the asthenosphere. Anisotropy in the lithospheric mantle may be representative of subduction episodes that have contributed to the growth and stabilization of cratonic roots in the region. Deeper anisotropy is attributed to asthenospheric flow.

2.1 Introduction

Cratons form the stable nuclei of continental plates and have survived tectonic activities since Precambrian time. Knowledge of the formation and evolution of cratons is important for better understanding tectonic processes active during Earth's early history, which have remained a subject of much debate over past decades and for which a variety of dynamic models has been proposed [e.g., Jordan, 1978; Hoffman, 1990, and the references therein]. Although much evidence shows that thick continental roots exist beneath cratons, a situation that results in their stability and longevity through geological time [e.g., Polet and Ander- son, 1995; Rudnick et al., 1999; Simons et al., 2002], controversies remain regarding the mechanism of stabilization and the depth extent of cratonic lithosphere. The Slave craton in the northwestern Canadian Shield provides a unique laboratory for detailed examination of the structure of cratonic lithosphere. This well-preserved Archean craton hosts the oldest rocks that have been found on Earth, the -4.03 Ga Acasta gneisses [Bowring et al., 1990], and numerous kimberlite pipes that are rich in mantle xenoliths [e.g., Griffin et al., 1999]. In addition, the favorable location of the Slave craton with respect to global seismicity affords a comprehensive data coverage that is ideal for seismological studies. In the past decade, the Slave craton has been exhaustively instrumented by passive seismic sensors, yielding a seismic dataset of unprecedented volume and quality. In this study, we investigate the upper mantle of the Slave craton using new surface wave data and a recently-developed, two-step inversion technique to obtain tomographic images that further constrain lithospheric thickness and anisotropic structure in this region, and to gain new insight into the dynamics of continental evolution.

The Slave province is a small (-600 x 400 km) Archean granite-greenstone craton located in the northwestern corner of the Canadian Shield (Fig. 2.1). To first order, the province can be subdivided into western and eastern parts based on an N-S trending boundary in geochronological, Pb and Nd isotopic signatures [see, e.g., Isachsen and Bowring, 1994]. The western portion, the so-called Central Slave Basement Complex [CSBC; Bleeker et al., 1999], is a composite gneiss and granitoid complex aged 4-2.8 Ga, whereas the eastern half consists of isotopically juvenile (<2.7 Ga) rocks [Isachsen and Bowring, 1994]. The E-W dichotomy is explained by a tectonic model consisting of a series of consecutive terrane accretion events [Kusky, 1989]. The Hackett River (volcanic) arc in the east was formed as an island arc by east-dipping subduction at 2.67 Ga. In front of this arc, the Contwoyto terrane was formed as an accretionary complex. This pair then collided with a microcontinent in the west (the CSBC), before 2.6 Ga.

The Slave craton is flanked by two Proterozoic provinces, the Thelon magmatic zone to the east and the Wopmay orogen to the west. The Thelon magmatic zone is a result of the collision between the Slave and Rae provinces at -1.9 Ga, before which there may have been episodes of east-dipping subduction beneath the Rae province [Hoffman, 1989]. The Wopmay orogen is an accretionary belt that resulted from the docking of terranes to the west of the Slave during successive events of subduction between 1.97-1.84 Ga [Bowring and Grotzinger, 1992]. The northern part of the Slave craton has been affected by the emplacement of the Mackenzie dyke swarm at ~1.26 Ga, which has been attributed to the impingement of a mantle plume beneath present-day Victoria Island [117'W, 71'N; LeCheminant and Heaman, 1989]. Kimberlite magmatism has occurred sporadically over the last 540 Ma, from the Cambrian to the Tertiary, with youngest events taking place between Late Cretaceous (75 Ma) and mid-Tertiary (45 Ma) and being concentrated on the Lac de Gras region, which is now host to several diamond mines [Pell, 1997; Snyder and Lockhart, 2005].

Geochemical and petrological studies of mantle xenoliths have yielded important insight into the mantle composition and structure of the Slave craton. Griitter et al. [1999] analyzed garnet crystals from surface till samples and kimberlitic xenoliths to infer the existence of an ultra-depleted mantle zone centered on the Lac de Gras region. Under the same region, thermobarometric analyses [Griffin et al., 1999] revealed a sharp boundary between ultra- depleted and less depleted mantle layers, confining the former to a depth of 140-150 km. Similar mantle stratification has also been suggested by studies of xenoliths from the northern Slave craton [Kopylova and Russell, 2000]. These studies all indicate a distinct stratification in the Slave lithosphere that is marked by variable degrees of iron-depletion and suggests an assembly by vertical stacking of subducted slabs.

A number of regional and local seismological investigations have helped characterize the deep lithospheric structures of the Slave province. Regional seismic tomography provides constraints on velocity structure in a broad area of the upper mantle. Both body- and surface-wave tomographic models [e.g., Grand, 1994; Li et al., 2009; van der Lee and Fred- eriksen, 2005] have revealed the existence of deep continental root beneath the Canadian Shield extending to -300 km depth. At a smaller scale, Bostock [1998] analyzed converted P-to-S (Ps) wave data from the permanent Yellowknife seismic array in the southwestern edge of the craton, and imaged well-developed mantle stratigraphy between the Moho and the transition zone, which he inferred as evidence for cratonic assembly by shallow subduction and underplating. Cook et al. [1999] also reported a remnant east-dipping slab imaged by a seismic reflection profile across the Wopmay orogen to the southwestern edge of the craton. Although promising, these results were derived over limited areas of the Slave province and more comprehensive, craton-wide studies are necessary to validate these interpretations. A previous teleseismic study by Bank et al. [2000] was carried out using data from a reconnaissance experiment of 13 temporary broadband stations on the Slave craton. The resultant P-wave travel-time tomography revealed higher velocities underlying the oldest part of the craton (i.e., CSBC), while a low-velocity anomaly was observed in the upper mantle at > 350 km depth beneath the Lac de Gras kimberlite field. Based on the strength of the fast velocity anomaly, this model yielded an estimate of the lithospheric thickness to be 250±50 km. More recently, Straub et al. [2004] combined travel-time data from the above experiment and the POLARIS broadband seismic network to improve the resolution of P-wave velocity model for the upper 700 km of mantle beneath the Slave craton. A low-velocity anomaly was observed centered to the south of Lac de Gras extending from 50 to >300 km, leading Straub et al. [2004] to speculate on the link between the low-velocity structure and kimberlite magmatism.

In addition, azimuthal anisotropy in the Slave craton has been investigated in a few shear-wave splitting studies. Bank et al. [2000] found an average splitting time of 1 s from the reconnaissance experiment, with a fast direction of N60 0 Ei20' consistent with the present-day absolute plate motion (APM) of North America. They consequently attributed the lithospheric anisotropy to the influence of plate motion. In another study, Eaton et al. [2004] conducted an experiment along the Great Slave Lake shear zone to analyze co- located SKS-splitting and magnetotelluric data, and found a two-layer anisotropic structure indicative of the presence of fossil lithospheric anisotropy. Their model yielded a crustal anisotropic layer localized near the shear zone. Snyder et al. [2003] also suggested a two- layer scenario from inversion of azimuthally-varying SKS-splitting data of a single station in Lac de Gras region, with an upper layer corresponding to crustal and uppermost mantle structure (0-90 km depth), and a lower lithospheric layer influenced by plate motion. To reconcile inconsistent results among studies mentioned above, including estimates of lithospheric thickness and the existence and depth locations of anisotropic layers, we under- take a surface-wave study from which we obtain higher resolution tomographic images of the Slave's lithosphere than previously available. So far, body-wave tomography has yielded good lateral resolution of mantle structure, but it lacks comparable vertical resolution due to smearing along steep incident ray paths. Surface wave studies provide better vertical resolution in the upper 200 km, but in large-scale regional or global tomographic models, short-wavelength heterogeneities are often compromised by smoothing. Here, we take advantage of Rayleigh wave data recorded at a local seismic network that focuses on the target region, and employ an array-analysis inversion technique that accounts for complexity of incoming wave field influenced by heterogeneities outside the array [Forsyth and Li, 2005]. The dataset and methodology used in this study are first discussed in Section 2.2 and 2.3, followed by a description of our results in terms of phase velocity maps in Section 2.4, and anisotropy in Section 2.5. The 1-D and 3-D S-wave velocity structures are discussed in Section 2.6 and 2.7, respectively. We conclude with a discussion on the implications of our findings for the evolution of cratonic lithosphere.

2.2 Data and processing

We use fundamental mode Rayleigh wave data recorded at the POLARIS seismic network, operated by the Geological Survey of Canada, and the Yellowknife array (Fig. 2.2a). The POLARIS network consists of 22 stations, 15 of which form a ~200-km long NNW-SSE array centered on the Lac de Gras region, while the remaining sites are located in the western Slave. The network encompasses a -600x200-km area in the central and southern part of the craton. All stations use TRD data acquisition systems to record teleseismic data at 40 Hz sampling rate, from three-component broadband seismometers. Two types of seismometers are used: six of them are Giiralp CMG-40T with flat response to ground velocity between 0.016 and 50 Hz, and the other 16 are CMG-3ESP (flat response 0.033 - 50 Hz). The Yellowknife array comprises 18 short-period sensors deployed along a cross- shaped network centered to the west of Yellowknife, and four broadband stations, one at each arm of the cross, whose data are included in our dataset. These four stations use Streckeisen STS-1 seismometers (flat response 0.0027 - 50 Hz). Therefore, data from a total of 25 broadband stations are used, but not all the stations were operational until late 2003. Instrument responses from different types of seismometers have been normalized to that of the CMG-3ESP before data processing. Although grid-like array geometry would be more advantageous for three-dimensional surface wave tomography, the crossing ray path coverage obtained in the current network still offers sufficient resolution of lateral heterogeneity in the central and southern regions of the craton.

The dataset used in this study consists of 42 events (Appendix Table A.2) at epicentral distances ranging from 30' to 120' with magnitude Ms > 5.5 (Fig. 2.2b), that occurred during 2003. Earthquake depths range from 7.6 to 175 km; among these events only one occurred deeper than 100 km. The fairly comprehensive azimuthal distribution of events provides dense crossing of ray paths that insure the resolvability of both lateral heterogeneity and azimuthal anisotropy. Fig. 2.3 shows the event-station path distribution of data at 50 s period, which is representative of the pattern of ray path coverage at most periods analyzed in this study. The white box outlines the region of densest ray coverage, where the highest resolution is expected (hereafter referred to as the target region).

The dispersive Rayleigh waves, which are observed here on the vertical component of the seismograms (Fig. 2.4), provide constraints on vertical variations of S-wave velocity. Vertical sensitivity kernels (Fig. 2.5) show that sensitivity of Rayleigh waves spans a broad depth range that is dependent upon the frequency considered; the distribution in S-wave velocity within this range contribute to the changes in phase velocities we observe in dispersive Rayleigh waves. The peak sensitivity occurs at a depth of approximately one-third the wavelength of Rayleigh wave analyzed. We measure Rayleigh wave phase velocities at 12 frequencies ranging from 7 mHz to 50 mHz (20 s to 142 s). We first filter the Rayleigh wave using a 10-mHz-wide band-pass filter centered at the target frequency. We then isolate the Rayleigh wave in a selected time window to eliminate body-wave phases and noise outside the window. The same window length is applied to all seismograms for a given event-frequency pair.

2.3 Phase velocity inversion

We adopt a two-step procedure for the inversion of Rayleigh wave for S-wave velocity structure. First, Rayleigh wave amplitude and phase data are inverted for phase velocities. Sec- ond, phase velocities are inverted for S-wave velocity structure. In this section, we describe the inversion for phase velocities. Most surface wave inversion methods are based on the assumption that the surface wave propagates on the great circle path, and that the wave front is planar when traveling across a seismic array. However, surface waves may deviate from great circle path due to Earth's heterogeneous structure along the ray path from source to receiver, resulting in changes in direction of propagation across an array, multipathing, focusing/defocusing of energy, and scattering [e.g., Alsina et al., 1993; Friederich et al., 1994]. In recent years, such non-great- circle-path effects have been taken into account in newly developed methods for the analysis of surface waves [Friederich and Wielandt, 1995; Forsyth et al., 1998]. In this study, we employ the array-analysis inversion technique developed by Forsyth and Li [2005] and recently modified by Yang and Forsyth [2006] to include finite-frequency sensitivity kernel in determining the amplitude of the incoming waves. This method models the incoming wave field with a two-plane-wave approximation and simultaneously solves for the characteristics of incoming wave field and phase velocity, as well as anisotropic parameters, taking into account multipathing effects caused by velocity anomalies outside the seismic array. The wave field is represented by the sum of two plane waves with unknown phase, amplitude, and propagation direction. Thus, a total of six parameters are used to describe the initial incoming wave field at each frequency. Although a simple approximation of a possibly more complex interference pattern, this two-plane-wave assumption provides a fairly good description of the spatial pattern of amplitude variation across an array [Li et al., 2003] by using a minimum number of model parameters to account for the non-planar energy in the incoming wave field.

The surface-wave phase velocity, C(w, 0), in an azimuthally anisotropic medium can be expressed as:

C(w, 0) = Bo(w) + B1(w) cos 2w + B 2 (w) sin(20) + B 3 (w) cos40 + B 4 (w) sin 40, (2.1) where w is frequency and 0 is azimuth. Bo is the azimuthally averaged phase velocity;

B1 and B 2 are anisotropic phase velocities. Higher-order terms (the 40 terms in eq. (2.1)) are usually ignored because theory indicates that they should be small for Rayleigh waves [Smith and Dahlen, 1973]. The variation of phase velocities across the array are described by Gaussian weighted average of velocities at model grid nodes. The grid used in this study region comprises 377 nodes (Fig. 2.6). The density of nodes is higher in the middle of the model with a spacing of 0.50 both in latitude and longitude, while two rows of sparser nodes surround the edges with a spacing of 1'. These outside nodes have less damping, and help to absorb travel time variations not accounted for by the two-plane wave approximation.

In the inverse problem, Rayleigh wave amplitudes and phases are determined through Fourier analysis of the filtered and windowed seismograms; each seismogram provides two pieces of information for a given frequency. Before inverting for phase velocities, we first solve for best fitting initial plane wave parameters for each event using the simulated annealing method [Press et al., 1992]. We then employ a generalized linear inversion [Tarantola and Valette, 1982] that simultaneously solves for phase velocities on grid nodes and adjustments to plane wave parameters for each event. The solution to the general non-linear least-squares problem is given by Forsyth and Li [2005]:

1 T Tm=(GTC +-1 - (G C- 'Ad - C- [m - mo]) (2.2) where

Am= [6A ,6A ,6# , 66 ,6 6v]T (2.3) and

0 0 0 0 0 0 l2 0 0

C- = 0 0 0 0 0 (2.4)

0 0 0 V 0

0 0 0 0 '- m is the current model, mo is the starting model, Am is the change to the current model, in which 6A', 6q, and 60 represent the perturbations of the amplitude, phase, and incident angle of the ith plane wave for the kth event; and 6v is the perturbation of phase velocity. 6d is the difference between the observed and predicted data for the current model, G is the partial derivative or sensitivity matrix relating predicted changes in d to perturbations in m; CnJ and C-,' are the a priori data and model covariance matrices, respectively. vii, vg, and Vkk denote diagonal elements in the inverse of the model covariance matrix, and o- is the damping parameter (see Section 2.6.3 for more discussion). In the inversion process, we iterate over eq. (2.2) until the root-mean-squares (rms) mistfit changes less than 0.05 % between two consecutive iterations. Previous experience has shown that in general the convergence is reached within 10 iterations. In the inversion, the standard errors of phase velocity measurements are estimated from the model covariance matrix as the square root of the variance at a given grid node.

2.4 Phase velocity results

In practice, the inversion for phase velocities is performed in two steps. First, we solve for a best uniform, i.e., average, phase velocity model of the target region at each period. These uniform models are then used as starting models in the inversions for phase velocities in each grid node.

2.4.1 Inversion for 1-D phase velocities

Fig. 2.7 shows the average Rayleigh wave phase velocities as a function of period obtained for the Slave craton, along with those from selected global and regional cratonic studies. At shorter periods between 20 and 33 s, the phase velocities increase from 3.7 to 4.05 km/s, with sensitivity in both crustal and uppermost mantle structures. These values are comparable to those of other cratons, e.g., Tanzanian craton [Weeraratne et al., 2003, and references therein]. Between 33 and 60 s, a decrease in slope is observed. The sensitivity of Rayleigh waves at these periods covers a depth range comprising both crust and upper mantle from about 40 to 70 km. At longer periods from 60 to 140 s, the dispersion curve is in general flat with an average velocity of 4.25 km/s, but error bars do not preclude a steady increase from 4.25 km/s at 60 s to 4.4 km/s at 140 s. Only slight fluctuation in phase velocities from 60 to 80 s may be suggested, but the velocities generally increase with period within error. The average value of ~4.25 km/s is significantly higher than the average Rayleigh wave phase velocity obtained by Brune and Dorman [1963] for the whole Canadian Shield in the same period range. It is also higher (-2%) than average cratonic values from other regions, e.g., eastern North America [Li et al., 2003], Superior craton [Darbyshire et al., 2007], South

Africa [Li and Burke, 2006], and global phase velocity models [Ekstr6m et al., 1997].

2.4.2 2-D phase velocity maps

Figs. 2.8 and 2.9 show the 2-D phase velocity maps for periods from 22 to 100 s. The color scale is shifted at 50 s to accommodate the increase in phase velocity at longer periods. Each phase velocity map is accompanied by a map representing twice the standard error. All maps are masked using 0.05 km/s error contour of the 50 s phase velocity map showing information within 95% confidence limits; areas outside the contour have error >0.05 km/s and are regarded as not well resolved. The standard error at periods up to 50 s is less than, or equal to, 0.05 km/s throughout the target region; the error becomes larger at longer periods within the same region. Checkerboard synthetic tests indicate that our inversion successfully resolves lateral velocity variations with scalelengths of -200 km but smaller anomalies are not well resolved. While there are no dramatic lateral variations observed in the maps, average phase velocities are well resolved and generally increase with increasing period. Between periods 22 and 33 s, lateral velocity variations are up to 0.08±0.05 km/s. In the southwestern corner, a region of higher velocity is observed. To better assess the range of lateral phase velocity variations, we display velocity perturbations relative to average velocity (Fig. 2.9). In this representation, some dominant features appear to be consistently present between periods. In particular, a high velocity anomaly is observed in the southwestern to western corner of the target region for period 25-33 s. Low velocities are also observed to the northeast and may persist to periods of up to 50 s. We note that these dominant high and low velocity perturbations are located in the western and eastern parts of the craton, which correspond to the CSBC and the region comprising juvenile crust, respectively. Therefore, the phase velocity perturbations appear to closely reflect the west-east division in surface geological features.

