An Anomalous Breccia in the Mesoproterozoic (~1.1 Ga) Atar Group,

Mauritania: Endogenic vs. Exogenic Genesis

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Douglas J. Aden

August 2010

© 2010 Douglas J. Aden. All Rights Reserved.

2

This thesis titled

An Anomalous Breccia in the Mesoproterozoic (~1.1 Ga) Atar Group,

Mauritania: Endogenic vs. Exogenic Genesis

by

DOUGLAS J. ADEN

has been approved for

the Department of Geological Sciences

and the College of Arts and Sciences by

Keith A. Milam

Associate Professor of Geological Sciences

Benjamin M. Ogles

Dean, College of Arts and Sciences 3

ABSTRACT

ADEN, DOUGLAS J., M.S., August 2010, Geological Sciences

An Anomalous Breccia in the Mesoproterozoic (~1.1 Ga) Atar Group,

Mauritania: Endogenic vs. Exogenic Genesis (101 pp.)

Director of Thesis: Keith A. Milam

The Tawaz Breccia (TB) is an anomalous breccia within the Mesoproterozoic

(~1.1 Ga) Atar Group on the West African Craton (WAC). This unit is unusual because its sedimentary features record a high energy event in an otherwise calm shallow marine depositional environment. In order to determine a depositional type for this unit, breccia types produced by common geologic processes (described through an extensive literature search) were evaluated for their statistical correlation with initial field observations of the

TB. This analysis has shown that breccias generated by either an impact or non-impact tsunami correlate best with the TB. Geochemical analyses on a suite of samples collected during reconnaissance field work were used to discriminate between these two tsunami types. Os and Re concentrations and the ratios of 187Os/188Os and 187Re/188Os demonstrate that TB values correlate with impact mixtures and terrestrial materials. Therefore, although the TB was likely formed by a tsunami, preliminary analyses can not completely eliminate the possibility that the tsunami was triggered by an impact event (of possible iron composition).

Approved: ______

Keith A. Milam

Associate Professor of Geological Sciences 4

ACKNOWLEDGMENTS

I would like to acknowledge the National Science Foundation’s Sedimentary

Geology and Paleontology Division for their support of this work. Without their help this research would not have been possible. 5

TABLE OF CONTENTS

Page Abstract ...... 3 Acknowledgments...... 4 List of Tables ...... 6 List of Figures ...... 7 Chapter 1: Introduction ...... 8 Chapter 2: Comparative Statistical Assessment of Possible Depositional Mechanisms for the Tawaz Breccia ...... 17 Methods ...... 17 Correlation Analyses ...... 21 Correlation Analysis #1 ...... 22 Correlation Analysis #2 ...... 24 Results ...... 27 Error Analysis ...... 30 Resolution Error ...... 30 Potential Error from the Number of References ...... 32 Number of Characteristics Used ...... 34 Discussion ...... 35 Summary ...... 38 Chapter 3: Rhenium-Osmium Analysis of the Tawaz Breccia ...... 39 Introduction ...... 39 Methods ...... 44 Sample Preparation ...... 44 XRD Analyses ...... 46 Re-Os (and Os Isotope Analysis) ...... 48 Results ...... 49 Discussion ...... 54 Conclusion ...... 57 References ...... 58 Appendix A: Breccia Spreadsheet References ...... 66 Appendix B: Breccia Spreadsheets ...... 76 Appendix C: Tawaz Breccia Sample Descriptions and Photos ...... 85 6

LIST OF TABLES

Page

Table 1: Breccia spreadsheet ...... 18

Table 2: Results of analysis #1 and #2...... 28

Table 3: Spreadsheet with random values between 0 and 1 ...... 31

Table 4: Spreadsheet with values from Table 3 ...... 31

Table 5: Potential correlation error (±) ...... 33

Table 6: Re-Os concentrations and isotopic ratios ...... 50

7

LIST OF FIGURES

Page

Figure 1: The West African Craton ...... 9

Figure 2: Cross-section through north-central Mauritania ...... 9

Figure 3: Stratigraphic column of the TB near Atar ...... 11

Figure 4: Faulted and laminated base ...... 13

Figure 5: Ball and pillow structures ...... 14

Figure 6: Fluidized beds ...... 14

Figure 7: Interbedded breccia and molar-tooth structures ...... 15

Figure 8: Interbedded breccia and molar-tooth structures...... 15

Figure 9: Flowchart for correlation analysis #1 and #2 ...... 23

Figure 10: Histogram for analysis #2...... 26

Figure 11: Calculated percent correlation ...... 35

Figure 12: Relative stratigraphic positions and descriptions ...... 45

Figure 13: XRD results ...... 51

Figure 14: Os and Re ...... 52 8

CHAPTER 1: INTRODUCTION

The West African Craton (WAC) is a large (~ 4.4 million km2) and ancient

portion of the African continent consisting of the Archaean to Proterozoic-aged Man and

Reguibat Shields, the Anti-Atlas Mountains, and the Taoudenni Basin (Figure 1). WAC was assembled into its present configuration during the Eburnean orogeny at 2.0 Ga

[Ennih and Liegeois, 2008], when the Paleoproterozoic-aged Eburnean terrane collided with an Archaean terrane along a NW-SE margin [Schofield and Gillespie, 2007], and has since remained tectonically stable. After assembly during the Mesoproterozoic, a period of minimal tectonic activity 1.7 – 1.0 Ga [Ennih and Liegeois, 2008] allowed this large area to become a craton through growth or accumulation of cooling asthenosphere at the base of the craton [Black and Liegeois, 1993]. After cratonization, a period of extensive subaerial erosion resulted in a nearly flat surface or peneplane. Between 1.1 and 1.0 Ga, subsidence (3-4 m/Ma) of the northern part of the Taoudeni Basin was controlled by the earlier Eburnean formation of the Tiris ferruginous gneisses within the surrounding (less dense) granites, gneisses and migmatites. This mass differential led to the formation of the Tiris-Richat-Tagant trough (Figures 1 and 2) [Bronner et al., 1980].