2.5 Anisotropy Results

As stated in Section 2.3, the inversion technique used here also solves for azimuthal anisotropy. Due to the frequency-dependent sensitivity of surface waves, the inversion provides estimates of azimuthal anisotropy as a function of depth. In the uniform anisotropic inversions (Sec- tion 2.4.1), the fast direction is required to be constant laterally within the craton but it is independently solved for at each period. The best fitting anisotropic parameters are shown in Fig. 2.11. We note that some level of anisotropy is detected at nearly all periods analyzed. The anisotropy appears to increase from an average of 1.37% at periods below 40 s to 2.7% for periods above 60 s. At periods below 29 s, the principal fast direction is N1 0 E±20'. The azimuth of anisotropy changes to a NE-SW direction of N59 0 Epm200 at longer periods between 29 and 100 s. At periods >100 s, the fast direction rotates towards a NNE-SSW direction of N23 0E±33'. Note that error bars become large at these longest periods, and therefore the recovered values are not as robust as those obtained at shorter periods.

2.6 1-D S-wave velocity structure

We invert for a one-dimensional S-wave velocity profile using the uniform phase velocity results obtained in the first inversion stage (Section 2.4). In the inversion, we use the ak135 reference model [Kennett et al., 19951 as the starting model, and search for the perturbed S- wave velocity model that best fits the observed phase velocities in a least-squares sense. The inversion process resembles the representation of eq. (2.2) in Section 2.3, but note that the data and model parameters used here are different. Here, the model (m) refers to the S-wave velocity model, and the starting model (mo) is ak135. We follow the layer parameterization in ak135, i.e., 24 layers between the surface and the upper mantle transition zone. The iteration criterion also resembles that in Section 2.3, i.e., we iterate over eq. (2.2), and consider that convergence has been reached when the rms misfit changes by less than 0.05% from one iteration to the next. The predicted phase velocities are computed from S-wave velocity models using partial derivatives obtained through the DISPER80 package [Saito, 1988]. To examine the robustness of the resulting 1-D velocity profile, we also systematically carry out a series of synthetic tests, in which we consider the effects of smoothing and damping parameter in the inversion process.

2.6.1 1-D velocity profile

The inverted 1-D S-wave velocity profile is shown in Fig. 2.12. A high-velocity lid averaging 4.72±0.2 km/s is observed to extend from 50 to -200 km depth, and is inferred to represent the cratonic lithosphere. The high-velocity lid is underlain by a negative velocity gradient at depths 150-250 km. Such negative gradient has been observed in other cratonic lithospheres

[e.g., Debayle and Kennett, 2000; Weeraratne et al., 2003], and is perhaps associated with the transition to a low-viscosity asthenospheric layer. Synthetic tests (see Section 2.6.2) show that S-wave velocities are well resolved down to ~200 km, below which the results become more dependent on the starting model. In light of this observation, we use a set of different starting models to test the robustness of the principal features observed in the resulting model. In all the inversions, high velocities from 50 to ~200 km depth are resolved independently of the starting model, indicating that this structure is required by the data. The presence of a high-velocity lid in this depth range is supported by continental/global- scale tomographic results [e.g., van der Lee and Frederiksen, 2005], which show a ~4% positive (fast) anomaly in the upper mantle beneath the Slave craton relative to reference Earth models. Finally, we also observe an apparent velocity reduction at -100 km depth. Synthetic tests in Section 2.6.2 show that the inversion tends to smooth rapid velocity contrasts. Therefore, that the low velocity layer is able to be retained in the resulting model indicates that this feature is also required by the data.

2.6.2 Synthetic tests of 1-D models

To evaluate the robustness of the inversion, we invert predicted phase velocities of known S- wave velocity models (initial models) from the literature and compare the resultant models to the initial models; the results of this exercise are shown in Fig. 2.13. For cratonic regions (Northern Superior, Kaapvaal, and India) where high velocities are present in a thick lithosphere, the inverted models capture well the general velocity structures of the initial models, but fail to recover sharp edges and introduce smoothing in the overall structure. This smoothing effect is even more significant in the Tanzanian, Hawaiian, and Iceland profiles, where the lithospheres are thinner and clear upper mantle low velocity layers are present. These tests indicate that the inversion may result in underestimated velocities by an average of 2%, especially in the vicinity of sharp velocity contrasts, and that a smoothed velocity gradient observed in the inverted model may represent a stronger velocity contrast in the initial model (i.e., recovered contrasts are on average 5% smaller than those present in the original models). 2.6.3 Damping parameter

In the inversion, the damping parameter is assigned as a dividing factor in the diagonal of the inverse of the model covariance matrix (see eq. (2.4)). Thus, a smaller parameter value imposes more damping on the resulting model, forcing the resulting model to remain close to the starting model. In contrast, a larger parameter value results in less damping, and the resulting model may vary more freely, sometimes causing artificial fluctuations especially when the data are poorly constrained. We vary the damping values from 0.05 to 0.3 and show the inversion results in Fig. 2.14. We observe that the velocity contrast between the high and low velocity regions indeed increases as the damping value increases. Conversely, when the damping value is smaller, the resulting model is more influenced by the starting model. In this study, we choose a damping parameter of 0.1 because the resulting model achieves the smallest rms misfit between the predicted and observed phase velocities.

2.7 Inversion for 3-D S-wave velocity structure

As described in Section 2.4.2, we obtain phase velocities as a function of period for each grid point in the study region. In this section, we invert these 1-D phase velocity profiles for S- wave velocities as a function of depth at all grid points. We adopt the neighborhood algorithm [NA; Sambridge, 1999a, b] to search in the complete model space for the S-wave velocity models that best fit the phase velocity observations. This approach has been successfully applied to a number of global and regional surface wave tomographic studies [e.g., Beghein et al., 2002; Yao et al., 2008].

2.7.1 The NA optimization

The NA is a two-stage direct search technique that surveys the parameter space to find an ensemble of 'good' data-fitting models, rather than seeking a single optimal model. The first stage, sampling, uses a geometrical construct, the Voronoi cells, to approximate the misfit function and drive the search towards the best data-fitting regions, while continuing to explore a wide variety of different models. In each sampling step, the algorithm calculates the misfit in the model space and uses this information to direct the next sampling, therefore it is derivative-free and self-adaptive. It only uses two 'tuning parameters': n, the number of models generated at each iteration, and nr, the number of 'best' data-fitting Voronoi cells in which random walks are performed at each iteration. The result of this sampling stage is a distribution of misfits, which approximates the posterior probability density function (PPDF).

The second stage, i.e., the appraisal stage, generates a 'resampled ensemble' using the information in the available ensemble derived from the sampling stage. Then, this resampled ensemble is integrated numerically to compute the likelihood (the 1-D marginal PPDF, or 1-D marginals) associated each model parameter, whose width can be used to infer the model uncertainty. One can also compute the covariance matrix (2-D marginals) to infer trade-off between two model parameters.

The model parameterization used in the NA is listed in Table 2.1. We illustrate the performance of the NA using results at the grid point [110.25 0W, 65.250 N]. A total number of 13,680 models were generated in the sampling stage of NA. Then, the 1-D marginals are computed from these models and their misfits, and are used to determine the posterior mean value and standard error for each model parameter. A most likely (best data-fitting) model can be determined by the peaks of the 1-D marginals. Fig. 2.15 shows the posterior mean model and its standard error of the example grid point. The corresponding 1-D marginals are shown in Fig. 2.16. The width of the 1-D marginal distribution indicates that the crustal and the two uppermost mantle layers are well constrained. In these layers, the posterior mean S-wave velocities (the black vertical line in each panel in Fig. 2.16) are close to the most likely values. Due to the depth-dependent sensitivity, fundamental mode Rayleigh waves provide weaker depth resolution toward longer periods, hence the poorer constraints on the lower upper mantle layers. The standard errors are in average 0.24 km/s in the crustal layers, 0.25 km/s in the top two upper mantle layers, and 0.28 km/s in the other mantle layers. The Moho depth is poorly constrained at this grid point, determined at 38.12 km depth, with a standard error of 2.8 km.

The posterior mean model is determined for each grid point, and these models are combined to infer 3-D velocity variations.

2.7.2 S-wave velocity lateral variations

Fig. 2.17 shows the resulting S-wave lateral variations of the study region at depths of 10, 30, 50, 70, 100, and 150 km. The geochemical boundaries are also outlined in the figure. Throughout the crust and the lithospheric mantle, the lateral variations in S-wave velocity are -<3%. In the central to western craton, there appears to be a prominent high velocity region extending from the lower crust (-30 km depth) to the uppermost mantle (-80 km depth), which is spatially coincident with the CSBC. The lateral extent of this region also appears to be confined by the outline of the mantle geochemical boundary by Griltter et al. [1999] (blue dashed lines). To the east, a low velocity zone is observed at 50-80 km depth, centered roughly on the petrologically-defined ultra-depleted layer (UDL) (green box; Griffin et al. [1999]) in the mantle and on the Lac de Gras kimberlite field. Below 100 km depth, the lithospheric mantle is characterized by high velocities, with the highest velocities observed to be coincident with the UDL. Fig. 2.18 shows a velocity cross-section (orange line in Fig. 2.17f) perpendicular to the geochemical boundaries. The confined high velocity structure is 3-5% faster than the reference 1-D model. In the uppermost mantle from Moho to 90 km depth, the S-wave velocity is -2.5% lower than the reference model.

2.8 Discussion

We have derived Rayleigh-wave phase velocity variations and S-wave velocity structure for the lithosphere of the Slave craton. The Slave's lithosphere exhibits a thick, high-velocity region, and anisotropy is observed within the crust, the lithospheric mantle, and asthenosphere. In this section, we discuss the determination of the thickness of cratonic lithospheric and the implication of depth-dependent azimuthal anisotropy for the Slave's evolutionary history. 2.8.1 The cold cratonic block in the central Slave craton

The lateral extent of the high S-wave velocity structure observed from the S-wave velocity in the Lac de Gras region at -100-200 km depth is spatially coincident with the geochemically distinct mantle layer. Petrological evidence from mantle xenoliths in the Lac de Gras region also indicates the presence of the ultra-depleted layer extending from -100 km to -140-150 km depth. In addition, the 1-D S-wave velocity profile shows a low velocity layer centered at -100 km depth, overlying a high velocity lithospheric lid down to -220 km depth. Taken together, we interpret that the high velocity structure represents the ultra-depleted cratonic block in the central Slave craton, and the low velocity layer likely marks its upper boundary. In Chapter 3, we draw on additional petrological evidence showing the presence of a compositional front at -100 km depth in this region, and suggest that the central Slave craton may be a remnant fragment of a once larger continent.

2.8.2 The thickness of the cratonic lithosphere

The depth extent of cratonic roots has long been a source of debate, especially since the proposal of the tectosphere hypothesis [Jordan, 1975], which suggests that an old, chemically distinctive continental keel persists to >300 km depth beneath cratonic regions. Thermal, petrological, and electrical studies on the continental lithosphere all indicate, however, that the conductive continental root is at most 200-250 km thick [Jaupart et al., 1998; Rudnick et al., 1998; Hirth et al., 2000]. Global/large-scale regional seismic tomography has shown high velocity structures representative of cratonic roots that may extend to >-250 km depth

[e.g., Grand, 1994; van der Lee and Nolet, 1997; Ritzwoller et al., 2002; Zhou et al., 2006; Li et al., 2009]. In the North American continent, van der Lee and Frederiksen [2005] suggested that the average lithospheric thickness is -250 km based on S-wave velocity model obtained from surface-wave inversion. Similar depth ranges have also been observed in Australian continent and Kaapvaal craton [e.g., Simons et al., 1999; Debayle and Kennett, 2003; Fouch et al., 2004]. Gung et al. [2003] pointed out, however, that seismic anisotropy induced by the low-viscosity asthenospheric channel can account for the discrepancy between the >300 km and the 200-250 km continental lithospheric thicknesses inferred by global versus regional studies, respectively.

One important objective of this study is to determine the depth extent of the Slave craton's lithosphere. Estimates of lithospheric thickness from seismic studies often vary depending on the criteria used to define the lithosphere and asthenosphere. In body-wave tomography, the depth extent of high-velocity regions (i.e., 0.5-2% above global velocity average) in the uppermost mantle has been used to define the extent of the lithosphere

[e.g., Grand, 1994]. This definition may, however, overestimate the lithospheric thickness due to vertical smearing. In surface-wave tomography, on the other hand, the base of the lithosphere is inferred from the strongest negative gradient in S-wave velocity marking the base of a high-velocity lid [e.g., Debayle and Kennett, 2000]. Weeraratne et al. [2003] also noted that, if the lithosphere-asthenosphere transition is due to a gradual thermal effect, then the top of the negative gradient may mark the lithosphere/asthenosphere transition. However, if the velocity contrast at the base of the lithosphere is due to other factors such as onset of melting, increase in water content, or compositional change, the top of the negative gradient could underestimate the thickness.

Synthetic tests (Section 2.6.2) show that, for cratonic regions, the recovered negative gradient marking the base of the lithosphere is usually shifted toward depth shallower than where it really is. Thus, the depth at which the negative gradient reaches the slowest velocity may approximate the depth of transition from the lithosphere to asthenosphere in the initial model. In our S-wave velocity profile through Archean stable lithosphere, high velocities of 4.7±0.2 km/s observed from 40-150 km depth are up to 5% higher than global averages (i.e., ak135) in this depth range. Below 150 km, we observe a negative gradient that reaches a minimum of -4.68 km/s at -275 km depth, consistent with the ak135 velocity at this depth (within error). If we use the center of the negative velocity gradient to infer the base of the lithosphere, we obtain a depth of 220+65 km, as derived in the 1-D S-wave velocity profile. However, reduced depth sensitivity of long period Rayleigh waves prevents us from uniquely resolving structures below -200 km depth. Therefore, we suggest that the base of the Slave's lithosphere occurs at no less than 150 km, and that a thicker lithosphere that extends to 250 km depth [van der Lee and Frederiksen, 2005] cannot be ruled out at this stage. The thickness of the Slave craton's lithosphere appears to be similar to that found in other Archean cratons, such as Kaapvaal [-250 km; Fouch et al., 2003] and Siberia [~220 km; Priestley and Debayle, 2003]. Our results generally agree with that from previous studies on the S-wave velocity structure of the Canadian Shield [Frederiksen et al., 2001], and P- wave travel-time tomography of the Slave craton [Bank et al., 2000], both of which suggest a -250-km thick lithosphere. In addition, the thickness agrees with the thermal thickness estimate in a global study by Artemieva and Mooney [2001], which is also -250 km. Our results agree with the idea that the Slave lithosphere observed at present time has remained stable since its assembly. It has been suggested that a shallow subduction/underplating event associated with the Wopmay Orogeny, in the Proterozoic (-1.9 Ga), represents the final stage of the Slave craton's stabilization [Bostock, 1998; Davis et al., 2003; Snyder, 2008]. Since then, the Wopmay orogen has likely protected the craton from being affected by the asthenospheric flow at the western edge of the North American craton. It has been reported in Australia [Simons et al., 1999] that the Archean cratonic regions to the west of this continent may have experienced destabilization during continental breakup, resulting in a thinner cratonic root (-150 km thick) than that beneath the Proterozoic region in the center of Australia (-300 km thick). Such kind of cratonic root destruction has also been suggested for the Tanzanian craton, but there it is attributed to the upwelling of an upper mantle plume [Weeraratne et al., 2003]. Although the emplacement of dyke swarms in the northernmost part of the Slave craton has been attributed to plume activity (see Section 2.1), there is no geophysical or petrological evidence that the current Slave's lithosphere experienced significant basal erosion associated to either continental breakup or plume in- teraction. Therefore, the Slave craton likely represents an example of cratonic lithosphere with undisrupted long-term stability.

2.8.3 Depth-dependent azimuthal anisotropy

The cause of continental seismic anisotropy has remained a contentious issue, for which two end-member hypotheses have been proposed: one suggests that anisotropy reflects the inher- ent lithospheric fabric associated with past deformation [e.g., Silver and Chan, 1991], and the other favors a correlation between lithospheric anisotropy and the present-day absolute plate motion (APM), thus recording current asthenospheric flow direction [e.g., Vinnik et al., 1992]. Recent advances in analysis method and observation of seismic anisotropy result in much evidence that points to the combination of both [see, Fouch and Rondenay, 2006, for a review]. For the Canadian Shield as a whole, the direction of APM is roughly parallel to the orientation of the lithospheric fabric, so both causes would give similar azimuth of fast polarization [Bokelmann and Silver, 2002]. For the Slave craton, the APM direction is reported as 2250 (or S450 W) [Minster and Jordan, 1978].

Until now, vertical distribution of anisotropy has only been elusively inferred in the Slave craton. Bank et al. [2000] suggested a solely sub-lithospheric origin; however, splitting data only provide limited constraints on the vertical extent of anisotropy. Conversely, here we find that anisotropic signatures are robustly observed at most periods between 20-100 s, indicating that anisotropy is present in the lithosphere as well. The anisotropy with APM- parallel fast direction is observed between 29 s and 100 s periods, an interval with greatest depth sensitivity ranging between sub-Moho to lower lithosphere (50-150 km). These results are consistent with those of Snyder and Lockhart [2005], who suggested solely on the basis of SKS-splitting results that the main component of mantle anisotropy may be caused by large-scale, shape-preferred orientation (SPO) geometry within the lithosphere. In their interpretation, this SPO is attributed to a concentration of parallel kimberlite dykes that are aligned with the direction of APM, i.e., perpendicular to the direction of minimum horizontal stress, in which fractures would have opened to accommodate kimberlite dyke intrusions.

Our results also indicate variations of anisotropy with period, but due to the broad depth range of surface wave sensitivity kernels, we only suggest the existence of a change in azimuth from a N-S fast direction at short periods below 29 s to a NE-SW fast direction at longer periods. The shallow N-S trending anisotropy may correspond to regional crustal and uppermost mantle structure resulting from Archean tectonic processes (e.g., sutures) that predated the stabilization of the Slave cratonic lithosphere [Davis et al., 2003], which, as described in Section 2.1, involved a series of E-W accretionary events. An independent study by Snyder and Bruneton [2006] reached similar conclusions by jointly analyzing SKS- splitting and Rayleigh wave dispersion data. In our study, the anisotropy results at periods >100 s are less well constrained, but we observe a consistent change in fast direction that may result from APM-oblique asthenospheric flow due to topography at the base of the lithosphere.

Some first-order calculation can help assess the contribution of anisotropy by comparing the delay time of the splitting data with that from the anisotropy constrained by our surface wave analysis. The calculation is described as follows: We have three known variables: the thickness (h) of the layer through which the seismic waves propagate, the average seismic velocity (V) and the anisotropy (a). Given a fast- and a slow-polarized velocity, Vf and Vs, respectively, the splitting time is 6t = h/(Vf - Vs), where Vs = 2V/(a+2), and Vf = (a+1)Vs. From our anisotropy results, the 40-km thick crust with 1% anisotropy and average S-wave velocity of 3.8 km/s would result in a delay time of 0.11 s. This value is consistent with that of the shear-wave splitting analysis by Silver and Chan [1991], who concluded that the anisotropy contributed by the entire crust in the Canadian Shield is on the range of 0.1-0.3 s. If we assume a 200-km-thick homogeneous layer as the lithospheric mantle with average S-wave velocity of 4.75 km/s and 1.8% anisotropy, its contribution in delay time would be 0.8 s. As a result, the whole lithosphere would give the delay time of 0.91 s. Some additional contribution from the sub-lithosphere may therefore be required to fully reconcile the average 1 s delay time derived from Bank et al. [2000]'s splitting analysis (Fig. 2.11).

Debayle et al. [2005] reported global azimuthal seismic anisotropy and argued that the Australian continent is the only region where its anisotropy strongly correlates with present-day plate motion, probably because it is the fastest moving continent (-8 cm/yr). In comparison, the North America plate is moving with an average speed of -2 cm/yr to the southwest [Gripp and Gordon, 2002]. Nevertheless, the depth constraint presented here suggests that the sub-lithospheric contribution to the observed anisotropy is present for the Slave craton, although the amount may be less than that of the Australia. 2.8.4 A mid-lithosphere low velocity layer?

In our 1-D S-wave velocity profile, we observe a velocity reduction centered at ~100 km depth. Furthermore, the lateral variations of S-wave velocity also indicate a significant low velocity region (-3% lower relative to the reference model) occurring from below Moho to -100 km depth beneath the Lac de Gras region. In Chapter 3, we use receiver-function analysis of P-to-S (Ps) converted waves to image the lithospheric structure beneath the central Slave craton, and observe a pronounced seismic discontinuity at ~100 km depth in the same region. In conjunction with results of magnetotelluric soundings and mantle xenolith analysis, we interpret that this seismic discontinuity represents a metasomatic boundary associated with an Archean subduction zone.

2.9 Conclusions

The surface-wave study described in this chapter has yielded new insights into the depth and velocity structure of the Slave's cratonic lithosphere, as well as into the vertical distribution of anisotropy in the upper mantle. The Slave cratonic root persists to a depth of -220 km, which is comparable to lithospheric thickness found in other Archean cratons. The observed high velocities in both 1-D S-wave velocity profile and 3-D structure indicate a particularly depleted and/or cold, unperturbed Archean continental lithosphere, and comparisons with other cratonic environments suggest that the Slave craton may be an end member in this respect. Furthermore, distinct anisotropic domains are revealed in the crust, the lithospheric mantle and the sub-lithospheric mantle. The layered anisotropic signatures may reflect the Slave craton's assembly and evolution: the crustal anisotropy reflects the recent deformation; the lithospheric mantle anisotropy indicates possible ancient subduction processes, and the anisotropy in the sub-lithosphere may depict the present-day dynamics of mantle convec- tion. The Archean lithosphere of the Slave craton exemplifies long-term stability due to the protection of surrounding Proterozoic orogens. 2.10 Table

Model parameter Reference value Perturbation range Average perturbation Moho depth 38 (km) [-5 51 km -0.48 km V, in the upper crust 3.5 km/s [-0.8 0.4] km/s 0.017 km/s V, in the lower crust 4.0 km/s [-0.8 0.4] km/s -0.027 km/s V, in the Moho-90 km 4.7-4.63 km/s [-0.6 0.6] km/s -0.031 km/s V, in 90-130 km 4.63-4.78 km/s [-0.6 0.6] km/s 0.051 km/s V, in 130-170 km 4.78-4.8 km/s [-0.6 0.6] km/s 0.079 km/s V, in 170-220 km 4.8-4.68 km/s [-0.6 0.6] km/s 0.094 km/s V, in 220-280 km 4.68 km/s [-0.6 0.6] km/s 0.039 km/s

Table 2.1: Model parameters as well as their reference values, perturbation ranges, and average perturbation in NA. The reference values are according to the 1-D S-wave velocity profile (Fig. 2.12). The perturbation ranges are with respect to the reference values. The average perturbation is the average perturbation for all grid points in the study region. 2.11 Figures

62 6

-115-110 -105 * kimberlite pipes central slave basement complex Slave craton, juvenile crust

Figure 2.1: Simplified geological map of the Slave craton and surrounding regions. The brown area is denoted as the central Slave basement complex (CSBC) by Bleeker et al. [1999]. Dashed blue lines indicate geochemical boundaries identified by Griitter et al. [1999]. (a) -120* -116' -112* -108" -04' 68" 4

64*

62"

-720" -112' -108' Figure 2.2: Receivers and earthquakes. (a) POLARIS and Yellowknife seismic stations used in this study (red triangles). Dotted white lines outline the boundaries of the Slave craton and CSBC. (b) Azimuthal distribution of 42 events in the current dataset. The epicentral distances range from 30' to 120'. The yellow box indicates the location of the Yellowknife array.

-120" -1_6 -11' -108" -104

60- f4 4 60' -720" -104' -116* -112' -108.

Figure 2.3: Great circle paths at 50 s are representative of the ray path coverage in this study. The white box outlines the target region where highest resolution is expected due to the optimal density of crossing ray paths. LGSN 2003/05/21 depth=10 km; distance=7360 km; mb=5.7; baz=52.5

Figure 2.4: Dispersion of Rayleigh wave is shown for an event in northern Algeria recorded at station LGSN located to the south of Lac de Gras region. Filters have bandwidth of 10 mHz centered at 12 periods ranging from 20 s to 142 s (7 mHz to 50 mHz). 50 - t

100 -

150 -

c 200 -

250

300

350

400 - 0.000 o.o5 0.010 0.015 0-020 0.025 0.030 6c/Sp

Figure 2.5: One dimensional sensitivity kernels for phase velocities from 20 s to 100 s. The depth of peak sensitivity occurs at 1/3 of the wavelength and is affected by structures in a broader depth range as the period increases.

-120" -116' -112* -108* -104*

-12 0* -116* -112* -10 8' -10 4'

Figure 2.6: Distribution of grid node locations within the study area for phase velocity inversions. -~ , I I I I

4.4

4.3

4.2

zE.4.1

o 4.0

' 3.9 Slave .2 -Can. Shield 3.8 - [Bre & Dorm n, 1963] Tanzanian 3.7 [Weeraratne et aL, 2003] Cmton average 3.6 - [.strom et al., 1997]

3.5- , i i i 0 20 40 60 80 100 120 140 160 Period (s)

Figure 2.7: Uniform Rayleigh wave phase velocities as a function of period for the study area. Red line shows the phase velocities obtained for the Slave craton in this study, whereas other color lines denote phase velocities of other cratonic entities for comparison (see legend). Uniform anisotropy is simultaneously solved in the inversion and is presented in Fig. 2.11. Error bars indicate 2o- errors......

phase velocity std error --116" -112' -108' -116' -112" -108"

66* 22s.16 4.12

4.04 4.00 62' L.7 km/s 3.96 < M 64* - 1 3.92 O 3.88 3.84

3.-/6 3.72 62" 3.856 km/s

66' s8.7

64'

62' 3.951km/s

66- 33 s

64'

62* .045 km/s

66" 40 s

64"

62' N.097 km/s

0.02 0.04 0.06 0.08 0.10 2 x Standard error (km/s)

Figure 2.8: Two-dimensional phase velocity maps for periods from 22 s to 40 s (left column). The color bar for velocity is to the right; contour intervals are 0.02 km/s. The starting model for inversion is the 1-D phase velocity profile shown in Fig. 2.7 (red line), whose values are also indicated in the lower right corner of velocity maps for each period. The trade-offs from anisotropy are considered in the inversions and will be discussed later. The map in right column shows 2- error of the recovered phase velocities for each period. All maps are masked to show the information within 95% confidence level (the target region). White triangles are locations of seismic stations, and boundaries of the Slave craton are outlined in gray dashed lines. phase velocity std error -116' -112' -108' -116' -112' -108' - 4.32

-4.28

-4.24 * 0 - 4.20 3 -4.16

- 4.12

0.02 0.04 0.06 0.08 0.10 2 x Standard error (km/s)

Figure 2.9: Phase velocity maps for periods from 50 s to 100 s as described in Fig. 2.8. The range of lateral velocity variations decreases somewhat with increasing period, as the sensitivity spreads to a broader depth extent and results in a decrease in depth resolution. As shown in the right column, the standard error increases within the target region at longer periods. Contour intervals for the standard error are 0.01 km/s. Also shown are the strikes of the Bathurst fault system to the northeast and the Great Slave Lake shear zone to the southeast (gray lines in the velocity maps) -116' -112' -108' -116' -112* -1080 -116* -112 -108*

66" 22ss6

/ \ 64*

62 - .777 km/ .045 km/ 0 .278 km/

h25as e' 80vse 64o

64"

62w .951 km/vle .59 km .28 kmS Fiur 6610Maps of2D'hseveoct petubtin forpeios 22s-10' Te etr

a a1.0 -0.8 in. th pesm-0.2 2s to 0 . Phase velocity perturbations (%)

Figure 2.10: Maps of 2-D phase velocity perturbations for periods 22 s - 100 s. The perturbations are calculated with respect to the 1-D phase velocity profile of Fig. 2.7 (red line), with reference values indicated in the lower right corner in each panel. The Slave craton is depicted by gray dashed lines, whereas the east boundary of the CSBC is outlined in white. Contour intervals are 0.1%. The degree of lateral perturbations decreases generally with increasing period, while some prominent features are consistently seen. In particular, we observe a pronounced high velocity anomaly from 25 s to 33 s in the west part of the craton, and a low velocity anomaly in the east, persisting from 22 s to 50 s. (a) I N I | I I I I I I I

I | I I1 I I I l I I I I 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 period (s) (b) -120' -116' -112' -108'

~62*

60-

-104*

Figure 2.11: Anisotropy results. (a) 1-D anisotropy model found for the study area in the inversion for each period. Fast directions are indicated by red bars in map view, with gray vectors denoting 2o errors. Vertical blue error bars indicate 2o errors on estimated strength of anisotropy. (b) Anisotropy results from SKS-splitting analysis of Bank et al. [2000]. Black bars point in the direction of the fast split wave; the length of bars represent the delay time between two split waves as indicated in the legend. 50 -

100

150 E .c 200 41-J

250

300

350 --- Slave ---- ak135 400 - I , I . I . I . i . i . 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 Vs (km/s)

Figure 2.12: 1-D S-wave velocity profile (black line) obtained from inversion of phase velocity in Fig. 2.7. The starting model (dashed line) is ak135. A moderate damping parameter of 0.1 is used in the inversion. See text for further discussion. 0

50 - 50 - ~ ~50

100 100 100 E 150 150 150 c 200 200 200

- 250 250 250

-300 300

-350 - 350 N. Superior .1 :aapvaal .400-, S I I . . I . . . I . 1 . 400 - , 3.2 3.6 4.0 4.4 4.8 5.2 3.2 3.6 4.0 4.4 4.8 5.2 3.2 3.6 4.0 4.4 4.8 5.2 Vs (km/s) Vs (km/s) Vs (km/s)

100

E 150

. 200 0. 250

-350 - Tanzania I Iceland r-1, -400 - , 3.2 3.6 4.0 4.4 4.8 3.2 3.6 4.0 4.4 4.8 5.2 3.2 3.6 4.0 4.4 4.8 Vs (km/s) Vs (km/s) Vs (km/s)

Figure 2.13: Synthetic tests of 1-D S-wave velocity profile inversions. In each panel, the dashed curve denotes the velocity model obtained from individual regional studies. The solid curve denotes the resulting model of the 1-D inversion. The thin, grey curve denotes the starting model in the inversion. The final models are able to capture the velocity structures of the initial models, but the sharp edges or discontinuities in the initial models are often smoothened in the resulting models...... MMONNOWL.

100

150

.; 200 damp = 0.05 - 0. damp 0.1

250 -- damp = 0.2

- damp =0.25

300 - - damp =0.3

350 -

--- ak135 (starting model)

400 - - , I I I I 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 5.2 Vs (km/s)

Figure 2.14: Summary of the resulting models obtained from using different damping parameters in the inversion. The damping parameter controls the magnitude of variation in the velocity profile. In the inversion, the damping parameter is assigned as a dividing factor in the diagonal of the model covariance matrix, thus a smaller value imposes stronger damping effect and results in a final model with less deviations from the starting model. Shear Wavespeed(km/s) 3.5 4 4.5 0 20 40 60 80 100 120 140

CL 160 180 200 220 240 260 280

Figure 2.15: S-wave velocity model at grid point [110.25W, 65.25N] derived from NA. The solid curve shows the posterior mean velocity at each layer, and the shaded area indicates the corresponding standard error. The dashed curve shows the reference model from Fig. 2.12. 1-D Marginals

I I I I A l , , , ,

33 40 43 Moho depth

AV:90-130 km AV:130-170 km

A .A~.!

0 -0.6 0 0.6 AV: 170-220 km AV:220-280 km

Figure 2.16: The 1-D marginal posterior probability density functions of the Moho depth and the S-wave velocity at each layer at the grid point [110.25W, 65.25N]. The horizontal axis indicates the variation range of the Moho depth (km) or the velocity variation range (AV; km/s). The vertical axis shows the normalized probability density. The solid line indicates the posterior mean values of each model parameter. The peak in the probability density distribution indicates the most likely (best data-fitting) parameter value. (a) depth:-10 km depth=30km

66 1A

65.5

3.45

3S5

Longitude(deg) Longitude(deg)

tss

4.6

4,65

4.7S

4,6

4.62

4.64

4.66

4.68

4.7

4.72

4.74

4.76

4.78

4.8

Longitude(deg) Longitude(deg)

Figure 2.17: S-wave velocity variations inferred from NA: (a) 10 km; (b) 30 km; (c) 50 km; (d) 70 km; (e) 100 km; (f) 150 km. Color bars indicate Vs (km/s). The blue dashed lines outline the ultra-depleted layer in the mantle lithosphere inferred from garnet xenocrysts [Griitter et al., 1999]. The green box outlines the UDL inferred from mantle xenoliths [Griffin et al., 1999]. 100 0

1500

200 5 0 100 200 300 Distance(km)

Figure 2.18: The S-wave velocity perturbation across the Lac de Gras region (line A-B in Fig. 2.17(f)). The color bar denotes perturbation percentage relative to the reference velocity model (the 1-D S-wave velocity model in Fig. 2.12). The high velocity structure is observed at -100 km to 200 km depth beneath the Lac de Gras region. Chapter 3

Geophysical detection of relict metasomatism from an Archean (ca 3.5 Ga) subduction zone

Abstract

The origin of Archean cratonic lithosphere is the subject of much debate. Geological and geochemical data are consistent with a plate tectonic origin involving assembly of predominantly oceanic terranes and micro continents into large stable cratons. Deep probing geophysical surveys of Archean cratons provide a means of understanding the processes responsible for the formation of these earliest continents, although the signals are complex and present-day cratons are only the surviving remnants of once larger entities. Here we present a unique geophysical view of structure within the Archean Slave craton, in northwestern Canada, that shows clear evidence for subduction processes frozen into Archean lithosphere. New seismic imaging results from the central Slave are synthesized with a coincident magnetotelluric model, and interpreted using petrological and geochemical constraints from mantle xenoliths. We find the most striking correlation between seismic and electrical structures

tParts of this chapter have been accepted for publication in Science as: Chen, C.-W., S. Rondenay, R. L. Evans and D. B. Snyder, Geophysical detection of relict metasomatism from an Archean (ca 3.5 Ga) subduction zone, Science, in press. ever observed in a continental setting in the form of a coincident seismic discontinuity and electrical conductor at -100 km depth. The magnitude of both anomalies, in conjunction with the occurrence of phlogopite rich xenoliths originating at the same depth, point to a metasomatic origin. We believe that fluids were released from a subducting slab and altered the mantle directly below the base of a pre-cratonic lithosphere. Our model suggests that cratons are formed by subduction underplating and accretion of pre-existing fragments, and that these processes were active as early as 3.5 billion years ago.

3.1 Introduction

Archean cratons form the core of many of Earth's continents. By virtue of their longevity, they offer important clues about plate tectonic processes during early geological times. A question of particular interest is whether subduction is the main mechanism of cratonic assembly and, if so, when this process began to operate. Answers to these questions may be found in the lithosphere of the Archean Slave craton, which is located in the northwestern Canadian Shield (Fig. 3.1) and represents the oldest known fragment of the North American continent [Bowring et al., 1989]. The Slave craton has been studied extensively to reveal processes of cratonic assembly as it is well exposed and has been insulated from plate margin processes for over 2 Ga. In the center of the Slave craton, inclusions of rock fragments from the mantle indicate that the lithospheric mantle is separated into two distinct layers at 140- 150 km depth, with a harzburgitic upper layer exhibiting a very high degree of iron depletion [Griffin et al., 1999; Griitter et al., 1999] and a lherzolitic lower layer that is less depleted in iron [Griffin et al., 1999]. Previous seismic studies identified remnants of subducted oceanic lithosphere [Bostock, 1998; Snyder, 2008; Moorkamp et al., 2007], believed to be linked to the final stages of assembly of the Slave craton during the late Archean and to its stabilization during the Proterozoic. Despite these results, there remains a great deal of uncertainty over the timing and mechanisms of the formation of the Slave craton. For example, it is unknown when its >200 km thick lithospheric root was in place, and if it was constructed solely by subduction accretion [Helmstaedt and Schulze, 1989] or by successive accretion-collision episodes [Jordan, 1978]. Further insight into these questions requires new and integrated datasets. In this study, we address this objective by investigating coincident seismic and electrical profiles across the Slave craton.

3.2 Seismic observations

In this section, we first introduce the seismic data and methods used to image the discontinuities in the lithospheric mantle of the Slave craton. This is followed by a presentation of the imaging results and evaluation of the robustness of the results.

3.2.1 Data

The dataset used in this study was acquired using 23 three-component broadband seismometers deployed across the Slave craton over a 24-month period (July 2004-August 2006). How- ever, due to expected shutdown of power during the Canadian winter, and unexpected failure of the recording device, at the end only six months' worth of data was recovered. Among the 23 seismic stations, 13 of them were part of the POLARIS Slave network. The other 10 stations are part of the MIT instrument pool (Fig. 3.1; Table Al). The POLARIS Slave network was designed primarily as a linear array with a few outlier stations to the west of the array for additional lateral data coverage; these outlier stations are not used in this study. The linear array is oriented perpendicularly to major geochemical boundaries in the mantle [Griitter et al., 1999]. The MIT array was deployed in July 2004, and remained in the field for two years. Each station was equipped with a CMG-3TD broadband seismometer (flat response 50 Hz- 120 s) and a Giiralp recording unit. The MIT array was designed to supplement the existing POLARIS stations so as to render the station spacing suitable for high-resolution seismic imaging. The combined POLARIS-MIT array results in an average station spacing of -20 km.