Subsidence along with eustatic changes resulted in a periodic epicontinental sea and the commencement of marine deposition. Within this basin, there are five supergroups (or 4 Megasequences [Deynoux et al., 2006] not counting the thin Meso-

Cenozoic cover), which are divided by discontinuities related to extra-cratonal tectonic events [Bertrand-Sarfati et al., 1991]. Supergroup 1 (Figures 1 and 2) consists of

9

Figure 1. The West African Craton, modified From [Ennih and Liegeois, 2008] and [Bronner et al., 1980] after [Fabre, 2005] and [Liegeois et al., 2005]. Mesoproterozoic aged Supergroup 1 from [Teal et al., 2005].

Figure 2. Cross-section through north-central Mauritania (to basement), featuring the Tiris-Richat-Tagant trough. Modified from [Bronner et al., 1980]. 10

carbonates, shales, and siltstones [Bronner et al., 1980] and contains the unit of interest for this study. This Mesoproterozoic supergroup varies (E-W) from 800 to ~3500 m in thickness across the trough [Moussine-Pouchkine and Bertrand-Sarfati, 1997; Deynoux et

al., 2006], and is sub-divided into three groups (in ascending order and separated by unconformities): Char, Atar, and Assabet el Hassiane (Figure 3).

The focus of this study is the Atar Group, a ~1.1 Ga [Teal et al., 2005; Bartley,

unpublished data], 550 m thick (near Atar) [Bertrand-Sarfati et al., 1991] package of

sedimentary rock that has been correlated across 1500 km of Mauritania, Algeria, and

Mali (Figure 1). The Atar Group (Figure 3) is composed of alternating carbonates and

siliciclastics (shales and siltstones), both indicative of a shallow marine setting. Seventy-

five percent of these carbonates are composed of columnar stromatolites such as

Conophyton and Jacutophyton. The interstitial space of the Conophyton in the Atar

Group was found to be free of transported sediment, indicating an environment located

below wave base [Bertrand-Sarfati and Moussine-Pouchkine, 1988]. It is thought that these stromatolites grew in an epeiric sea (on the WAC), as evidenced by a very shallow bottom gradient and basin wide stromatolites. The more extensive columnar stromatolites

(mainly Conophyton) can be biostratigraphically linked since they show basin-wide simultaneous evolutionary development of filmy microstructures (that do not show up again). Further up section another basin-wide development - tussocky microstructures (a mounded and radially fibrous fabric) - can be found, again showing that this area periodically existed as one continuous cratonic shallow sea [Bertrand-Sarfati and

Moussine-Pouchkine, 1988]. This extensive columnar stromatolite regime alternates with 11

TB

5

Figure 3. Stratigraphic column of the TB near Atar. (1) carbonate micrite (2) stromatolite flakes / molar-tooth, (3) vermicular structures, (4) breccia. (5) ball and pillow structures at base. [After Bertrand-Sarfati and Moussine-Pouchkine, 1988)

a facies mosaic system of marine shales and mixed carbonates of uncertain and likely irregular water depth (likely due to stromatolite topography) thought to relate to storm- 12

influenced shallow marine conditions and not a tidal system. A tidal system is not

favored because common sedimentary features attributed to tidal environments

(herringbone cross stratification, mud cracks, flat laminated stromatolites, channels, or

evaporites) are rare or absent [Bertrand-Sarfati and Moussine-Pouchkine, 1988]. Within

the Atar Group, the Tawaz Formation (Figure 3) contains a 2-6 m thick unit informally

known as the Tawaz Breccia (TB), that is as extensive as the Atar Group (1500 km), and whose lithologic and textural characteristics are unrelated to the preceding and anteceding beds [Bertrand-Sarfati et al., 1997] and indicative of a high-energy depositional environment.

An ascending transect (Figures 4-8) [Kah, 2008] through the TB transitions from a faulted and laminated base (Figure 4), to ball and pillow structures (Figure 5) to fluidized beds (Figure 6), followed by interbedded breccia and molar-tooth structures

(Figure 7 and 8). These breccias are composed of light (white to grey) clasts of allochthonous molar-tooth (MT) structures (Figure 7), thought to originate as rapidly lithified cracks in the substrate [Pratt, 2001; Shields, 2002; Crawford et al., 2006]. Also

present are dark (bluish) clasts composed of MT microspar [Kah, 2008]. Clasts in the TB

range from granule (2mm) to boulder size (meters in maximum diameter). No clasts

larger than boulders (defined by Blair and McPherson [1999] as having a maximum

diameter larger than 4.1m), are known in the TB [Kah, 2008]. Below and above this unit,

the stromatolitic carbonates are indicative of a low energy depositional environment

within the photic zone (relatively shallow water, 10-100m deep [Kah, 2010]). The

presence of breccias along with features indicative of rapid erosion (scour surfaces) and 13

rapid deposition (suspended clasts, rip up clasts, ball and pillow structures) indicates an

unusually high energy event (or events) was responsible for deposition of the TB. This

unit has been variously referred to as a breccia, megabreccia, “blue bed” (due to its blue

appearance at distance in the field), and a molar-tooth dominated carbonate. This

nomenclature variability reflects the considerable lateral facies changes of this unit.

Figure 4. Faulted and laminated base of TB. 14

Figure 5. Ball and pillow structures.

Figure 6. Fluidized beds. 15

Figure 7. Interbedded breccia and molar-tooth structures.

Figure 8. Interbedded breccia and molar-tooth structures. 16

Earlier studies considered the TB to be a seismic deposit associated with the Pan-

African Orogenic Belt in Algeria that was active in the Neoproterozoic [Moussine-

Pouchkine, and Bertrand-Sarfati, 1997] However, later studies determined the TB to be

Mesoproterozoic in age [Teal et al., 2005; Bartley, unpublished data], a stable time

period, and therefore not associated with the Pan-African Hoggar Uplift [Kah et al.,

2007]. Furthermore, the cyclic and extensive nature of the deposits does not correlate

well with a seismic regime [Kah et al., 2007]. Therefore, a depositional mechanism for the TB remains to be determined. What depositional mechanism could create a deposit as

laterally extensive as the TB? What type of breccia is associated with the features

described in the TB?

In order to answer these questions a survey of possible breccia formation methods was undertaken and then characteristics of known breccia types were compared with the

TB (Chapter 2). Two correlation analyses were then used to determine which of the

breccia forming mechanisms was the most likely to have led to the formation of the TB.

In Chapter 3, geochemical analysis of samples from the TB are characterized using X-ray diffraction (XRD) followed by Re and Os analyses to better understand the composition of the rocks composing the TB, and to test for the possible presence of impactor components. 17

CHAPTER 2: COMPARATIVE STATISTICAL ASSESSMENT OF POSSIBLE

DEPOSITIONAL MECHANISMS FOR THE TAWAZ BRECCIA

It is challenging to ascertain a specific breccia forming mechanism for the TB because there are many geologic processes that can form breccias. By systematically comparing the characteristics of common breccia types to the TB, breccia mechanisms that are more likely to have formed the TB can be identified.

Methods

An extensive literature review of one hundred and eight references was used to describe breccia types based on commonly-used characteristics. Of these references, all but eleven were journal articles, nine were text books (useful for general data) and two were personal communications with a collaborator regarding the TB. This was combined with personal observations of a few specific breccia types observed during field work in

California (Inyo County, Dublin Hills, volcanic breccia), Tennessee (karst breccia in the

Flynn Creek impact structure), Nevada (Alamo Impact breccia), and Wyoming

(Yellowstone volcanic breccia). These personal observations were included as additional sources. Characteristics between each breccia type and the TB were compared based on these sources.

In order to identify the geologic process responsible for deposition of the TB, a spreadsheet was constructed to compare characteristics of different breccia types to the same characteristics in the TB (Table 1). Two correlation analyses were then applied to this table in order to determine which known breccia forming mechanism (of 14) correlated the best with the TB. The first correlation analysis compared the characteristics 18

. nly (correlation lue of 0.5) has lue of 0.5) s the respective . Green cells indicate that the respective breccia type commo cate that the respective breccia type sometimes (correlation va arate referenceBreccia list (Appendix A: Spreadsheet References) espective breccia type rarely/never (correlation value of 0) ha characteristic. Numbers in cells represent references; see sep value of 1) has the respectivevalue of 1) has characteristic. Yellow cells indi the respective characteristic, and red cells indicate that the r Breccia types axis across the top, characteristics on the vertical Table 1. Breccia spreadsheet Table 1. Breccia spreadsheet 19

of each known breccia type to the TB to determine which breccia type was most

comparable to the TB, thus serving as a proxy for associating a particular breccia

depositional mechanism with the TB. The second correlation analysis initially compared the characteristics of each breccia type to every other known breccia type (excluding the

TB) in order to determine which characteristics are most useful for distinguishing between breccias. Statistical correlation was then used to compare breccia types to the TB using the most discriminating characteristics.

Fourteen of the most common breccia types (glacial, tsunami, storm, turbidite, debris flow/alluvial fan, rock fall/talus slope, fault, pyroclastic flow, auto-brecciation, karst collapse, evaporite collapse, lithic ejecta, suevite ejecta, and impact tsunami) were selected for comparison. These were examined because they represent the range of common ways that breccia is formed at the Earth’s surface. A few special cases of breccia formation were not considered because they were found to be too localized or not long lasting enough to have formed the TB (extant since 1.1 Ga). An example of a breccia type that was not considered is hyaloclastic breccia; it forms in association with pillow lava and the resulting glassy clasts are very small and devitrify quickly into palagonite [Winter, 2001]. This breccia mechanism could not have formed the large clasts that are found in the TB. Furthermore, the glass that volcanic eruptions form is short- lived and would have devitrified over the long period of time that the TB has existed.