We recorded 62 teleseismic events with magnitude mb > 5.8 within the epicentral distance range of 30'-100'. These events illuminate the study region from various backazimuths (Fig. 3.1), but most events come from western Pacific, Fiji-Tonga, and Central and South America. Thus, the best backazimuthal coverage is in the NW and the SE quadrants.

3.2.2 Method

We analyze P-to-S (Ps) converted waves in the coda of teleseismic P waves using an approach developed by Bostock [1998], which is similar to the traditional receiver function method [e.g., Langston, 1979], but with some modifications, including the incorporation of the wavefield decomposition and the use of simultaneous deconvolution. In the following, we outline the key steps in the data processing and imaging. First, all three-component [radial (R), transverse (T), and vertical (Z)] seismograms for each event are transformed into upgoing P, SV, and SH wavefields using the inverse free- surface transfer matrix [Kennett, 1991], which is expressed as

P 0 -? I R SV = 2p2 0 p# T (3.1) SH 0 0 Z where a and # are the P- and S-wave velocities at the free surface, p is the horizontal slowness, 2 and q, = - p- and qO = X/-2 _ p-2 are the corresponding vertical slownesses. In a layered, 1-D Earth model, this transformation, in theory, isolates completely the incident P wave from the converted Ps wave at horizontal discontinuities, and suppresses the effect of signal distortion caused by downgoing waves reflected at the free surface. Secondly, a time-domain window is applied to the P component, so as to exclude undesirable signals that are considered not part of the source time function, and that could cause degradation of the subsequent deconvolution process, such as PP. The resultant windowed P component thus represents the approximation of the effective source time function. The next key step is the normalization of source signature for each event. This step is necessary in order to separate the structural impulse response in the subsurface from the extended earthquake source time functions. It also allows for coherent stacking of the converted waveforms and thus enhancing the signal-to-noise ratio. The normalization process requires the construction and application of a deconvolution operator that is able to solve the forward problem:

d(t) = w(t) * r(t), (3.2)

in which the observed signal d(t) is represented by the convolution of the Earth's impulse response r(t) with the combined source time function and instrument response w(t), and where r(t) is the unknown. In the most commonly used method, the frequency-domain deconvolution, an approximate solution of the impulse response, i(w), can be expressed as

[e.g., Berkhout, 1977]

P(w) w *(w) d(w) (3.3) w(W)w*(W) + 6 where the asterisk denotes the complex conjugate, w is angular frequency and 6 is a regularization factor, sometimes termed waterlevel (Clayton and Wiggins, 1976), which controls the trade-off between model variance and data misfit. In Chapter 4, we will discuss and introduce an alternative approach to the deconvolution problem in more detail. Bostock [1998] refined this process by grouping several wavefield-transformed seismograms to estimate a single impulse response (?(t)) in a least-squares simultaneous deconvolution of N seismograms in the frequency-domain as

(t)= F-1 N . 1), (3.4) where dn and wn represent the nt' S-wave component and corresponding windowed P-wave component, respectively, and F- 1 denotes an inverse Fourier transform. Finally, to map the energy of stacked converted phases into mantle "reflectivity" as a function of conversion depth, R(z), a moveout correction is required and is achieved by including the term of phase delay times in eq. (3.4) as

REzN d1[(w)w* (w)exp(WT(, z)) R(z) = F N n(3.5) En wn (W)w* (W) + where T(pn, z) is the delay time of the converted phase originating at an interface at depth z, and pn is the horizontal slowness of the nth P-component seismogram. These stacked traces can be plotted as a function of time or conversion depth, and they form the basis of 1-D receiver function (RF) imaging. In the following, we will show two types of RF profiles. First, for individual stations, the stacks are plotted as a function of epicentral distances, and thus provide insight into the structures underlying the station. Second, for array data, the RF traces are binned according to common conversion points at the depth of interest. The resulting stacks are then plotted side-by-side to form a pseudo-2-D profile, illuminating the structure underlying the array in a continuous manner.

3.2.3 Single station profiles

Figs. 3.2 and 3.3 show the SV- and SH-component RF profiles, as a function of epicentral distance, of two exemplary stations, ACKN and EKTN (Fig. 3.1). Note that the epicentral distance profiles are constructed using only events from the NW quadrant because the dense data coverage in this backazimuthal range provides the most continuous sampling. On the plots of SV-component RF stacks versus epicentral distance (Figs. 3.2a, 3.3a), three main features are apparent for both stations. First, the crust-mantle boundary, or Moho, is observed as a strong positive (downward slow-to-fast) velocity gradient at ~5 s. Second, two free-surface reverberations are observed as positive and negative impulse responses at -16 and -21 s, respectively. Third, we observe a pronounced negative impulse response at -12 s, corresponding to a depth of -100 km. This feature exhibits negative differential slowness with respect to direct P wave, i.e., the apparent arrival time of the phase decreases as the distance increases, which is characteristic of primary converted phases, indicating that it originates from a mantle discontinuity. In contrast, free-surface reverberations display an increase in the apparent arrival time as the distance increases. Further insight into the nature of the Moho and the mantle discontinuity is provided by examining the SH-component profiles (Figs. 3.2b, 3.3b). The lack of coherent signals at -5 s on the SH components indicates that the Moho is a flat velocity discontinuity with minimal or no anisotropy beneath the two stations. The same characteristics persist in all other stations in this study. Moreover, a number of impulse responses are observed in the SH components beneath these two stations. These include a weak negative response and a positive one at -8 s and -18 s, respectively, beneath ACKN, and a positive-negative dipolar response at 12-14 s beneath EKTN. These features have been previously identified and interpreted as caused by anisotropy [Snyder, 2008]. The lack of consistent observation of these features in nearby stations suggests that these are likely localized heterogeneous structures.

3.2.4 Common-conversion-point (CCP) profiles

The examination of RF profiles as a function of epicentral distance reveals heterogeneous layered structures in the lithospheric mantle beneath the stations of interest. To gain a better understanding of the lateral extent of these structures and their possible association with the tectonic history, we take advantage of the data of the densely-spaced POLARIS-MIT array and construct a pseudo-2-D profile using the CCP stack [Dueker and Sheehan, 1997]. The most pronounced and consistent structure observed in the single station profiles is the negative response at -100 km depth. Therefore, we focus our image at this particular depth and to achieve better lateral resolution of this discontinuity. We calculate the Ps conversion points at the depth of 100 km by tracing the converted S wave back into that depth (Fig. 3.4), and then perform geographical binning to group seismograms with conversion points within the same bin. Each bin is 0.10 in length along the latitude. The resulting RF stacks for each bin are then juxtaposed to each other, and therefore form a pseudo-2-D profile projected in an N-S direction (see Fig. 3.1 for projection line). We construct CCP profiles using data from different quadrants. The data from the NW quadrant yield the clearest and most stable RF stacks, and we therefore use the corresponding profile for the following analysis, shown in Fig. 3.5. In this profile, two main features reveal the structure of the underlying lithosphere. First, the Moho appears as a positive (downward slow-to-fast) velocity gradient across the entire profile at depths ranging from 34 to 41 km, marking a southward thickening of the crust. Second, a pronounced negative (downward fast- to-slow) velocity gradient dips southward from 96 km to 124 km depth beneath the center of the craton. We refer to this signal as the central Slave seismic discontinuity. Portions of this seismic discontinuity have been identified previously in our single station profiles and by other investigators [Snyder, 2008; Moorkamp et al., 2007], but here it is seen as a continuous profile. The lack of SH-response by the seismic discontinuity (See Section 3.2.5) suggests that it is not caused by anisotropic parameters [Bostock, 1998]. To estimate the velocity contrast of this discontinuity, we compared its amplitude with that of the Moho. The receiver function signals are caused by seismic impedance (product of density and seismic velocity) contrasts in Earth, but sensitivity tests show that in the context of lithospheric structure they are mostly sensitive to velocity variations [Rychert et al., 2007]. Therefore, their amplitudes effectively represent seismic velocity contrasts. The velocity contrast of the Moho across the Slave craton is determined to be -20% from a seismic refraction experiment [Fernandez Viejo and Clowes, 2003]. Accordingly, the amplitude of the central Slave seismic discontinuity corresponds to a 9-21% velocity contrast. These values corroborate the results from modeling of the converted SV signals, which imply a velocity contrast of ~6-23% [Moorkamp et al., 2007].

3.2.5 The robustness and nature of the central Slave seismic discontinuity

To evaluate the nature and robustness of the central Slave seismic discontinuity, we construct SV and SH receiver function profiles using data from three different backazimuthal ranges (Fig. 3.6): 0-90' (NE), 90-180' (SE), and 270-360' (NW). The southwest quadrant (180- 2700) contains only one event that does not yield a continuous and stable receiver function profile, thus it is excluded from the discussion. The SV-component profiles from all three quadrants indicate the presence of a low velocity discontinuity at -100 km depth beneath the central Slave craton. The amplitude and lateral extent of the discontinuity varies from one quadrant to the other. This may reflect (i) variability in the number and quality of the events - the NW quadrant comprises 36 events, versus 18 and 4 for the SE and NE quadrants, respectively, (ii) rapid lateral variations in the properties of the discontinuity, or (iii) a combination of these two factors. The scale of possible lateral variations can be constrained by the locations of the Ps conversion points of Ps waves that are incident from different quadrants (Fig. 3.4). This exercise suggests that lateral variation must occur over scale-lengths of the order of ~20 km to account for discrepancies between quadrants. The SH-component profiles for the various quadrants (as well as for smaller backazimuthal bins - not shown) do not reveal any clear, coherent signals near 100 km depth beneath the central Slave. This suggests that the discontinuity observed in the SV profiles is not due to seismic anisotropy.

The uncertainty on the depth of the central Slave discontinuity is related mainly to the background velocity model and to the coherence between individual waveforms that are combined into stacks. To evaluate the depth uncertainty due to changes in the background velocity model, we generate depth-migrated receiver functions using models with different crustal and mantle Vp and Vs/Vs (Table 3.1), and determine the corresponding changes in depth of the 100 km discontinuity signal. A realistic range of Vp and Vp/Vs for the crust is determined on the basis of the aforementioned seismic refraction experiment [Fernandez Viejo and Clowes, 2003]. Results from this experiment show that the crust comprises two layers: an upper layer (0-20 km depth) with Vp ranging between 6.35-6.55 km/s, and a lower layer (20-40 km depth) with Vp ranging between 6.7-6.9 km/s. The Vp values in the upper crustal layer correspond to a rock composition of metagabbro and greenschist with Vp/Vs of 1.78 [Holbrook et al., 1992]. For the lower crust, the values of Vp correspond to felsic to mafic granulite with Vp/Vs of 1.78-1.9. For the upper mantle, the seismic velocities are estimated based on properties of lherzolitic and harzburgitic lithologies at pressure- temperature condition of the Slave lithosphere, giving a range in Vp of 7.7-8.3 km/s and a Vp/Vs ratio of 1.77-1.78 [Hacker et al., 2003]. We derive four end-member models based on the estimated values of Vp and Vp/Vs (Table 3.1), and use these models to re-compute the receiver functions for the 8 bins in which the central Slave discontinuity is most clearly detected. The corresponding depth range of the discontinuity (i.e., the minimum of the negative impulse) is shown by a green line in Fig. 3.7, and implies an uncertainty of <10 km on the depth of the discontinuity. To evaluate the depth uncertainty related to waveform coherence, we use the bootstrapping technique [Efron, 1979 and estimate the 95% confidence interval on the depth of the discontinuity (Fig. 3.7). The bootstrap test is conducted on the bins that contain at least 10 converted waves. A population of receiver function waveforms is generated by simultaneously deconvolving groups of traces randomly selected from the original dataset (with replacement, up to the size of the original samples) and repeating the operation 100 times. For those bins that sample fewer than 10 waves, we simply display the individual receiver functions in conjunction with the simultaneously deconvolved waveform. This analysis indicates that variability between waveforms causes uncertainties of 2-10 km on the depth of the discontinuity. This test also yields an estimate of uncertainty on the amplitude of converted signal. From the bins that are amenable to bootstrapping, we find that the amplitude of the signal exhibits an error bar of 0.017 (normalized units) based on the 95% confidence interval, which translates into an uncertainty of 2.5-6% on the magnitude of the S velocity anomaly.

3.3 Seismic vs. magnetotelluric observations

3.3.1 The magnetotelluric observations

The magnetotelluric (MT) technique measures time variations of the magnetic and electric fields at the Earth's surface to infer electrical resistivity structure in the subsurface. Three MT experiments were conducted on the Slave craton during 1996-2000 by a Canada-US team [Jones et al., 2001]. The acquired MT response data were then processed and inverted to infer the electrical resistivity structure of the Slave craton's lithosphere. The resulting electrical image reveals a highly conductive anomaly in the central Slave mantle [Jones et al., 2001] (Fig. 3.5). The anomaly has a conductivity of 0.01-0.03 S/m, which is two orders of magnitude higher than that of the surrounding mantle and therefore prevents penetration of the signal (and imaging) at greater depths. It has a depth ranging from -80 to 120 km with a southward dip, and may be caused by interconnected graphite films deposited along grain boundaries, as interpreted by Jones et al. [2001].

3.3.2 Coincident seismic and electrical anomalies

When displayed simultaneously in vertical cross-section (Fig. 3.5), the seismic discontinuity and the conductive anomaly exhibit a remarkable spatial coincidence. Although the conductive anomaly appears slightly shallower (by ~4-9 km) than the seismic discontinuity, this difference is within reasonable error of the depth estimates associated with each technique - namely 10 km for the seismic profile, as discussed in Section 3.2.5, and 15 km for the MT image [Jones et al., 2001]. In map projection, the regions over which these structures are observed overlap with the lateral extent of the upper-mantle harzburgitic layer [Griffin et al., 1999] (Fig. 3.1). In the next section, we provide a review of possible causes and explanation for both anomalies.

3.4 Causes of the seismic and electrical anomalies

Possible causes for both discontinuities include the existence of local gradients in temperature, composition or water content, and the presence of partial melt. In this section we investigate the effect of each of these factors on the seismic and electrical properties of the upper mantle.

3.4.1 Thermal anomaly

Experimental studies [Jackson et al., 2002] have shown that the thermal dependence of velocity can be described by the following expression:

aln Vs ln Vsu Q- 1(w,T) (E+PV) aT = aTT -F(a) 7 ~ RT2R(3.6)

73 where

Q m o(E±PV) =Ad- T' exp RT (3.7)

ira ira F(a) = - cot - (3.8) 2 2

where Vs is the shear-wave velocity, T is the temperature, Q is the seismic quality factor, and F(a) is attenuation-dependent coefficient in which F(a) --+ 1 as a -* 0. The parameter values are chosen from previous studies [Jackson et al., 2002; Isaak, 1992; Rychert et al., 2005] and are conservative so as to yield the minimum possible temperature change: change in the

unrelaxed shear-wavespeed with respect to temperature, In VsU/&T = -8.6 x 10-5K-1;

activation energy, E=424,000 J/mol; A=730 s-a"mm ; grain size, d=1 mm; frequency (w) dependence of attenuation, a=0.26; grain size sensitivity of attenuation, m=0.26; activation volume, V = 6 x 10- 6m 3/mol; pressure, P=3.16 GPa. The background temperature is set at T=750'C [Kopylova and Caro, 2004] and the central period is To=2 s. If the observed seismic anomaly of 9-21% Vs reduction is to be explained solely by thermal effect, the required temperature increases are therefore calculated to be 1000-2200 C. Lab measurements [Bagdassarov et al., 2007] on the Slave peridotite xenoliths also indicate that a temperature increase of -400'C (assuming the background temperature to be ~750'C) would be needed to explain the electrical anomaly. Such gradients are implausible in the context of a cold, depleted mantle root with a petrologically-inferred thermal gradient of < 6 C/km [Kopylova and Caro, 2004], unless they were caused by a transient heating event such as a sudden injection of kimberlitic magma. However, the last kimberlite eruption occurred -50 million years ago [Griffin et al., 1999; Snyder and Lockhart, 2005], much too long ago to explain the required, current day thermal anomalies.

3.4.2 Compositional anomaly

The main causes of compositional anomalies in cratonic environments are melt depletion, juxtaposition of different mantle terranes, and mineral alteration/deposition. Xenolith studies suggest that the lithosphere of the Slave craton exhibits varying degrees of depletion in iron and other magmaphile elements [Griffin et al., 1999]. However, the largest depletion interface inside the lithosphere, which occurs at -150 km in the central Slave, could account for at most ~1% shear-wavespeed anomaly [Kopylova et al., 2004]. Moreover, the effects of iron depletion on electrical conductivity are negligible (only about one third of an order of magnitude) [Jones et al., 2009]. Therefore, melt depletion alone is not a viable candidate to explain the anomalies.

Juxtaposition of different mantle terranes is associated with possible mechanism of cratonic assembly and modification, such as underplating by flat subduction, plume head subcretion, and foundering of the lithospheric root. However, interfaces between such adjacent terranes (if one omits the effects of metasomatic alteration for the time being) result either in varying degrees of melt depletion, which as seen above is not a viable explanation, or in transitions between mantle peridotites and eclogitized oceanic crust, which are quasi- indistinguishable seismically [Rondenay et al., 2001; Rondenay et al., 2008] and electrically [Heinson and Constable, 1992]. Interfaces between underplated terranes have been invoked to explain anisotropic discontinuities observed in the SW Slave craton [Bostock, 1998]. How- ever, the central Slave discontinuity does not require seismic anisotropy (Fig. 3.6), and therefore an alternative mechanism may be required.

There is probably no single type of mineral deposit or alteration that can simultaneously explain both anomalies. However, the anomalies can be due to different minerals that stem from a unique process of metasomatic alteration. For the seismic anomaly, hydrated minerals such as phlogopite, chlorite and talc are the most likely candidates, because they are stable at P-T conditions corresponding to the location of the anomaly [Frost, 2006; Grove et al., 2006; Pawley and Wood, 1995], and are known to reduce seismic velocity [Hacker et al., 2005; Mainprice et al., 2008]. Strong supporting evidence for this hypothesis comes from analyses of xenoliths from the central Slave that identify a horizon of phlogopite bearing eclogites (-50 vol% phlogopite) at ~100 km depth [Aulbach et al., 2007]. Although phlogopite has been invoked to explain conductive anomalies in the mantle [Boerner et al., 1999], these anomalies have much smaller magnitude than that observed here and there is no experimental data that shows that phlogopite may generate the 0.01-0.03 S/rn anomaly observed in the central Slave [Jones et al., 2001]. Alternatively, a more favored mineral to explain conductive anomalies in the mantle is graphite [Jones et al., 2001; Mareschal et al., 1992]. Very small quantities of graphite deposited as interconnected films along grain boundaries can increase the electrical conductivity by several orders of magnitude [Mareschal et al., 1992]. There are a few mantle xenoliths from the central Slave that contain small concentrations of graphite, although they are not the phlogopite-rich xenoliths [Aulbach et al., 2007]. However, it is possible that a thin lithospheric layer exhibiting high concentration of graphite may not be well-represented in the population of xenoliths entrained to the surface due to its high level of alteration and resulting friability [Aulbach et al., 2007]. Therefore graphite films can be retained as a possible candidate for the electrical anomaly. Mineral deposits can therefore explain the seismic and electrical anomalies, whereby the two types of minerals invoked could be related to a same process of metasomatic alteration by a fluid rich in carbon. This would be consistent with fluid cycle within a subduction zone complex where large quantities of

sediments (source of C) are being subducted [Kerrick and Connolly, 2001].