Specific characteristics (62 in total) were used to describe each of these breccias, based on those commonly used in sedimentology literature; the first, clast size, is indicative of peak depositional energy. Grain sizes were delineated using an extension of 20

the Udden-Wentworth grain-size scale proposed by Blair and McPherson [1999]. Clast

variability (mono- or polymict) is a combination of many factors (source, energy, range).

Clast sorting (well, moderate, poor, unsorted) is determined by the energy of the system.

Clast rounding (rounded, subrounded, subangular, angular) usually shows how far the

sediments were transported or how easily they are weathered. Support (clast or matrix) depends on the clast/matrix ratio; in a clast-supported breccia the clasts rest on one another and would occupy the same volume if the matrix was not present. In a matrix- supported breccia, the clasts are held up by a framework of matrix. Clast lithology

(igneous (intrusive or extrusive), metamorphic (contact, shock, dynamic, or regional), sedimentary (chemical or detrital)) indicates clast source and sometimes can narrow down formational mechanisms. Clast source (allo- parauthi- or authigenic) is reflective of transport distance and energy of deposition. Clast grading (vertical, lateral, or multi- sequence) varies depending on the energy gradient. Vertical grading implies that energy was decreasing (or increasing if reverse) throughout deposition, lateral grading is a result of energy decreasing in a horizontal direction such as shoreward. Multi-sequence grading occurs when the energy source increases and decreases multiple times (e.g. storm surges).

Clast alignment (parallel or perpendicular to movement, bi- or unidirectional imbrication, random) indicates if there was entrainment and if it was in one direction, or cyclic

(multiple directions). Thickness per event (<1m, 1-10m, or >10m) and lateral extent

(<100km, 100-1000km, >1000km) are also related to the amount of sediment moved and the system energy. Depositional environment (subaqueous, transitional, subaerial) and morphology of deposit (cross-sectional and plan view) can be distinctive of particular 21 types of breccia, for example karst and evaporite collapse breccias are often sinkhole shaped in cross-section, and fault breccia commonly has a linear expression.

The spreadsheet (Table 1) used for comparing the characteristics between different breccia types was constructed after an extensive literature search. Each cell in the spreadsheet contains reference information (represented by numbers) for characteristics pertaining to each breccia type and indicates whether or not a particular breccia has that characteristic. If literature references indicated that a particular breccia type was likely to display a specific characteristic, then that cell was assigned the color green to visually represent a positive occurrence. If literature references indicated that a particular breccia type sometimes displayed a specific characteristic, then that cell was colored yellow. If literature searches indicated that a breccia type did not display a particular characteristic, the associated cell was color-coded red. This color-coding allowed for simplicity of visual identification for subsequent correlation analyses.

Correlation Analyses

Two correlation analyses of the spreadsheet (Table 1) were performed to determine which of the breccia types correlates best with the sedimentological characteristics of the TB. Correlation analysis #1 compared all characteristics of each breccia type to the TB. As an added step, non-distinguishing characteristics were removed from comparison and the remaining characteristics were compared to the TB.

Correlation analysis #2 compared each breccia type to each other breccia type (initially excluding the TB) in order to determine which characteristics are the most suitable for distinguishing between breccia types in general. Then the least distinguishing 22 characteristics are progressively removed (through the use of bins on a histogram) and the remaining (more distinguishing) characteristics compared to the TB after each removal.

For both correlation analyses #1 and #2 (Figure 9), once the characteristics to be compared to the TB were chosen, (or in the case of analysis #1 part 1, all characteristics), each spreadsheet cell for each breccia type (column) was assigned a number (by assigned color comparison) indicative of the significance of the correlation (positive, partial, or negative) between the breccia type and the TB. For example, if a specific breccia-forming method commonly had a particular characteristic (assigned green) and the TB also had this characteristic (green), the spreadsheet cell for the known breccia type would be assigned a value of one (commonly + commonly = 1) however if one of the two only sometimes (assigned yellow) had a characteristic only a 0.5 would be assigned since the correlation is not as strong (commonly + sometimes = 0.5). Next, since absence of evidence (assigned red) does not imply evidence of absence, negative correlations were assigned a zero; if two breccia types lack a particular feature, that does not imply that they are the same, or correlate better with each other, (rarely or never + anything = 0).

Correlation Analysis #1

Analysis #1 is divided into two parts. In the first part, all 62 characteristics of the

14 breccia types are directly compared to the TB as a means of determining which breccia mechanism may have been responsible for deposition/emplacement of the TB. 23

Figure 9. Flowchart for correlation analysis #1 and #2. 24

The cell corresponding to a particular characteristic of each breccia type (column) was

compared to the TB and a correlation value was calculated (1, 0.5 or 0 as described

above). Correlation values were summed per breccia type (column) and divided by the

total number of characteristics (62). This gave an approximation of how well each breccia type correlated with the TB using all characteristics (Appendix B: Breccia Spreadsheets, first one). In the second part, characteristics that occur in every breccia type (ignoring the

TB), or only correlate negatively, are removed because they did not distinguish breccia

types (characteristics that never occur in any breccia type would also be eliminated by

this step, but they were not initially included on the spreadsheet). New correlation values

were calculated (in the same way as part one) but only for the remaining (most

distinguishing) characteristics. Then, in order to determine an average percent correlation

of each breccia type with the Tawaz Breccia, the correlation values for each column

(breccia type) were individually summed and then divided by the number of remaining

(48 of 62 original) characteristics (Appendix B: Breccia Spreadsheets, second one).