3.4.3 Water

Water can lower both seismic velocities and electrical resistivity [Karato, 1995; Karato, 1990]. Liquid water is unlikely to remain stable over periods of millions of years in a cold, stable cratonic lithosphere, as it would eventually react with surrounding rocks to form alteration minerals. Therefore its effects are not considered further. Alternatively, water present as hydrogen in the structure of nominally anhydrous minerals is a factor that must be considered. In terms of seismic observations, water primarily reduces seismic velocities through anelastic effects [Karato, 1995] - theoretical calculations suggest that increasing water content by an order of magnitude results in an increase in seismic attenuation by a factor of two. Based on the observed range in values that attenuation can take in the continental lithosphere [Gung and Romanowicz, 2004] and the relationship between attenuation and seismic velocities (considering water as the only cause of attenuation, i.e., omitting additional effects from temperature), it has been argued that water may cause at most 4.3% velocity reduction in the lithosphere [Rychert et al., 2005]. This is not enough to explain the anomaly observed here, but it may very well contribute to it, especially in the framework of the hypothesis that the discontinuity is due to an alteration front caused by metasomatism. With regards to electrical conductivity, the Archean lithosphere is considered to be cold and dry [Hirth et al., 2000], and the Slave's upper mantle temperature is insufficient to provide high enough activation energy to induce hydrogen diffusion that would enhance electrical conductivity to the observed levels.

3.4.4 Partial melt

The last mechanism we investigate as a possible source for both anomalies is a layer of partial melt at 100 km depth in the lithosphere of the central Slave. Theoretical calculations show that a very small quantity of partial melt (<3%) is sufficient to explain both the observed seismic velocity drop [Hammond and Humphreys, 2000] and electrical conductivity increase [Roberts and Tyburczy, 1999]. However, the Slave mantle temperature inferred from steady- state geotherm [Kopylova and Caro, 2004] is much below the minimum solidus temperature under water saturation conditions [Grove et al., 2006]. Moreover, as observed in section 3.4.1, it appears unlikely to sustain a thermal perturbation required to induce partial melting at the depth of observed anomalies.

3.4.5 Summary

In summary, we find that the most probable mechanism that can explain the coincident seismic and electrical anomalies below the central Slave craton is a metasomatic alteration front. Although no single mineral can produce both anomalies, secondary minerals that formed from metasomatic alteration may be responsible. For the seismic anomaly, phlogopite is the most likely candidate, as it is stable at P-T conditions corresponding to the location of the anomaly [Boerner, 1999] and represents up to -50% of the bulk composition of the eclogitic mantle xenoliths that originated ~100 km below the central Slave craton [Aulbach et al., 2007]. This quantity of phlogopite could reduce S-velocities by -17% relative to the host eclogite and surrounding mantle peridotite [Hacker et al., 2005], which is in agreement with the observed seismic discontinuity. Phlogopite also increases electrical conductivity, but experimental data shows that it can contribute at most 1% of the observed conductive anomaly [Guseinov et al., 20051. We attribute the additional conductivity increase to thin, interconnected films of graphite deposited along grain boundaries, the presence of which has been invoked to explain this and other conductivity anomalies in cratonic lithosphere [Jones et al., 2001; Mareschal et al., 1992].

3.5 Implications for the assembly of cratonic lithosphere

We propose that the two minerals, phlogopite and graphite, are related to metasomatic alteration, consistent with dehydration within a subduction zone complex where large quantities of sediments (source of carbon) were subducted [Kerrick and Connolly, 2001]. The existence of a metasomatized layer at 100 km depth beneath the central Slave fits well within the context of subduction related to the assembly of Archean cratons [Snyder, 2008; Helmstaedt and Schulze, 1989; Lee, 2006]. Two possible subduction models can cause the observed metasomatic front and underlying ultra-depleted layer. In one model, the discontinuity is due to the underplating of a subducted oceanic slab (Fig. 3.8a), and marks the interface between the overriding plate and a layer of eclogitized and metasomatized oceanic crust. In this model, the ultra-depleted layer represents harzburgitic oceanic mantle [Griffin et al., 1999]. An alternative scenario of assembly involves an episode of shallow subduction (Fig. 3.8b), in which the mantle wedge becomes stagnant and freezes due to low subduction angle - a process that has been documented in Phanerozoic times, for example in parts of the Andes [Wagner et al., 2006]. In this case, the discontinuity marks the interface between the overriding plate and a layer of ponded melt that has crystallized into eclogite and has been metasomatized at the top of a rapidly cooling mantle wedge. Here, the ultra-depleted layer would correspond to a mantle wedge that has undergone extensive partial melting prior to becoming isolated from mantle circulation. We note that regardless of the exact subduction model, the discontinuity appears to represent the base of an ancient lithosphere that preceded cratonization. The role played by this subduction event in the assembly of the Slave craton may be constrained by the age of minerals found in local xenoliths and by the lateral extent of the observed discontinuity. Lu-Hf dating of clinopyroxenes in the phlogopite-bearing eclogite indicates a minimum age of ~2.7 Ga [Aulbach et al., 2007], which approaches closely the timing of collision between the younger eastern Slave terranes and the older Central Slave Basement Complex (Fig. 3.1) [Davis et al., 2003]. However, this event caused a N-S suture that runs nearly parallel to the seismic line [Davis et al., 2003], and a related subduction should therefore be observed over the entire length of the profile, which is not the case. Moreover, robust Re-Os age constraints on peridotitic diamonds from the ultra-depleted layer suggest that the central Slave's lithospheric mantle down to 150 km was formed and stabilized by 3.5 Ga [Westerlund et al., 2006]. Therefore, the central Slave discontinuity is likely caused by metasomatized mantle from a Paleoarchean subduction zone. The presence of a Paleoarchean cratonic lithosphere in the Slave does not conflict with the occurrence of craton-wide arc volcanism and plutonism during ulterior Neoarchean time [Davis et al., 2003]. It is rather an indicator that the blocks that aggregated in the final stage of cratonic assembly may themselves have been fragments of older, thick proto-cratonic entities. This model is consistent with the limited lateral extent of the discontinuity, which may mark the outline of the broken block (Fig. 3.8), and is supported by the unique geochemical signature and high concentration of diamondiferous kimberlites found in the same region [Griffin et al., 1999]. If subduction has been active since the Paleoarchean, similar or related structures with limited lateral extent should be detected in other parts of the craton and in other cratons worldwide. Indeed, anisotropic seismic discontinuities have been detected below the SW edge of the Slave craton [Bostock, 1998] and the adjacent Wopmay orogen [Mercier et al., 2008]. These have been inferred to mark Proterozoic collision-subduction events associated with the final assembly and stabilization of a large part of the Laurentian continent [Hoffman, 1988]. Away from cratonic edges, similar seismic discontinuities have been detected by re- analysis of data from the dense Kimberley array in the Kaapvaal craton of South Africa [Snyder et al., 2004]. Moreover, a global compilation of long-aperture seismic refraction data indicates the existence of a widespread (albeit not ubiquitous) negative discontinuity in the 90-120 km depth range beneath several Archean cratons [Thybo and Perchuc, 1997]. This negative discontinuity could signal the presence of structures similar to that observed in the central Slave under other cratons worldwide. Our model predicts that subduction-related discontinuities would have limited lateral extent. Indeed, the character of discontinuities (i.e., their spatial extent, depth, dip, and physical properties) depends on the details of each subduction event, including the amount of metasomatism, subduction geometry, and the history of much younger events of accretion and (kimberlite) magmatism, which could have altered or erased evidence of earlier subduction episodes, making their geophysical detection difficult. Given the limited detailed geophysical sampling of cratons and the non-uniform nature of these subduction-related discontinuities, it is thus not surprising that not many of them have been observed to date.

3.6 Conclusions

We have synthesized coincident seismic, magnetotelluric, and petrological evidence to detect the presence of a geophysical/petrological boundary in the lithosphere of the central Slave craton. The spatial overlap between the seismic discontinuity and the conductive anomaly, in conjunction with the occurrence of mantle xenoliths rich in alteration minerals representative of a metasomatic front, supports cratonic assembly by subduction and accretion of lithospheric fragments. Although evidence of cratonic assembly is rarely preserved, these results suggest that plate tectonics was operating as early as Paleoarchean times. This study demonstrates that further studies combining high-resolution seismic, magnetotelluric, petrological and geochemical data are key for mapping and characterizing similar discontinuities in cratons worldwide. 3.7 Table

Model UC UC LC LC UM UM Discontinuity depth (km) VP VP/VS VP VP VS P VP /VS 1 6.35 1.78 6.7 1.9 7.7 1.77 100 2 6.35 1.78 6.7 1.9 7.7 1.78 98 3 6.55 1.78 6.9 1.78 8.3 1.77 108 4 6.55 1.78 6.9 1.78 8.3 1.78 106

Table 3.1: End-member models used to estimate the uncertainty on the discontinuity depth due to the background velocity model. Last column shows values for the bin centered at 64.97'N. The model values are from Fernandez Viejo and Clowes [2003], Hacker et al. 2003], and Holbrook et al. [1992]. UC, upper crust. LC, lower crust. UM, upper mantle.

3.8 Figures ......

-115 -110

Seismicdiscontinuity 0 POLARISstation Electricalconductive 0 MIT station anomaly Outlineof the harzburgiticlayer A-A: Seismicprofile (from mantlexenoliths) B-B':Electrical profile Outlineof the harzturgiticlayer *-(from garnetxenocrysts)

Figure 3.1: Map of the Slave craton. The boundaries of the exposed craton are outlined in red. Inset shows a polar projection of the world centered on the craton (green square), with the 62 earthquakes (red circles) used for the analysis. Crustal topology and geochemical signatures broadly subdivide the Slave craton into two distinct regions [Bleeker et al., 1999]: The older (4.03-2.83 Ga) Central Slave Basement Complex to the west, filled in brown, and isotopically juvenile rocks (2.67-2.6 Ga) to the east, filled in blue. The lateral extent of an ultra-depleted harzburgitic layer of the mantle lithosphere has been inferred from petrological analysis of mantle xenoliths [Griffin et al., 1999] (outlined in green) and geochemical analyses of garnet xenocrysts [Griitter et al., 1999] (region between the two black dashed lines). The seismic stations used in this study are denoted by white circles and squares for POLARIS and MIT stations, respectively, which form a -400 km long linear array with ~20 km average station spacing. The line A-A' marks the location of the seismic profile shown in Fig. 3.5. The lateral extent of the central Slave seismic discontinuity is indicated by blue shading, and that of the conductive anomaly is outlined in red. The line B-B' is the nominal projected location of the MT array [Jones et al., 2001]. ACKNSV component Epicentraldistance [degrees]

4-- Moho Lithospheric discontinuity Moho Id Reverberations

ACKNSH component Epicentraldistance [degrees] 40 50 60 70 80 90 100

- - Anisotropic s - structures?

mug- Uw

Figure 3.2: (a) SV- and (b) SH-component RF profiles of station ACKN for phases arriving from 0-40 s after the direct P wave, plotted as a function of epicentral distance. Red and blue colors effectively represent positive (downward slow-to-fast) and negative (downward fast to slow) seismic velocity contrasts, respectively. Arrows point to observed consistent signals indicating the Moho and its reverberations, as well as possible lithospheric structures. EKTNSV component Epicentral distance [degrees] 100 4- Moho Lithospheric M discontinuity I Moho Reverberations

EKTNSH component Epicentral distance [degrees]

Anisotropic structure?

Figure 3.3: The same as in Fig. 3.2 but for station EKTN.

84 OUR,

-116' 68* -112' -108* 68

66*-

64* 64'

62* 62

-116' -112 -108'

Figure 3.4: Ps conversion points at 100 km depth for events from the NW (magenta), NE (blue), SW (yellow), and SE (green) quadrants. The red points denote the conversion points from NW events for which the low-velocity discontinuity is observed. (a) (b),

ic CL100 ci) 8~

B i- 34 116 400 1350 4600 16000 54000 B

Resistivity (i'm)

160 180 200 220 Horizontal distance (kin)

Figure 3.5: Teleseismic receiver-function (RF) and magnetotelluric (MT) profiles across the Slave craton. (a) Color-coded seismic profile along the line A-A' (Fig. 3.1). Red and blue colors effectively represent positive (downward slow-to-fast) and negative (downward fast to slow) seismic velocity contrasts, respectively. M, Moho; RI and R2, free-surface reverberations. (b) MT-derived resistivity profile [Jones et al., 2001] across the line B-B' shows the central Slave conductive anomaly (red to yellow) at 80-120 depth. The solid black line highlights the base of the conductive anomaly within the region of interest (dashed black box), repeated to scale in RF profiles for comparison. (c) Close-up RF traces for bins in the region of interest. SV component SH component

0 -2 100 100 M-- - -

200 200

300 300 E 400 400 -:

500 0500

600 600

700 700 8000 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 Horizontal distance (kin) Horizontal distance (kn)

01 l 10 0L 200 2 10 LI i W mI * - 300 E 0 - 400 g WAR o i r c 40 ~ 10* via __-u 500 EmE 50 )0 600 -- 6010 - --

700 -.. 70

800 80 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350 Horizontal distance (kn) Horizontal distance (kin)

0 0- 300 100 200 200 -

300 E - r-n - E -~400 700 3004500 . -- -- 500 - - -

600 - - - - 600 - ii 700 700-

Ofnn L -- - 800 0 50 100 150 200 250 300 0 50 100 150 200 250 300 350 Horizontal distance (kn) Horizontal distance (kn)

Figure 3.6: SV- and SH-component receiver function profiles constructed by simultaneous deconvolution of converted P-to-S (Ps) waves binned according to common conversion points along line A-A' (Fig. 3.1), using data from (a) NW; (b) NE; and (c) SE quadrants. Red and blue colors represent positive (downward slow-to-fast) and negative (downward fast to slow) seismic velocity contrasts, respectively. In all three SV components, a coherent positive discontinuity corresponding to the Moho is observed across the entire array at -40 km depth, and a negative discontinuity is observed at -100 km depth below the central Slave (green arrows). No clear coherent signal is observed across the array in the SH profile.

87 bin 64.57 IN(3) bin 64.67 *N(44)

01 - 0 00

0- < -0.1 0 50 100 150 200 250 0 50 100 150 200 250 bin6477 *N(26) bin64.87 N(26)

0 E 0 1 <-0.1 0 50 100 150 200 250 0 50 100 150 200 250 bin64.97 *N(5) bin65.07 IN(17) S0.1

0' . EE 51 E 0 50 100 150 200 250 0 50 100 150 200 250 bin65.17 ON(29) bin65.27 IN (6) 0.1 -01' 0

<--0.10 0 50 100 150 200 250 0 50 100 150 200 250 Depth (kn) Depth (k1)

Figure 3.7: Depth and amplitude uncertainty of the central Slave seismic discontinuity. Each panel shows SV receiver functions (in blue), simultaneously deconvolved and stacked, for the 8 bins at 100 km depth in which the discontinuity is observed. The central latitude of the bins is indicated above each panel, along with the number of waves contributing to each bin (in parentheses). In bins containing more than 10 events, a 95% confidence interval for the resulting receiver functions was obtained by bootstrapping, and is indicated by grey lines. For those bins that sample fewer than 10 waves, we display the individual receiver functions (grey dashed lines) to provide a visual assessment of the inter-trace variability. Note that these receiver functions are individually normalized. The red triangles indicate the depth of the discontinuity estimated from the receiver functions. The green lines denote the range of depths obtained by recalculating the receiver functions for a range of possible background velocity models (Table 3.1). The magenta lines represent the 95% confidence interval on the depth estimate of the central Slave discontinuity based on bootstrapping. Bak

(a) r Crust (a Metasomatic

Uppermost \ boundary ) Mantle Proto-lithosphere Oceanic crust Edogite - Layer1 _ c antle UltraLdepletedLayer- - 1

Layer2 - Lherzolitic Layer -

Present LAB

(b) Crust /Metsomatic( budary Uppermost Mantle Proto-lithosphere -- Eclogite - Layeri1 Ultra-depleted Layer Edlogite oceanic Crust Layer2

Lherzolitic Layer Oencmnl m Present Oceanic LAB

Figure 3.8: Two models of cratonic assembly by subduction that explain the seismic-electrical discontinuity in the central Slave's lithosphere. The tectonic elements of each model are assigned to petrological horizons (labeled axis to the left of both panels) inferred from mantle xenoliths [Griffin et al., 1999; Aulbach et al., 2007]. LAB: Lithosphere-asthenosphere boundary. (a) Accretion of a subducted slab at the base of a pre-cratonic lithosphere. The seismic-electrical discontinuity is associated with the interface between the proto-lithosphere and the altered crust of the subducted slab, while the ultra-depleted layer (UDL) represents the subcrustal lithospheric portion of this slab (13). An additional subcretion event involving subduction [Bostock, 1998] and/or a mantle plume [Griffin et al., 1999] is required in this model to account for the lower layers of the cratonic lithosphere. (b) Low-angle subduction beneath the pre-cratonic lithosphere. The seismic-electrical discontinuity is associated with the interface between the base of the proto-lithosphere and a layer of crystallized and metasomatized partial melt. The UDL is due to extensive melting in the mantle wedge [Davis et al., 2003]. The lower eclogitic and lherzolitic layers represent the crust and mantle of the subducted slab. The inferred subduction event (either (a) or (b)) led to the assembly of a proto-cratonic lithosphere that was at least 150 km thick by 3.5 Ga, based on peridotitic diamonds found in the UDL [Westerlund et al., 2006]. We propose that the central Slave comprises a broken fragment of this proto-craton (see vertical broken lines), which was aggregated to other continental fragments during the Neoarchean assembly of the Slave craton [Davis et al., 2003; Snyder, 2008]. 90 Chapter 4

Array-conditioned deconvolution of multiple component teleseismic recordings

Abstract

We investigate the applicability of an array-conditioned deconvolution technique, developed for analyzing borehole seismic exploration data, to teleseismic receiver functions and data preprocessing steps for scattered wavefield imaging. This multichannel deconvolution technique constructs an approximate inverse filter to the estimated source signature by solving an overdetermined set of deconvolution equations in a least-squares optimal sense, using an array of receivers detecting a common source. We find that this technique improves the efficiency and automation of receiver function calculation and data preprocessing workflow. We apply this technique to synthetic experiments and to teleseismic data recorded in a dense array in northern Canada. Our results show that this optimal deconvolution automatically determines and subsequently attenuates the noise from data, enhancing P-to-S converted phases in seismograms with various noise levels. In this context, the array-conditioned de-

tThis chapter is in review at Geophys. J. Int. as: Chen, C.-W., D. E. Miller, H. A. Djikpesse, J. B. U. Haldorsen, and S. Rondenay, Geophys. J. Int., in review, 2009. convolution presents a new, effective and automatic means for processing large amounts of array data, as it does not require any ad-hoc regularization; the regularization is achieved naturally by using the noise present in the array itself.

4.1 Introduction

A number of methodologies have been developed over the years to analyze converted seismic waves, ranging from single station applications to high-resolution imaging using dense arrays of broadband seismometers. Such developments have been made possible by the increased availability of teleseismic data recorded at dense broadband seismic arrays. We refer the reader to Rondenay [2009] for a comprehensive review of processing steps that have been developed to obtain images of discontinuities in the Earth's subsurface from data consisting of seismograms sampled by dense arrays of recorders. Of particular interest are methods focused on P-to-S (Ps) conversion in the coda of teleseismic P waves, due to its generally high signal-to-noise ratio and lack of contamination from later arriving primary phases. Such signal was first used for direct imaging in landmark studies by Vinnik (1977) and Langston (1979). To increase the signal-to-noise ratio of converted phases, these authors combined records from multiple sources by stacking traces that were source-normalized and time-shifted according to incidence angle. The term receiver function (RF) was introduced by Langston (1979) to describe these normalized records of converted waves and their stacks. A key step in the RF processing chain is the 'source-normalization' which requires the construction and application of a deconvolution operator to remove the extended earthquake source function, replacing it with an approximate impulse. Fig. 4.1 illustrates this step using data from the POLARIS-MIT seismic array in the Slave province, Canada. Fig. 4.1a shows the P and SV component data from a single earthquake recorded at 18 stations, after application of the free-surface transfer matrix method [Kennett, 1991] to partition the three- component records into P-SV-SH wavefields. The effective source function clearly rings for more than a minute, mainly due to reverberation in the crust near the source. Fig. 4.1b shows the same data after application of a deconvolution operator derived by the method of Haldorsen et al. [1994, 1995], as discussed herein. The deconvolved SV data show a clear arrival at -4.8 seconds, resulting from P to SV conversion at the Moho discontinuity. It is the purpose of this paper to discuss this deconvolution method in the context of teleseismic data and to describe its application to data from the POLARIS-MIT array.

4.2 Methodologies

Our study focuses on investigating the effectiveness of the array-conditioned deconvolution, in comparison with conventional frequency-domain deconvolution method, i.e., the waterlevel deconvolution. Thus, in this section, we first provide a review of the waterlevel deconvolution method, and then introduce the array-conditioned deconvolution.

4.2.1 Waterlevel deconvolution

Deconvolution is usually cast as a solution to the forward expression (c.f. Rondenay, 2009, Section 5):

d(t) = w(t) * r(t) + n(t) (4.1) in which the observed signal d(t) is expressed as the convolution of an Earth impulse response r(t) with a source signature w(t). In eq. (4.1), n(t) represents residual energy, typically assumed to be Gaussian random noise with zero-mean. The normalization process to solve for r(t) involves deconvolving w(t) from d(t). For the ideal case, i.e., there is no noise, the source signature and the observed signal are known and not frequency band-limited, this problem may be solved directly by division in the frequency domain. However, the deconvolution procedure is usually ill-posed because of the presence of random noise, frequency bandwidth limitation, and inaccuracies in estimation of source signature. Therefore, the process has to be regularized. This is usually achieved in the frequency domain by prewhitening the amplitude spectrum of the source wavelet, to avoid small amplitudes that would cause numerical instabilities and ringing in the deconvolved signal. Hereafter, we will only be using signals in the frequency domain. For simplicity, we shall keep the same notation for the variables in eq. (4.1).

An approximate solution of the impulse response f is expressed as [e.g., Berkhout, 1977]:

f(w) = w*d(w) (4.2) w(w)w*(w) + 6 where the asterisk denotes the complex conjugate, w is angular frequency and 6 is a regularization factor. The factor, sometimes termed waterlevel [Clayton and Wiggins, 1976], represents the expected noise power. When 6 is zero, eq. (4.2) is a simple spectral division solving the equation d(w) = w(w) r(w). When 6 is large, the denominator in eq. (4.2) is approximately constant and eq. (4.2) becomes a correlation with the estimated source.

The method assumes that the noise spectrum is white and requires either independent knowledge of the noise power or a search for the 'best' parameter that stabilizes the deconvolution process. This is usually done on a trial and error basis, and thus is subjective and labor-intensive. It is desirable to introduce more objective means to estimate the regularization parameter. For example, Bostock [1998] considered a family of recorded traces dm(w) and associated source estimates wm(w) and proposed choosing 6 by minimizing the generalized cross-validation function GCV(6) shown as

MLZh [d, (wi) - Wm,(Wi) T(Wi)]12 GCV(6) =M E 1 (4.3) [ML -E'_Xo)] where

X(w) = rnzziWrn(W)Wn(W) (4.4) E -1 Wm(W)W*(W) + 6 with M denoting the number of traces, and L is the number of frequencies represented in the discrete Fourier transform. This process does not require any assumption concerning the noise level in the data, but it still assumes a white noise spectrum and requires an iterative grid search to obtain the value for 6 (within a given range) that results in the minimal GCV. 4.2.2 Array-conditioned deconvolution

Haldorsen et al. [1994, 1995] described a method for exploiting the redundancy in seismic array data to obtain an optimized deconvolution filter by using the data to estimate both the source and noise spectra without assuming that either is white. That method may be summarized as follows.

Suppose we are given data recorded at an array of receivers and time-shifted and normalized such that each observed trace dm(t) can be assumed to contain a common source signature w(t), superposed with a variable 'noise' nm. That is, we are given a subscripted array of equations, like eq. (4.1):

dm(t) = w(t) + n,,(t) (4.5)

Here r(t) from eq. (4.1) is assumed to be an impulse. Thus, all aligned signals contributing to the source estimation are assumed to be part of the source signature. Additional copies shifted and misaligned (e.g., multipath signal arriving obliquely across the array) are formally part of the 'noise', but will be preserved and spiked insofar as they carry the same signature as the aligned signal. Similarly, the filter derived from the aligned P data can be applied to SV data to compress and enhance the converted signal carrying the same source signature, yielding a compressed arrival with the delay relative to the aligned signal preserved by the deconvolution operator.

In the frequency domain, this data model is written as a set of equations:

dm(w) = w(w) + nm(w). (4.6)

A deconvolution filter W can be determined, independently for each w, as the solution to the set of eq. (4.6) augmented by the equations

W(w)dm(w) = 1. (4.7) These equations have the least-squares solution

W(w) = (W) (4.8) Er (w)' where the caret denotes estimate, and ET(w) is the average total energy of the raw traces:

M ET(w) = M 1 dm(w)| 2. (4.9) m=1

Substituting from eq. (4.6), eq. (4.8) can be rewritten as

W(w) =(W) (4.10) jz(w)j2 + EN(W) where

M EN(w) m - w (w) (4.11) m=1

This agrees with eq. (4.2) when EN(w) is a constant, independent of w, and thus represents a data-adaptive solution to the filter regularization problem, which is applicable in a wider context than is the waterlevel deconvolution.

The properties of this optimum filter are discussed in detail in Haldorsen et al. [1994]. For example, one can rearrange eq. (4.8) to give

W(w) = D(w), (4.12) where the frequency-domain semblance D(w) is given by

D(w) () . (4.13) E (u)

96 The optimum filter in eq. (4.12) is thus recognized as a spectral division filter, multiplied by the semblance, which acts as a data adaptive, band-limiting filter attenuating frequencies where the signal-to-noise ratio is small. In the original discussion, the source estimate and the filter construction were derived together, assuming that all the data from a single recorded component were used in con- structing both the numerator and the denominator of the filter (eq. (4.8)). Subsequent experience has shown that these two aspects of the filter construction can be uncoupled and that the traces used to estimate 7i may be distinct from those used in estimating ET. Moreover, the filter itself may be applied to traces that are distinct from the design traces. In particular, when, as in the case of teleseimic data, it may be reasonably assumed that a complicated packet of energy is converted from P to S somewhere near the receiver array, the P arrivals can be aligned and used to estimate the signature while the complete ensemble of component data is used in estimating the total energy. Note, however, that stability is only guaranteed if the source estimation traces are included in the estimate for total energy. In the next section, we carry out synthetic experiments to evaluate the performance of the array-conditioned deconvolution and to compare the results with those using waterlevel deconvolution.

4.3 Synthetic experiments

We construct the synthetic waveforms by using forward-modeled Earth impulse responses, as well as observed seismograms from the 16 August 2005 earthquake (mb=6.5) in Japan, recorded at 18 stations of the POLARIS-MIT array in the Slave province, Canada. We perform deconvolution on this synthetic dataset with the addition of various levels of noise. The procedure of the synthetic waveform construction is as follows: (1) We compute the synthetic P and SV impulse responses using Zoeppritz reflection and transmission coefficients [e.g., Aki and Richards, 2002] calculated for a simple two-layer velocity model and a single horizontal slowness representative of the field data. Fig. 4.2a shows the result of this computation. The P component has the direct P wave (P) and the first order multiples that end with P (PPP,PSP, SPP, $$#). The S component has the converted S wave (S) and the first order multiples that end with S (PPS, PSS, SPS, $$$) Note that each P arrival has a corresponding S arrival obtained by replacing the last P segment with an S segment, hence the relative time delay is the same in all cases. (2) We align the P-component seismograms of the Japan event and derive a 'synthetic' source signature through diversity stack [Embree, 1968] of the aligned seismograms. This synthetic source signature thus represents the noise-free common source signal (Fig. 4.2b). (3) We convolve the synthetic source signal with the synthetic P and SV impulse responses to yield the noise-free synthetic data (Fig. 4.2c). (4) For each trace, we extract 300-second long data before the P arrival from the P- and SV-component seismograms recorded by the POLARIS-MIT array, to be representative of background noise. We also subtract the synthetic source signal from the respective observed P-component seismogram, and the residuals obtained are representative of additional inco- herent noise between traces. We combine these two types of noise, randomly shift them in the time domain, and add a scaling factor A for controling the amplitude, before adding them to the noise-free synthetic data. As such, the complete synthetic data model for the P-component (d,(t)) and SV-component (d,,(t)) can be described as, respectively,

dp(t) tb (t) * gp(t) + AN,(t); (4.14)

and

dsv(t) = f(t) * gsv(t) + ANsv(t ), (4.15) where gp(t) and gev(t) are the synthetic P and SV impulse responses, and N,(t) and Nsv(t) are the total (combined and shifted) noise in P and SV components. By changing the scaling factor A, we are able to generate synthetic data with various noise levels so as to test the effectiveness of the deconvolution methods.

Fig. 4.3 summarizes the results of the synthetic experiments. Fig. 4.3a shows the synthetic array data (P and SV components) with noise level A=1. Fig. 4.3b and c show the deconvolution results using the waterlevel method with the GCV-derived 6 parameter and with waterlevel of 1% of the maximum amplitude of the source signature estimate, respectively. Fig. 4.3d shows the result using the array deconvolution. This synthetic test allows us to make the following observations. First, the GCV yields trace-dependent 6 values that are equivalent to 0.001 to 0.01 percent of the maximum amplitude of the source estimate. Second, while the waterlevel method in general recovers the impulse response in most SV traces, it fails to resolve traces that are anomalously noisy, for instance, traces 3 and 17. Furthermore, as the waterlevel factor increases, the deconvolved signal broadens and loses resolution. This is expected because using a higher waterlevel amounts to prewhitening more high frequency signals. In a sense, it becomes a low-pass filter, removing high frequency content in the data. Conventionally, this process of iterating over a number of waterlevel factors is conducted and visual inspection is required until a 'best' waterlevel is determined. On the other hand, the array deconvolution (Fig. 4.3d) does not require any iterative process or human intervention, and stabilizes noisy traces while better resolving the impulse response consistently across the array. Here, ET(w) is calculated using P-component data.

Note that, in the deconvolution process, (v(t)*gp(t) becomes the effective source signature, and that relative amplitudes in the deconvolved SV data are slightly altered from those of g8 (t). This is an issue for any deconvolution process. The consistency achieved by using a single deconvolution operator for all receivers should enable further analysis beyond the scope of this paper.

Similar results are observed when we increase the noise in the synthetic data. The waterlevel deconvolution becomes unstable, i.e., the deconvolved traces are more ringing, whereas the array deconvolution still achieves similar resolution.

One way to evaluate the performance of the deconvolution filters is to measure the variance between the deconvolved signals across the array. We calculate the variance by summing the square of the difference between each trace and the mean trace. The corresponding variance of each deconvolved data section is shown as the number in the parentheses above each panel in Fig. 4.3. The array deconvolution yields a much better, i.e., smaller, variance than those from the other two approaches. For waterlevel deconvolution, we note that there appears to be a trade-off between variance and broadening of the deconvolved signal; larger waterlevel results in smaller variance but less sharp impulse. In contrast, array-coditioned deconvolution always achieves small variance and sharper impulse. Fig. 4.4 shows the comparison of the amplitude spectrum of deconvolved signals of trace 3 (Fig. 4.3), from the three approaches. The 'raw' synthetic data is dominated by low frequency noise, and the array deconvolution, compared with the other two approaches, achieves a better resolution of the impulse without sacrificing much high frequency content. In this section, we have demonstrated the effectiveness of the array-conditioned deconvolution, especially for noisy data. In the following section, we will apply this deconvolution to a field dataset of the Slave province. In this example, we focus our demonstration on the P- and SV-component seismograms, but note that the method is readily applicable to SH components as well.

4.4 Application to the Slave craton array data

We use seismic array data recorded in the Slave province, an Archean craton which is located in the northwestern Canadian Shield. The Slave craton has been the subject of intensive geophysical and petrological studies due to its longevity and the presence of abundant diamondiferous kimberlites. The POLARIS-MIT seismic array (Fig. 4.5) in the Slave craton consists of 30 seismic stations, each equipped with a three-component broadband seismometer. A previous receiver-function study [Chen et al., 2008] identified a distinct crust-mantle boundary, or Moho, at ~4.8 s across the array, using waterlevel deconvolution and common-

conversion-depth stacks of high quality data from 62 teleseismic events with magnitude mb 5.8 recorded during 2004-2006. Now, using the new array-conditioned deconvolution method, we are able to analyze data from 135 events with magnitude mb > 5.5 (Fig. 4.5) during the same recording period. We use the event locations provided by the USGS PDE catalog, and rotate the horizontal-component data to radial and transverse components (vertical component remains the same). We subsequently partition the components into P, SV, and SH wavefields by the free surface transfer matrix [Kennett, 1991]. After wavefield partition,

100 we align the data by the predicted arrival times calculated in a 1-D global reference model

(e.g., iasp9l, Kennett et al. 1991). The source signature is estimated from the P-component by diversity stack [Embree, 1968], and the noise energy is calculated from the P-component data. The deconvolution is then performed to yield deconvolved P and S signals. We observe that the deconvolved P impulses across the array often show time differences between each other, indicating inaccurate original reference alignment. Therefore, in practice, the deconvolved P impulses are iteratively realigned by adjusting their time lags, and a subsequent deconvolution is performed to yield the final results.

Fig. 4.6 shows the raw data of four example earthquakes. These raw data show different characteristics of the coherently aligned signals in the P components, marking the various earthquake source signatures, as well as different patterns and amplitudes of the background noise in the SV components. Fig. 4.7 shows their deconvolved results from array deconvolution, compared with those from waterlevel deconvolution. The results of earthquake data are consistent with those of synthetic tests. Both deconvolution methods result in delta-function-like and well-aligned P signals; however, the array-deconvolved ones appear sharper, indicating the effectiveness of the array deconvolution in collapsing the signal into a spike. On the SV components, coherent signals at -4.8 s can be observed in all data sections, representing the conversion at the Moho. However, the array-deconvolved data appear more stable and consistent throughout, while the corresponding waterlevel-deconvolved data are less so. In addition, a number of differences are worth noting. First, the array-deconvolved traces contain more high frequency energy than do the waterlevel-deconvolved ones. Second, there are traces that cannot be well resolved by waterlevel deconvolution and that result in anomalously low frequency signal (e.g., in the Costa Rica event, SV traces 1 and 8; in the Tonga event, traces 3, 6, and 8). In contrast, array deconvolution in general achieves more stability. We also calculate the variance, as defined in the synthetic tests, of the deconvolved data (shown as the number in the parentheses above each panel). In these four examples, the array-deconvolved data all have much smaller variances (at least one order of magnitude smaller) than those of the waterlevel-deconvolved ones. This shows the advantage of array deconvolution in extracting coherent signals across array while attenuating noise. An addi-

101 tional advantage can be noted by examining the Tonga event (Fig. 4.7d). This event has a

magnitude mb = 6.3, but the noisy SV components with anomalous low frequency patches has prevented it from being used in the previous receiver function analysis. Using the array deconvolution, however, we are able to attain more stable and thus usable signals from this event.

The processing procedure is implemented for the whole dataset of 135 events. In Fig. 4.8, we show the deconvolved SV traces as a function of backazimuth at five receivers. We observe that, in addition to coherent signals corresponding to the Moho, there appears to

be various coherent signals at different times between the surface (t = Os) and the Moho

(t = 4.8s) from receiver to receiver. These variations suggest the presence of local crustal heterogeneities beneath each receiver, and were not observed before when only limited high quality seismic records were utilized. We also plot the deconvolved data as a function of earthquake magnitude. An example using data from station ACKN is shown in Fig. 4.9. We observe that the Moho signal appears consistently visible in the entire magnitude range, and does not degrade at smaller magnitudes (5.5 < mb < 5.8). This promising result indicates that the array deconvolution can be readily applied to earthquakes with smaller magnitudes. Further analysis of this application is the topic of a separate paper. In closing, we note that, traditionally, the deconvolution has been achieved in an iterative manner, whether it is to find a 'best' regularization parameter in the frequency-domain deconvolution, or to minimize the difference between observed and modeled data in the time-domain deconvolution (e.g., Gurrola et al., 1995; Ligorria and Ammon, 1999). In this context, the array-conditioned deconvolution presents a new, effective and automatic means for processing large amounts of array data, as it does not require any ad-hoc regularization; the regularization is achieved naturally by using the noise present in the array itself.