Correlation Analysis #2

In order to provide an independent assessment of which breccia characteristics are

the most discriminating among the breccia types (initially excluding comparison to the

TB) a second correlation analysis between breccia types was performed. With this

analysis, each characteristic for all 14 breccia types was compared to the same

characteristic for each other breccia type (excluding the TB). This resulted in 91

comparisons with each pair of spreadsheet cells compared and assigned a 1, 0.5 or 0 (by

using the colors assigned from the referenced sources) and a series of new spreadsheets 25

(Appendix B: last 7 breccia spreadsheets). The logical statement used to calculate

correlation values between each pair of cells compared is: IF cell X = cell Y THEN

assigned value = zero because cells are the same (non-distinguishing), ELSE take the absolute value of cell X – cell Y to find the difference in correlation value as a positive number. For example; [1 compared to 1=0], [1 compared to 0.5=0.5], [0.5 compared to

0.5=0], and [0 compared to 1=1]. Then cell values for each row resulting from this comparison, were summed and divided by the total number of comparisons (91). The resulting row average falls between zero (characteristic never correlates between any pair) to one (characteristic correlates between every pair), the values between these two extremes show how often a characteristic correlated between breccia types. Next, characteristics (rows) that had averages of zero were excluded because they were not useful for distinguishing between breccias. The remaining 56 distinct characteristics were compared to characteristics of the TB using correlations assigned by color, and each spreadsheet cell for each breccia type was given a new correlation value of 1, 0.5, or 0 after comparison with the TB. Correlation values in each column (each corresponding to a specific breccia type) were then individually summed and divided by the number of utilized (56 of the 62 original) characteristics, which resulted in an average percent correlation of each breccia type/mechanism with the unknown (TB).

Since a breccia characteristic is not as distinguishing if it is almost always present

(closer to 1) or usually not present (closer to 0) in all breccia types, a method was devised to progressively eliminate these less distinct characteristics using a frequency distribution

histogram (Figure 10). Correlation values were divided into bins and plotted on a 26

histogram (Figure 10) that shows more distinguishing characteristics toward the center and less distinguishing characteristics toward the edges of the histogram. Fourteen bins were used because the range of correlation value sums is from 0 to 14. If all 14 breccia types correlate they have a row (characteristic) sum of 14. For this analysis the bin size was set at 1, with 14 chances to correlate or not. This histogram was used to retain the

Figure 10. Histogram for correlation analysis #2.

most discriminating characteristics, by eliminating the least distinguishing (one high and

one low bin at a time). Each of the 14 bins represents the number of correlations for the respective characteristic, if a characteristic is commonly found in all breccia types (all green) then it falls into the fourteenth bin (14 of 14 or 100%). If a characteristic is found 27

in 6 (all green) of 14 breccia types it falls into the sixth bin (6 of 14 or 43%). Elimination

of the bins in this way selectively removes the specific characteristics that correlate the least with other breccia types and focuses on the ones most discriminating for breccias in

general. This process was repeated six times (Appendix B: last 7 breccia spreadsheets), each time by eliminating the next highest and lowest bins (and thus their respective characteristics) then summing each column (corresponding to a breccia type) and dividing by the number of remaining characteristics, resulting in a new percent correlation, based on more distinct characteristics (Table 2). The outcome is a percent agreement between each known mechanism and the Tawaz Breccia based on the most distinct physical characteristics of breccia. This elimination is based on the idea that the most distinguishing characteristics among all breccia types are not those that are always present (since they don’t distinguish anything), nor those that are rarely or never present

(since they seldom distinguish a specific breccia type), but the features that lie between

these extremes and eliminate some breccia types but still identify others. Therefore,

analysis #2 identifies characteristics that are the best for distinguishing between breccia

types (while analysis #1 compares all characteristics to the TB, and then compares again

after removing those known to not be distinguishing). In addition, analysis #2 is a check

to see if analysis #1 is valid by comparing the results of two independent methods of

analysis.

Results

The results of correlation analysis #1 (Table 2) for all ranges of characteristic included (or excluded) show that the breccia type that correlates best with the Tawaz 28

Presented as percent correlation. correlation. percent Presented as Table 2. Results of analysis #1 and #2 29

Breccia, when all characteristics are used, is normal tsunami (71% correlation) closely

followed by impact tsunami (69% correlation) and then glacial and evaporite collapse

(both 65% correlation). If non-distinguishing characteristics are removed, impact tsunami is highest (57% correlation) followed by non-impact tsunami (54% correlation) glacial

(50% correlation) and turbidites (43% correlation).

The results of analysis #2 provide a similar set of percent correlations; of which

impact tsunami is the highest (in all seven eliminations of non-distinguishing

characteristics), closely followed by non-impact generated tsunami breccia types in the

first four sets of eliminations. After the first four sets of eliminations of non-

distinguishing characteristics, 70% of characteristics (and 4 bins from each side of the

histogram; 8 of 14 total bins) have been removed and few (19 of 62) distinguishing

characteristics remain. At this point, other breccia types begin to correlate as well as non-

impact tsunami (though impact tsunami remains the highest). Here impact tsunami is

45% correlated and karst collapse and glacial types are 39% correlated followed by lithic

impact ejecta at 37%. At 5 bins eliminated, impact tsunami is 43% correlated, while

glacial, non-impact tsunami, and lithic ejecta are 39% correlated, with four others 32%

correlated. Finally, with 6 bins eliminated (only 7 characteristics remaining) impact

tsunami is 64% correlated with the TB, and again glacial, non-impact tsunami, and lithic

ejecta are 57% correlated. In all cases but one (for analysis #1 when all categories were

retained, and non-impact tsunami was 2% higher), the percent correlation between impact

tsunami and the TB is highest. 30

Error Analysis

Resolution Error

Since the literature does not often report details to within specific percents (x

breccia characteristic occurs y% of the time), it is possible that resolution is being lost in

this study by limiting the number of correlation values to three. In order to address

potential error arising from insufficient numerical resolution (of the detail of breccia

characteristics) a spreadsheet was constructed and assigned hypothetical values of higher

resolution than the literature. For this study (Table 1) correlation values were limited to 1,

0.5, and 0 (commonly, sometime, and rarely to never). However, for this hypothetical

spread sheet, random correlation values (using the Excel random number generator

function) were assigned from 0 to 1 in steps of 0.1 (i.e. 0.0: the characteristic never

occurs for the breccia type, 0.1: the characteristic occurs 10% of the time, 0.6: the

characteristic occurs 60% of the time for the given breccia type, etc., Table 3).

Then, assuming that these random values were the true resolution of breccia

characteristics in the field, each column of correlation values was summed and then

divided by 10 (100% correlation) and multiplied by 100 to give the average percent

correlation for each breccia type with the TB, using this random data set at 10% data

resolution. Next, these random values were binned into the original lower resolution

(Table 4) (0=<.25, .25<0.5< .75, and 1=>.75) to see what potential resolution is lost by only using three (available) instead of ten (hypothetical) divisions.

Comparing the differences in percent correlation between Table 3 and Table 4, results in a mean error of 6.1% (1σ standard deviation of 4.4%, max 14.3%). This shows 31

that if the details of every breccia and characteristic considered could be specified to

within 10% of their occurrence frequency (i.e. glacial breccia has boulder sized clasts

90% of the time), but were binned into categories of commonly, sometimes, and rarely to never, then correlation values could be off by 6% on average. Errors this high mean that some of the correlation values presented in Table 2 are indistinguishable within the margin of error. For example, for correlation analysis #1 (all characteristics included) 12

of the 14 breccia types are indistinguishable; only rock fall and auto-breccia have

correlations values low enough to be distinguishable from other breccia types.

Table 3. Spreadsheet with random values between 0 and 1

Rounded past the first decimal place, breccia types A-J, characteristics 1-10.

Table 4. Spreadsheet with values from Table 3

Rounded to 0, 0.5 or 1, breccia types A-J, characteristics 1-10.

32

For correlation analysis #1 (when non-distinguishing characteristics are

excluded), the most correlative tsunami and impact tsunami breccia types are

distinguishable from other breccia types except for glacial breccias. For correlation

analysis #2 (when non-distinct characteristics are excluded) the most correlative tsunami

and impact tsunami are indistinguishable from glacial, turbidite, debris flow, and pyroclastic flow. Similarly for correlation analysis #2 (when more non-distinguishing characteristics are excluded – one high and one low bin) tsunami and impact tsunami are distinguishable from everything except for glacial and turbidite breccia types. The other elimination steps of correlation analysis #2 (Table 2) are similar.

If the correlation values used for Table 1 have lost resolution (from a hypothetical precision of 0.0, 0.1, 0.2, 0.3, etc.) by being grouped into the less precise categories of 0,

0.5 and 1, then this analysis shows that not all breccia types are distinguishable.

Unfortunately, this hypothetical error analysis could only be achieved if each breccia

outcrop was reexamined to determine the percent likelihood of each characteristic occurring within each breccia type.

Potential Error from the Number of References

Since the breccia spreadsheet is based on literature review, having as many references as possible is important for covering a wide range of breccia types and examples. Due to time constraints, the maximum number of references in each comparison (spreadsheet cell) was set at 5 (a sixth would not be used unless it was more

detailed and could replace one of the five) and the minimum at 1. During my literature

search, I attempted to fill each cell with a total of 5 references. For the entire spreadsheet 33

5 references were used in 10% of cells, 4 references in 15%, and 3 and 2 for 33% of the

cells (868 cells total). In cases where only 1 reference was used (10% of cells); specific

details of the characteristics of a breccia are not well studied and/or reported in the

literature. In order to determine error for cells with only one reference, a test was run on each of the 9 statistical analyses presented in Table 2. For these, the value of an arbitrary cell was changed from a positive to a negative correlation to test what the percent error in the correlation value for that breccia type would be if one cell was incorrect, which is

more likely if a cell only has one reference (Table 5). Next, 2 and then 3 cells were

changed from positive to negative to observe the percent correlation change if 2 or 3 cells

were incorrect. Table 5 shows the potential (plus or minus) percent correlation errors if

one, two, or three cells (characteristics) for any of the breccia types are wrong. As the

number of characteristics used is decreased the potential percent error (in correlation)

remains much the same until there are only a few characteristics left (i.e. the last three columns of Table 5). For all 9 correlation analyses presented on Table 2, if one cell is inaccurate then the percent error (± the values in Table 5) of impact tsunami overlaps

with non-impact tsunami, and the two correlations are indistinguishable. If more than

Table 5. Potential correlation error (±)

For analyses #1 and #2 if 1, 2, or 3 cells are incorrect.