4.5 Conclusions

The application of the array-conditioned deconvolution improves the efficiency and automation of the deconvolution process that is an essential step in receiver function analysis and in

102 data preprocessing for imaging of scattered waves. Synthetic experiments demonstrate the effectiveness of the deconvolution technique, especially for noisy data. Application of this technique to a teleseismic dataset from the Slave craton yields a deconvolved data section that clearly identifies the Ps conversion at the Moho, and suggests the presence of local crustal heterogeneities beneath each receiver. The performance of the array deconvolution with noisy data promises the potential of exploiting earthquakes with smaller magnitudes, which would increase the number of usable sources, thus providing more comprehensive azimuthal coverage than was possible before.

103 4.6 Figures

(a) (b) Event:20052280246 Event:20052280246

300 Time (sec) Time (sec) Figure 4.1: (a) The P- and SV-component data of a Japan (mb=6.5, 36-km deep) earthquake. (b) The deconvolved P- and SV-component data of (a).

104 0.2'

-0.6-

-150 -100 -50 0 50 100 150 Time(se)

(C)

-150 1 50 0 50 100 150 Time(sec)

Figure 4.2: (a) The synthetic P- and SV-component impulse responses for an incident P wave of ray parameter p=0.06 s/km, sampling an isotropic two-layer model. The model cosists of a 40 km-thick horizontal layer (ao=6.6 km/s, #0=3.7 km/s, po= 26 0 0 kg/m 3 ) over 3 5 00 3 a half space (a 1=8.1 km/s, /1=4.5 km/s, po= kg/m ). (b) The source signature estimate used to construct the synthetic array data. (c) The noise-free synthetic data constructed from convolving (a) with (b).

105 (a) Synthetics A = 1

-5 0 5 10Time ( 15 20.0) 25 30 (c) Waterlevel 6 = 1 % (0.164) (d) Array decon (0.0003)

-10 -5 0 5 10 15 20 25 30 35 -10 -5 0 5 10 15 20 25 30 35 Time(se) Time(sec) Figure 4.3: Summary of the synthetic experiments. (a) The synthetic array data of A=1. The deconvolved data section using (b) waterlevel deconvolution with GCV-derived 6; (c) waterlevel deconvolution with the factor of 1%; and (d) array-conditioned deconvolution. The number in the parentheses indicates the corresponding variance.

106 Waterlevel

GCV

E 2.5 Array decon

Raw

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Frequency (Hz)

Figure 4.4: Comparison of the amplitude spectra of the deconvolved SV signals of trace 3, along with that of the 'raw' synthetic trace.

107 (b)

(a) 68 - 68

Figure 4.5: (a) The earthquake event distribution projected with the Slave craton in the center (yellow diamond). The red circles denote events used in the previous receiver function study [Chen et al., 2008]. The white circles denote the additional events that are analyzed by the array deconvolution. The green circles denote the four exemplary events whose data are shown in Fig. 4.6. The combined dataset includes a total of 135 events. (b) Simplified geological map of the Slave craton (outlined in red). The yellow shaded area is the central Slave basement complex [CSBC; Bleeker et al., 1999], which is the oldest portion (2.6-4 Ga) of the craton. The blue shaded area denotes the eastern Slave craton where is covered by juvenile crust. The seismic stations used in this study are denoted in squares (MIT stations) and circles (POLARIS stations). The five stations denoted in blue are those whose data are shown in Fig. 4.8. From south to north, these stations are BOXN, LGSN, LDGN, EKTN, and ACKN.

108 (a) Honshu, Japan (mb=6.5; d=36 km) (b) Crete, Greece (mb=5.9; d=25 km) Event:20052280246 Event:20040770521

Time (sec) (c) Costa Rica (mb=5.8; d=9 km) (d) Tonga Islands (mb=6.3; d=130 km) Event:20041810701 Event:20040251143

300 300 Time(sec) Time (sec) Figure 4.6: The raw data (P and SV components) of four exemplary earthquakes from (a) Honshu, Japan; (b) Crete, Greece; (c) Costa Rica; and (d) Tonga Islands. Note the traces are individually normalized. The magnitude (mb) and depth of each event are also indicated.

109 (a) Japan (0.0004) 6 = 0.1 % (0.032)

(b) Crete, Greece (0. 0023) 6 1 % (0. 013)

8w -0Q702 - -52

-'c -s t s 1 15 8) -6 0 10 15 2) (c) Costa Rica (0.0018) 6-=0.1 % (0.033)

(d) Tonga Islands (0.0025) 6 = 0.01 % (0.129)

Figure 4.7: The deconvolved data of the four earthquakes shown in Fig. 4.6. The left column shows the results from array-conditioned deconvolution. The right column shows the corresponding results from waterlevel deconvolution, denoted with the waterlevel factor used. The number in the parentheses indicates the corresponding variance.

110 (a) (b) Deoolved SVo receiver:6: BOXN DeonolvedSV for receiver: 9 : LGSN

100

60--

20 ------2-

0 0 -5 0 5 10 15 20 -

80 0

400

1~10

200

-5 0 5 10 15 20 (S~OO (-) 0 -- (e)

100 0

2 -5 0 5P 10 15 80

20 10 15 -5 0 5 0 0 10 10 (Sec) Backazim h (*) Figure 4.8: The deconvolved SV data sections of five receivers. (a) BOXN; (b) LGSN; (c) LDGN; (d) EKTN; (e) ACKN. (f) The representative backazimuthal distribution of the teleseismic events recorded at this array. A majority of earthquakes are located at the western Pacific subduction zones (around 300').

111 (a) (b) DeconvolvedSV for receiver:15 : ACKN Magnitudedistribution for eventsrecorded at receiver:15 ACKN 120

100 .

80 ---...... -. ... .-.-.-.-.--- - .--- -. - -. ...- .-..--.-- --

E 60 --.. ..- -....-. .. .. -. ... -...-. -......

40 --.. - .-. - -- ...... ------......

20 Iw

5 10 15 20 5.4 5.6 5.8 6 6.2 6.4 6.6 6.8 7 7.2 Time (sec) Magnitudein mb Figure 4.9: (a) The deconvolved SV data section of station ACKN plotted as a function of earthquake magnitude. (b) The distribution of traces according to magnitude.

112 Chapter 5

Conclusions

In this dissertation, I used a number of seismological techniques to investigate the lithospheric structure of the Slave craton and used the results to infer the mechanisms that led to its assembly. The results from surface wave tomography provide new constraints on the thickness of the lithosphere, its lithology and its mineral/structural fabric. The receiver- function analysis identifies a seismic discontinuity at -100km depth. This discontinuity is interpreted, in conjunction with magnetotelluric, petrological and geochemical results, as the remnant of a subduction front that was active during the Archean (< 3.5 Ga) and led to the assembly of the central Slave craton. In parallel to these applied seismic studies, I implemented an exploration-based deconvolution technique that improves the efficiency of receiver function calculations. The key findings and contributions of this dissertation are summarized as follows. Chapter 2 presents the first surface wave tomography analysis of the Slave province to be done at craton-scale. The study outlines lateral variations of Rayleigh wave phase velocities that can be associated with surface geological expressions. The one-dimensional shear-wave velocity profile reveals the existence of a lithospheric lid with an average velocity that is 4% higher than the global average. This reflects a particularly depleted and unperturbed cratonic lithosphere, which corroborates petrological and geochemical constraints. Further- more, the depth dependence of azimuthal anisotropy reveals distinct domains throughout the lithospheric column, reflecting the influences of various tectonic processes on the evolution

113 of the cratonic lithosphere. Finally, a laterally-confined high shear-wave velocity region is observed below ~100 km depth in the central Slave craton. This region is spatially coincident with both the geochemically-defined ultra-depleted mantle layer and the diamondiferous kimberlite cluster. These results point to a distinct origin of the lithosphere underlying the central Slave craton.

In Chapter 3, I produce a high-resolution receiver-function image across the Slave craton that resolves fine structures in the lithosphere. A prominent seismic discontinuity is observed at ~100 km depth below the central Slave, spatially coincident with an electrical conductive layer observed in magnetotelluric models. Analyses of mantle xenoliths collected in the central Slave identify a layer of mineral alteration at the same depth as the seismic/electrical discontinuity and place an age constraint of >3.5 Ga on the emplacement of the underlying lithospheric mantle. The synthesis of these geophysical and geological observations suggests that the -100 km boundary represents a metasomatic front associated with an Archean subduction event. This finding suggests that plate tectonic processes, i.e., subduction, were operating as early as Paleoarchean times.

The finite lateral extent of the seismic/electrical discontinuity further coincides with the region of high shear-wave velocity indentified in the central Slave by surface wave tomography. This reinforces the hypothesis proposed in Chapter 3 that this region represents the broken block of a thick (>150 km) continent with ultra-depleted roots that was formed by subduction ~3.5 billion years ago and was later broken apart.

Chapter 4 presents an array-conditioned deconvolution technique that constructs an optimal deconvolution filter based on the estimate of a common source signature, and is naturally regularized using the noise present in the data itself. Without any ad-hoc choice of regularization parameters, this technique proves especially suitable for analyzing large amounts of array data, such as those from the USArray.

This dissertation demonstrates that multi-disciplinary investigations using geophysical, geochemical, and petrological analyses are key to better constraining the origin of the Archean cratonic lithosphere. Pursuing such integrated approaches in combination with the development of new effective data processing technique will further enable us to char-

114 acterize the interior structure of the whole planet, from the ancient stable cratons to active subduction zones, and to better understand the tectonic processes that shape the Earth.

115 116 Appendix A

Tables

117 Station name Latitude Longitude Elevation Data used in Data used in (ON) (OE) (m) SW RE ACKN 64.991 -110.870 469 _V V BOXN 63.852 -109.717 436 V CAMN 63.732 -110.899 420 CARNa 65.868 -111.783 513 V COKN a 63.269 -108.728 366 _V COWN 65.268 -111.186 513 DSMN 63.179 -113.900 307 _V DVKNb 64.509 -110.309 420 V/ V EKTN 64.698 -110.609 468 EXLN a 64.857 -110.827 450 FINNS 65.558 -111.430 520 GBLN 64.199 -112.993 392 GLWN 64.725 -109.330 449 IHLN 63.305 -110.891 417 ILKN 64.224 -115.130 266 KNDN 63.419 -109.201 421 V KNSN a 63.357 -109.001 418 V/ V LDGN 64.578 -110.522 416 V LGSNb 64.333 -110.130 440 V LUPN 65.738 -111.256 505 V MCKN 64.198 -110.213 431 V, MGTN 63.685 -109.591 411 V A/ MLON 63.969 -109.895 418 V/ MRDNa 63.568 -109.543 434 V NODN 63.960 -110.960 435 NORMa 64.779 -110.786 468 V NUNN a 66.149 -112.151 529 ROCN a 65.991 -111.750 512 V V SNPN 63.517 -110.907 458 TACNa 66.286 -112.201 568 V YMBN 64.874 -111.532 439 V YNENb 65.088 -111.050 463 YKW1 62.482 -114.484 171 YKW2 62.433 -114.603 144 YKW3 62.561 -114.610 170 YKW4 62.492 -114.743 161

Table A.1: The seismic stations used in this thesis. aThe MIT stations. Each was equipped with a CMG-3TD seismometer. bThe POLARIS stations that were using CMG-40T seismometers. The remaining stations are POLARIS stations with CMG-3ESP seismometers. Also listed are four broadband stations of the Yellowknife array (YKW1-4). SW, surface wave tomography. RF, receiver-function analysis.

118 611

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Aki, K., and P. G. Richards, Quantitative Seismology, 2nd Ed., University Science Books, Sausalito, 704 pp., 2002.

Alsina, D., R. Snieder, and V. Maupin, A test of the great circle approximation in the analysis of surface waves, Geophys. Res. Lett., 20, 915-918, 1993.

Anderson, D. L., The deep structure of continents, J. Geophys. Res., 84, 7,555-7,560, 1979.

Artemieva, I. M., and W. D. Mooney, Thermal thickness and evolution of Precambrian lithosphere: A global study, J. Geophys. Res., 106, 16,387-16,414, 2001.

Ashwal, L. D., and K. Burke, African lithospheric structure, volcanism, and topography, Earth Planet. Sci. Lett., 96, 8-14, 1989.

Aulbach, S., N. J. Pearson, S. Y. O'Reilly, and B. J. Doyle, Origins of xenolithic eclogite and pyroxenites from the central Slave craton, Canada, J. Petrol., 48, 1843-1873, 2007.

Bagdassarov, N. S., M. G. Kopylova, and S. Eichert, Laboratory derived constraints on electrical conductivity beneath Slave craton, Phys. Earth Planet. Inter., 161, 126-133, 2007.

Bank, C.-G., M. G. Bostock, R. M. Ellis, and J. F. Cassidy, A reconnaissance teleseismic study of the upper mantle and transition zone beneath the Archean Slave craton in NW Canada, Tectonophys., 319, 151-166, 2000.

Beghein, C., J. Resovsky, and J. Trampert, P and S tomography using normal mode and surface wave data with a Neighborhood Algorithm, Geophys. J. Int., 149, 646-658, 2002.

Berkhout, A. J., Least-squares inverse filtering and wavelet deconvolution, Geophysics, 42, 1369-1383, 1977.

Bleeker, W., L. Ketchum, V. Jackson, and M. Villeneuve, The central Slave basement complex, part I, its structural topology and autochthonous cover, Can. J. Earth Sci., 36, 1083-1109, 1999.

Bleeker, W., Evolution of the Slave craton and the search for supercratons, Geol. Assoc. Can., Abstract, 26, 14, 2001.

123 Bokelmann, G. H. R., and P. G. Silver, Shear stress at the base of shield lithosphere, Geophys. Res. Lett., 29, 2091, doi:10.1029/2002GL015925, 2002.

Boerner, D. E., et al., Electrical conductivity in the Precambrian lithosphere of western Canada, Science, 283, 668-670, 1999.

Bostock, M. G., Mantle stratigraphy and evolution of the Slave province, J. Geophys. Res., 103, 21,183-21,200, 1998.

Bowring, S. A., and J. P. Grotzinger, Implications of new chronostratigraphy for tectonic evolution of Wopmay orogen, Northwestern Canadian Shield, Am. J. Sci., 292, 1-20, 1992.

Bowring, S. A., I. S. Williams, and W. Compston, 3.96 Ga gneisses from the Slave province, Northwest Territories, Canada, Geology, 17, 971- 975, 1989.

Bowring, S. A., T. B. Housh, and C. E. Isachsen, The Acasta gneisses: remnant of Earth's early crust, in Origin of the Earth, H. E. Newsom and J. H. Jones, eds., Oxford Univ. Press, New York, 319-343, 1990.

Bruneton, M., et al., Complex lithospheric structure under the central Baltic Shield from surface wave tomography, J. Geophys. Res., 109, B10303, doi:10.1029/2003JB002947, 2004.

Chen, C.-W., S. Rondenay, R. L. Evans, and D. B. Snyder, Lithospheric discontinuity in the central Slave craton: A new perspective from seismological, electrical, and petrological constraints, EOS Trans. AGU, 89(53), Fall Meet. Suppl., Abstract U43B-0067, 2008.

Clayton, R. W., and R. A. Wiggins, Source shape estimation and deconvolution of teleseismic bodywaves, Geophys. J. R. astr. Soc., 47, 151-177, 1976.

Clowes, R. M., P. T. C. Hammer, G. Fernandez-Viejo, and J. K. Welford, Lithospheric structure in northwestern Canada from Lithoprobe seismic refraction and related studies: a synthesis, Can. J. Earth. Sci., 42, 1277-1296, 2005.

Conrad, C. P., and C. Lithgow-Bertelloni, Influence of continental roots and asthenosphere on plate-mantle coupling, Geophys. Res. Lett., 33, L05312, doi:10.1029/2005GL025621, 2006.

Cook, F. A., and P. Erdmer, An 1800 km cross section of the lithosphere through the northwestern North American plate: lessons from 4.0 billion years of Earth's history, Can. J. Earth Sci., 42, 1295-1311, 2005.

Cook, F. A., A. J. van der Velden, K. W. Hall, and B. J. Roberts, Frozen subduction in Canada's Northwest Territories: Lithoprobe deep lithospheric reflection profiling of the western Canadian Shield, Tectonics, 18, 1-24, 1999.

Davis, W. J., A. G. Jones. W. Bleeker, and H. Griitter, Lithosphere development in the Slave craton: A linked crustal and mantle perspective, Lithos, 71, 575-589, 2003.

124 Debayle, E., and B. L. N. Kennett, The Australian continental upper mantle: Structure and deformation inferred from surface waves, J. Geophys. Res., 105, 25,453-25,450, 2000.

Debayle, E., B. Kennett, and K. Priestley, Global azimuthal seismic anisotropy and the unique plate-motion deformation of Australia, Nature, 433, 509-512, 2005.

Dueker, K. G., and A. F. Sheehan, Mantle discontinuity structure from midpoint stacks of converted P to S waves across the Yellowstone hotspot track, J. Geophys. Res., 102, 8313-8327, 1997.

Eaton, D. W., A. G. Jones, and I. J. Ferguson, Lithospheric anisotropy structure inferred from collocated teleseismic and magnetotelluric observations: Great Slave Lake shear zone, northern Canada, Geophys. Res. Lett., 31, L19614, doi: 10.1029/2004GL020939, 2004.

Efron, B., Bootstrap methods: Another look at the Jacknife, Annals Statistics, 7, 1-26, 1979.

Embree, P., Diversity seismic record stacking method and system, U.S. Patent, 3,398,396, 1968.

Fernandez Viejo, G., and R. M. Clowes, Lithospheric structure beneath the Archaean Slave Province and Proterozoic Wopmay orogen, northwestern Canada, from a Lithoprobe refraction/wide-angle reflection survey, Geophs. J. Int., 153, 1-19, 2003.

Forsyth, D. W., and A. Li, Array-analysis of two-dimensional variations in surface wave phase velocity and azimuthal anisotropy in the presence of multipathing interference, in Seismic Earth: Array Analysis of Broadband Seismograms, AGU Geophys. Monogr., 157, A. Levander and G. Nolet, eds., 81-98, 2005.

Fouch, M. J., and S. Rondenay, Seismic anisotropy beneath stable continental interiors, Phys. Earth Planet. Inter., 158, 292-320, 2006.

Frederiksen, A. W., M. G. Bostock, and J. F. Cassidy, S-wave velocity structure of the Canadian upper mantle. Phys. Earth Planet. Inter., 124, 175-191, 2001.

Friederich, W., and E. Wielandt, Interpretation of seismic surface waves in regional net- works: Joint estimation of wavefield geometry and local phase velocity-Method and tests, Geophys. J. Int., 120, 731-744, 1995.

Friederich, W., E. Wielandt, and S. Stange, Non-plane geometries of seismic surface wavefields and their implications for regional surface-wave tomography, Geophys. J. Int., 119, 931-948, 1994.

Frost, D. J., The stability of hydrous mantle phases, Rev. Mineral. Geochem. 62, 243-271, 2006.

Goes, S., R. Govers, and P. Vacher, Shallow mantle temperatures under Europe from P and S wave tomography, J. Geophys. Res., 105, 11,153-11,169, 2000.

125 Grand, S. P., Mantle shear structure beneath the Americas and surrounding oceans, J. Geophys. Res., 99, 11,591-11,621, 1994.

Griffin, W. L., et al., Layered mantle lithosphere in the Lac de Gras Area, Slave Craton: Composition, structure and origin, J. Petrol., 40, 705-727, 1999.

Gripp, A. E., and R. G. Gordon, Young tracks of hotspots and current plate velocities, Geophys. J. Int., 150, 321-361, 2002.

Grove, T. L., N. Chatterjee, S. W. Parman, and E. Medard, The influence of H20 on mantle wedge melting, Earth Planet. Sci. Lett., 249, 74-89, 2006.

Griitter, H. S., D. B. Apter, and J. Kong, Crust-mantle coupling: evidence from mantle- derived xenocrystic garnets, in Proceedings of the 7th InternationalKimberlite conference, J. B. Dawson v. 1, J. J. Gurney et al., eds., Red Roof Design, Cape Town, 307-313, 1999.

Gung, Y., M. Panning, and B. Romanowicz, Global anisotropy and the thickness of continents, Nature, 422, 707-711, 2003.

Gurrola, H., G. E. Baker, and J. B. Minster, Simultaneous time-domain deconvolution with application to the computation of receiver functions, Geophys. J. Int., 120, 537-543, 1995

Guseinov, A. A., I. 0. Gargatsev, and R. U. Gabitova, Electrical conductivity of phlogopite at high temperatures, Phys. Solid Earth, 41, 670-679, 2005.

Hacker, B., et al., Near-ultrahigh pressure processing of continental crust: Miocene crustal xenoliths from the Pamir, J. Petrol., 46, 1661-1687, 2005.

Haldorsen, J. B. U., D. E. Miller, and J. J. Walsh, Multichannel Wiener deconvolution of vertical seismic profiles, Geophysics, 59, 1500-1511, 1994.

Haldorsen, J. B. U., D. E. Miller, and J. J. Walsh, Walk-away VSP using drill noise as a source, Geophysics, 60, 978-997, 1995.

Hammond, W. C., and E. D. Humphreys, Upper mantle seismic wave velocity: Effects of realistic partial melt geometries, J. Geophys. Res., 105, 10975-10986, 2000.

Heinson, G., and S. Constable, The electrical conductivity of the oceanic upper mantle, Geophys. J. Int., 110, 159-179, 1992.

Helmstaedt, H. H., and D. J. Schulze, Southern African kimberlites and their mantle sample: implications for Archean tectonics and lithosphere evolution, in Kimberlites and related rocks: Their composition, occurrence, origin, and emplacement, v. 1, Spec. Pub. 14, J. Ross, ed., Geol. Soc. Australia, 358-368, 1989.

Hirth, G., R. L. Evans, and A. D. Chave, Comparison of continental and oceanic mantle electrical conductivity: Is Archean lithosphere dry? Geochem. Geophys. Geosys. 1, doi:10.1029/2000G000048, 2000.

126 Hoffman, P. F., United plates of America, the birth of a craton: Early Proterozoic assembly and growth of Laurentia, Ann. Rev. Earth Planet. Sci., 16, 543-603, 1988.

Hoffman, P. F., Precambrian geology and tectonic history of North America, in The Geology of North America - An overview, W. W. Bally and A. R. Palmer, eds., Geol. Soc. Am., Boulder, 447-512, 1989.

Hoffman, P. F., Geological constraints on the origin of the mantle root beneath the Canadian Shield, Phil. Trans. R. Soc. London, Ser. A, 331, 523-532, 1990.

Holbrook, W. S., W. D. Mooney, and N. I. Christensen, in Continental Lower Crust, D. M. Fountain, R. Arculus, and R. W. Kay, eds., Developments in Geotectonics 23, Elsevier, Amsterdam, 1-43, 1992.

Isaak, D. G., High-temperature elasticity of iron-bearing olivines, J. Geophys. Res., 97, 1871-1885, 1992.

Isachsen, C. E., and S. A. Bowring, Evolution of the Slave craton, Geology, 22, 917-920, 1994.

Jackson, I., J. D. Fitz Gerald, U. H. Faul, and B. H. Tan, Grain size sensitive sesmic wave attenuation in polycrystalline olivine, J. Geophys. Res., 107, doi:10.1029/2001JB001225, 2002.

Jordan, T. H., The continental tectosphere, Rev. Geophys. Space Phys., 13, 1-12, 1975.

Jordan, T. H., Composition and development of the continental tectosphere, Nature, 274, 544-548, 1978.

Jones, A. G., I. J. Ferguson, A. D. Chave, R. L. Evans, and G. W. McNeice, Electric lithosphere of the Slave craton, Geology, 29, 423-426, 2001.

Jones, A. G., R. L. Evans. and D. W. Eaton, Velocity-conductivity relationships for mantle mineral assemblages in Archean cratonic lithosphere based on a review of laboratory data and application of extremal bound theory, Lithos, 109, 131-143, 2009.

Karato, S., The role of hydrogen in the electrical conductivity of the upper mantle, Nature, 347, 272-273, 1990.

Karato, S., Effects of water on seismic wave velocities in the upper mantle, Proc. Japan Academy, 71, 61-66, 1995.

Kennett, B. L. N., The removal of free surface interactions from three-component seismograms, Geophys. J. Int., 104, 153-163, 1991.

Kennett, B. L. N., and E. R. Engdahl, Traveltimes for global earthquake location and phase identification, Geophys. J. Int., 105, 429-465, 1991.

127 Kennett, B. L. N., E. R. Engdahl, and R. Buland, Constraints on seismic velocities in the Earth from traveltimes, Geophys. J. Int., 122, 108-124, 1995.

Kerrick, D. M., and J. A. D. Connolly, Metamorphic devolatilization of subducted marine sediments and the transport of volatiles into the Earth's mantle, Nature, 411, 293-296, 2001.

Kopylova, M. G., and G. Caro, Mantle xenoliths from the southeastern Slave craton: Evi- dence for chemical zonation in a thick, cold lithosphere, J. Petrol., 45, 1045-1067, 2004. Kopylova, M. G., and J. K. Russel, Chemical stratification of cratonic lithosphere: Con- straints from the Northern Slave craton, Canada, Earth Planet. Sci. Lett., 181, 71-87, 2000.

Kopylova, M. G., J. K. Russel, and H. Cookenboo, Upper-mantle stratigraphy of the Slave craton, Canada: Insight into a new kimberlite province, Geology, 26, 315-318, 1998.

Kopylova, M. G., J. Lo, and N. I. Christensen, Petrological constraints on seismic properties of the Slave upper mantle (Northern Canada), Lithos, 77, 493-510, 2004.

Kusky, T. M., Accretion of the Archean Slave province, Geology, 17, 63-67, 1989.

LeCheminant, A. N., and L. M. Heaman, Mackenzie igneous events, Canada: middle Pro- terozoic hotspot magmatism associated with ocean opening, Earth Planet. Sci. Lett., 96, 38-48, 1989.

Langston, C., Structure under Mount Rainier, Washington, inferred from teleseismic body waves, J. Geophy. Res., 84, 4749-4762, 1979.

Lee, C.-T. A., Compositional variation of density and seismic velocities in natural peridotites at STP conditions: Implications for seismic imaging of compositional heterogeneities in the upper mantle, J. Geophys. Res., 108, doi:10.1029/2003JB002413, 2003.

Lee, C.-T. A., Geochemical/petrologic constraints on the origin of cratonic mantle, in Archean geodynamics and environments, AGU Geophys. Monogr., 164, K. Benn, J.-C. Mareschal, and K. C. Condie, eds., 89-114, 2006.

Lenardic, A., et al., What the mantle sees: the effects of continents on mantle heat flow, in The history and dynamics of global plate motions, AGU Geophys. Monogr., 121, 95-112, 2000.

Lewis, T. J., R. D. Hyndman, and P. Fliick, Heat flow, heat generation, and crustal temperatures in the northern Canadian Cordillera: Thermal control of tectonics, J. Geophys. Res., 108(B6), 2316, doi:10.1029/2002JB002090, 2003.

Li, A., D. W. Forsyth, and K. M. Fischer, Shear velocity structure and azimuthal anisotropy beneath eastern North America from Rayleigh wave inversion, J. Geophys. Res., 108(B8), 2362, doi: 10.1029/2002JB002259, 2003.

128 Li, C., R. D. van der Hilst, E. R. Engdahl, S. Burdick, A new global model for P-wavespeed variations in Earth's mantle, Geochem. Geophys. Geosys., 9, Q05018, doi:10.1029/2007GC001806., 2009.

Ligorria, J. P., and C. J. Ammon, Iterative deconvolution and receiver-function estimation, Bull. Seismol. Soc. Am., 89, 1395-1400, 1999.

Mainprice, D., Y. Le Page, J. Rodgers, and P. Jouanna, Ab initio elastic properties of talc from 0 to 12 GPa: Interpretation of seismic velocities at mantle pressures and prediction of auxetic behaviour at low pressure, Earth Planet. Sci. Lett., 274, 327-338, 2008.

Mareschal, M., W. S. Fyfe, J. Percival, and T. Chan, Grain-boundary graphite in Kapuskas- ing gneisses and implications for lower-crustal conductivity, Nature, 357, 674-676, 1992.

Mercier, J.-P., M. G. Bostock, P. Audet, J. B. Gaherty, E. J. Garnero, and J. Revenaugh, J. Geophys. Res., 113, doi:10.1029/2007JB005127, 2008.

Minster, J. B., and T. H. Jordan, Present-day plate motions, J. Geophys. Res., 83, 5331-5354, 1978.

Moorkamp, M., A. G. Jones, and D. W. Eaton, Joint inversion of teleseismic receiver functions and magnetotelluric data using a genetic algorithm: Are seismic velocities and electrical conductivities compatible? Geophys. Res. Lett., 34, doi:10.1029/2007GL030519, 2007.

Nielsen, L., H. Thybo, A. Levander, and L. N. Solodilov, Origin of upper-mantle seismic scattering - evidence from Russian peaceful nuclear explosion data, Geophys. J. Int., 154, 196-204, 2003.

Pawley, A. R., and B. J. Wood, The high-pressure stability of talc and 10 A phase: Potential storage sites for H20 in subduction zones, Am. Mineral., 80, 998-1003, 1995.

Pell, J. A., Kimberlites in the Slave craton, Northwest Territories, Canada, Geoscience Canada, 24, 2, 77-91, 1997.

Polet, J., and D. L. Anderson, Depth extent of cratons as inferred from tomographic studies, Geology, 23, 205-208, 1995.

Pollack, H. N., S. J. Hurter, and J. R. Johnson, Heat flow from the Earth's interior; analysis of the global data set, Rev. Geopys., 31, 267-280, 1993.

Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd ed., Cambridge Univ. Press, Cambridge, 963 pp., 1992.

Priestley, K., and E. Debayle, Seismic evidence for a moderately thick lithosphere beneath the Siberian Platform, Geophys. Res. Lett., 30(3), 1118, doi:10.1029/2002GL015931, 2003.

129 Ritsema, J., and H. van Heijst, New seismic model of the upper mantle beneath Africa, Geology, 28, 63-66, 2000.

Ritzwoller, M. H., N. M. Shapiro, M. P. Barmin, and A. L. Levshin, Global surface wave diffraction tomography, J. Geophys. Res., 107(B12), 2335, 2002.

Roberts, J. J., and J. A. Tyburczy, Partial-melt electrical conductivity: Influence of melt composition, J. Geophys. Res., 104, 7055-7065, 1999.

Rondenay, S., Upper mantle imaging with array recordings of converted and scattered teleseismic waves, Surv. Geophys., 30, 377-405, doi:10.1007/s10712-009-9071-5, 2009.

Rondenay, S., M. G. Bostock, and J. Shragge, Multiparameter two-dimensional inversion of scattered teleseismic body waves, 3, Application to the Cascadia 1993 data set, J. Geophys. Res., 106, 30,795-30,808, 2001.

Rondenay, S., M. G. Bostock, and K. M. Fischer, Multichannel inversion of scattered teleseismic body waves: practical considerations and applicability, in Seismic Earth: Array Analysis of Broadband Seismograms, AGU Geophys. Monogr., 157, A. Levander and G. Nolet, eds., 187-204, 2005.

Rondenay, S., G. A. Abers, and P. E. van Keken, Seismic imaging of subduction zone meta- morphism, Geology, 36, 275-278, 2008.

Rudnick, R. L., and A. A. Nyblade, The thickness and heat production of Archean lithosphere: Constraints from xenolith thermobarometry and surface heat flow, in Mantle Petrology: Field Observations and High Pressure Experimentation, Y. Fei, C. M. Bertka, and B. 0. Mysen, eds., Geochem. Soc. Spec. Pub., 6, 3-12, 1999.

Rudnick, R. L., W. F. McDonough, and R. J. O'Connell, Thermal structure, thickness and composition of continental lithosphere, Chem. Geol., 145, 395-411, 1998.

Rychert, C. A., K. M. Fischer, and S. Rondenay, A sharp lithosphere-asthenosphere boundary imaged beneath eastern North America, Nature, 436, 542-545, 2005.

Rychert, C. A., S. Rondenay, and K. M. Fischer, P-to-S and S-to-P imaging of a sharp lithosphere-asthenosphere boundary beneath eastern North America, J. Geophys. Res., 112, doi:10.1029/2006JB004619, 2007.

Saito, M., DISPER80: A subroutine package for the calculation of seismic normal-mode solutions, in Seismological Algorithms: Computational Methods and Computer Programs, D. J. Doornbos, ed., 293-319, Academic Press, San Diego, 1998.

Sambridge, M., Geophysical inversion with a neighbourhood algorithm- I. Searching a parameter space, Geophys. J. Int., 138, 479-494, 1999a.

Sambridge, M., Geophysical inversion with a neighbourhood algorithm- II. Appraising the ensemble, Geophys. J. Int., 138, 727-746, 1999b.

130 Savage, M. K., Seismic anisotropy and mantle deformation: What have we learned from shear wave splitting? Rev. Geophys., 37, 65-106, 1999.

Silver, P. G., and W. W. Chan, Shear wave splitting and subcontinental mantle deformation, J. Geophys. Res., 96, 16,429-16,454, 1991.

Simons, F. J., A. Zielhuis, and R. D. van der Hilst, The deep structure of the Australian continent from surface wave tomography, Lithos, 48, 17-43, 1999.

Simons, F. J., R. D. van der Hilst, J.-P. Montagner, and A. Zielhuis, Multimode Rayleigh wave inversion for heterogeneity and azimuthal anisotropy of the Australian upper mantle, Geophys. J. Int., 151, 738-754, 2002.

Smith, M. L., and F. A. Dahlen, The azimuthal dependence of Love and Rayleigh wave propagation in a slightly anisotropic medium, J. Geophys. Res., 78, 3321-3333, 1973.

Snyder, D. B., Stacked uppermost mantle layers within the Slave craton of NW Canada as defined by anisotropic seismic discontinuities, Tectonics, 27, TC4006, doi: 10. 1029/2007TC002132.

Snyder, D. B., and G. D. Lockhart, Kimberlite trends in NW Canada, J. Geol. Soc., London, 162, 737-740, 2005.

Snyder, D. B., and M. Bruneton, Seismic anisotropy of the Slave craton, NW Canada, from joint interpretation of SKS and Rayleigh waves, Geophys. J. Int., 169, 170-188, 2007.

Snyder, D. B., M. G. Bostock, and G. D. Lockhart, Two anisotropic layers in the Slave craton, Lithos, 71, 529-539, 2003.

Snyder, D. B., S. Rondenay, M. G. Bostock, and G. D. Lockhart, Mapping the mantle lithosphere for diamond potential using teleseismic methods, Lithos, 77, 859-872 , 2004.

Sobolev, S. V., H, Zeyen, G. Stoll, F. Werling, R. Altherr, and K. Fuchs, Upper mantle temperatures from teleseismic tomography of French massif Central including effects of composition, mineral reactions, anharmonicity, anelasticity and partial melt, Earth Planet. Sci. Lett., 139, 147-163, 1996.

Straub, K. M., S. Rondenay, and D. B. Snyder, Upper mantle structure of the Slave craton from teleseismic body-wave tomography, Eos Trans. AGU, 85(17), Jt. Assem. Suppl., Abstract S33A-06, 2004.

Tarantola, A., and B. Valette, Generalized non-linear problems solved using the least-squares criterion, Rev. Geophys. Space Phys., 20, 219-232, 1982.

Thybo, H., and E. Perchu6, The seismic 8' discontinuity and partial melting in continental mantle, Science, 275, 1626-1629, 1997.

131 Ulrych, T. J., M. D. Sacchi, and S. L. M. Freire, Eigenimage processing of seismic sections, in Covariance Analysis for Seismic Signal Processing, Geophys. Development, 8, Soc. Exploration Geophysicists, 14, 978-997, 1999. van der Lee, S., and A. Frederiksen, Surface wave tomography applied to the North Amer- ican upper mantle, in Seismic Earth: Array Analysis of Broadband Seismograms, AGU Geophys. Monogr., 157, A. Levander and G. Nolet, eds., 67-80, 2005. van der Lee, S., and G. Nolet, Upper mantle S velocity structure of North America, J. Geophys. Res., 102, 22,815-22,838, 1997.

Vinnik, L. P., Detection of waves converted from P to SV in the mantle, Phys. Earth Planet. Inter., 15, 39-45, 1977.

Wagner, L. S., S. Beck, G. Zandt, and M. N. Ducea, Depleted lithosphere, cold, trapped asthenosphere, and frozen melt puddles above the flat slab in central Chile and Argentina, Earth Planet. Sci. Lett., 245, 289-301, 2006.

Weeraratne, D. S., D. W. Forsyth, and K. M. Fischer, Evidence for an upper mantle plume beneath the Tanzanian craton from Rayleigh wave tomography, J. Geophys. Res., 108(B9), 2427, doi: 10.1029/2002JB002273, 2003.

Westerlund, K. J., et al., A subduction wedge origin for Paleoarchean peridotitic diamonds and harzburgites from the Panda kimberlite, Slave craton, evidence from Re-Os isotope systematics, Cont. Mineral. Petrol., 152, 275-294, 2006.

Yang, Y., and D. W. Forsyth, Regional tomographic inversion of amplitude and phase of Rayleigh waves with 2-D sensitivity kernels, Geophys. J. Int., 166, 1148-1160, 2006.

Yao, H., C. Beghein, and R. D. van der Hilst, Surface wave array tomography in SE Tibet from the ambient seismic noise and two-station analysis- II. Crustal and upper-mantle structure, Geophys. J. Int., 173, 205-219, 2008.

Zhou, Y., G. Nolet, F. A. Dahlen, and G. Laske, Global upper-mantle structure from finite-frequency surface wave tomography, J. Geophys. Res., 111, B04304, doi:10.1029/2005JB003677, 2006.

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