34 one cell is inaccurate then other breccia types begin to overlap as well. The potential error for the later correlation analysis (analyses #2 elimination 4 and beyond) is very large.

Since there are so few characteristics remaining even a single incorrect cell generates a significant percent error. Thus, use of as many references as possible minimized the error in these correlation analyses.

In addition, the average number of references per correlation type was calculated and found to be comparable; 2.9 references per green cell, 2.5 per red, and 2.3 per yellow cell. This shows that the different correlation significances (colors) were given equal weight through the literature research and that no correlation category had significantly more references than another.

Number of Characteristics Used

In order to determine if 62 characteristics (and not a smaller number) were the most useful for distinguishing breccia types, an additional error analysis was performed.

For each of the 14 breccia types, a random selection of 10, 20, 30, 40, and 50 characteristics (generated by Excel, one random set was used for all cases) were removed and a new correlation analysis performed with the remaining characteristics for each breccia type versus the TB. Figure 11 shows that changes in percent correlation with a decreasing number of characteristics used is minimal (averages between 1% and 3%), but that below 22 (to 12) characteristics, percent correlation falls (more significantly) an average of 7%. This suggests that the number of characteristics used in this study were sufficient to minimize error. 35

Discussion

In addition to correlation analyses, which show that impact tsunami or non-impact tsunami are the most likely generative mechanisms for the TB, several breccia formational types can also be qualitatively eliminated if their most distinct characteristics are absent from the TB. Others can be labeled as unlikely if the type/mechanism is not known to have been active during the time of formation of the TB, or on the WAC.

Correlation with Random Characteristic Selection

60

55

50 glacial tsunami

45 storm turbidite debris flow /alluvial fan rock fall (talus slope) 40 Percent Correlation Percent fault pyroclastic flow auto-brecciation 35 karst collapse evaporite collapse lithic suevite 30 impact tsunami

25

20 0 10203040506070 Number of Characteristics Used

Figure 11. Calculated percent correlation by random characteristic removal.

36

The lack of apparent igneous clasts common to pyroclastic breccias [Curtis, 1954]

or melt in the TB precludes emplacement by volcanic pyroclastic flows or auto-

brecciation. Marine slumps are restricted to steeper gradient settings (such as a

continental shelf break) than that in which the TB was deposited. Furthermore, slumps

involve the movement of large coherent masses [Vestal and Lowrie, 1982] rather than

mobilization of clasts to form a breccia such as the TB. Glacial mechanisms can also be

excluded because the only known glacial deposit in the Mesoproterozoic is the recently

identified Vazante Formation in Brazil (Geboy, 2006; Miller et al., 2009). Tectonic

breccias can be distinguished by their requisite confinement along tectonically-active margins [Woodcock and Mort, 2008]. The Atar Group, however, represents cratonal deposition [Ennih and Liegeois, 2008] with no indication of active syndepositional orogenesis.

Evaporitic collapse breccias form by the dissolution of evaporites, rapidly leading to weakening and collapse of overlying rocks [Johnson, 2008]. Although there is evidence of localized evaporites in the Atar Group [Goodman and Kah, 2004], the timing of TB deposition is earlier than the development of thick, basin-wide evaporites, which did not arise until the Neoproterozoic [Kah, 2001]. Karst collapse breccias are also

typically limited in lateral extent, based on the size of the failing cavity. A collapse 1500

km wide and a few meters thick (like the TB) is very unlikely and does not match the

morphology or extent of even the largest karst landforms [Ford, 1988].

Another possible mechanism with wider lateral extent includes turbidites or

sediment gravity flows which can transport large amounts of material hundreds of 37 kilometers. Unlike the TB, however, they are only preserved in deeper water environments below 250-300m [Walker, 1992; Walker and Plint, 1992], where they are seen to cut pre-existing beds. Storm deposits (formed with enough energy to move clasts) preserve unidirectional imbrication of clasts [Kortekaas and Dawson, 2007] unlike the

Tawaz Breccia, in which clasts are bidirectional to multidirectional. However, storm deposits rarely contain the large rip-up clasts or boulders [Kortekaas and Dawson, 2007] as are commonly found in the Tawaz Breccia [Kah, 2008].

Deposition by tsunami can result in laterally-extensive deposits in shallow marine and onshore environments. However, tsunamis require a high-energy triggering event such as an earthquake, whose source would not likely be nearby in this cratonic region which was stable throughout the Mesoproterozoic. It has also been proposed that marine impact events could provide sufficient energy for tsunamis that could deposit allochthonous material ripped up from the seafloor [Oberbeck et al., 1993]. Well known examples include the breccias from the Alamo [Morrow et al., 2005], Chicxulub [Smit,

1999], and Lockne [Sturkell et al., 2000] marine impact events. Clasts deposited by ballistic sedimentation can also be a significant component of an impact event, emplaced by an expanding ejecta curtain in the shape of an inverted cone, with clast size fining away from the impact [Melosh, 1989]. Depending on the size of the trigger impact, large clasts can be ejected long distances, contributing to the clasts transported by the tsunami.

Multi-sequence beds [Dypvik et al., 1996], high energy deposition, and the large lateral extent of deposits that can be generated by an impact tsunami match the characteristics of the TB, and therefore, the remainder of this thesis will specifically attempt to determine 38

whether or not the tsunami that deposited the Tawaz Breccia was induced by a marine impact event.

Summary

Most correlation analyses from this study support deposition of the TB by tsunami. Since tsunamis can be generated by non-impact and impact mechanisms, additional analysis is needed to determine whether or not the TB contains a meteoritic

component indicative of an impact event. 39

CHAPTER 3: RHENIUM-OSMIUM ANALYSIS OF THE TAWAZ BRECCIA

Introduction

In order to determine if a stratigraphic horizon is associated with an impact event, unequivocal impact indicators; i.e. remnant impactor fragments, shatter cones, petrographic shock effects, and extraterrestrial geochemical markers must be identified

[French, 1998]. No impactor fragments or shatter cones were located (limited samples) in the Tawaz Breccia (TB) and in order to locate shocked quartz, large amounts (tens of kilograms) of sample must be dissolved or thin sections made. Samples of the TB were too small for either acid dissolution or production of thin sections. Of these, geochemical markers are useful because they can indicate the presence of an impactor far from an impact site [Ryder, 1996], even when only preserved in very small concentrations.

Rhenium-osmium (Re-Os) isotopic analyses have been used to identify meteoritic contributions in crustal materials. Meteorites are enriched in Re and Os relative to crustal materials because they source from the mantle or core of disrupted parent bodies that are concentrated in Re and Os during differentiation [Warren et al., 1999] (stony-iron, iron,

and achondrites), or are undifferentiated (Re and Os not ubiquitous in chondrites). Re-Os

analyses have been used on impact-melt rocks and lithic clasts from within the outer

crater ring of the Chicxulub multi-ring impact basin [Gelinas et al., 2004], as well as on the K-T boundary clay [Luck and Turekian, 1983; Lichte et al., 1986; Meisel et al., 1995;

Muñoz-Espadas et al., 2003]. They have also been used on impact-melt rocks from the

Chesapeake Bay impact crater [Lee et al., 2006]. Since the Re-Os system was able to 40 detect impactor contributions in these impact horizons and craters it is suitable for detecting a meteoritic component in the Tawaz Breccia.

Concentrations (in parts per billion, ppb) of Os and Re are useful for identifying a meteoritic component in crustal materials. If abundances of Re and Os in the TB are significantly higher than in average crust, they may be indicative of the collision of a large asteroid or comet at the Earth’s surface. Osmium, a platinum group element, is relatively depleted in crustal materials (<0.050 ppb [Morgan and Lovering, 1967; Walker et al., 2002]). This depletion occurred during core and mantle formation when metals, sulfides, and silicates separated due to elemental partitioning [Chou et al., 1983], and highly siderophile elements (HSE’s: including Re and Os) were largely differentiated from the (still-forming) crust and mantle of the Earth and concentrated in the core

[Warren et al., 1999]. In order to be compatible, elements must have the correct charge and atomic radii to combine with a host mineral [Hiraga and Kohlstedt, 2009]). In a magmatic system, Re (charge +4, atomic radii 0.65Å) is incompatible with most minerals, such as olivine [Warren et al., 1999], and becomes enriched in the melt.

Conversely, Os (charge +4, atomic radii 0.69Å) is compatible with newly-formed cumulates, such as olivine [Warren et al., 1999], that settle out of the melt. Little Re and

Os is present in the continental crust (due to fractionation) compared to the mantle or core. However, the continental crust formed from upper mantle melt that was concentrated in Re (incompatible) relative to Os (compatible), resulting in high concentrations of Re relative to Os. In the mantle, less Re and Os have been lost to the core (relative to crust) so Re and Os are more enriched in the mantle than the crust. 41

Finally, due to this early differentiation, the core has the majority of highly siderophile

elements (HSEs) (due to their affinity for iron, and density) and therefore has the highest

concentrations of Re and Os [Warren et al., 1999].

Osmium (a HSE) is, however, concentrated in three of the four primary meteorite types: chondritic, iron, and stony-iron [Koeberl and Shirey, 1997; Koeberl et al., 2002], all of which have Fe-Ni phases. Chondrites are enriched in Os because they have not been differentiated (undifferentiated abundances), while iron and stony-iron are enriched because they source from the core or core mantle boundary (respectively) of a differentiated body (concentrated in compatible elements such as Os) that has been disrupted. For chondritic impactors, typical Os concentrations are about 600 ppb [Gelinas et al., 2004]. Chondritic sources are of particular importance because they make up more than 85% of all observed meteorite falls [Grady, 2000], thus a significant number of

potential impactors are chondritic [Tagle and Berlin, 2008]. In contrast to chondrites,

stony-iron meteorites have average Os concentrations of 2212 ppb [Chen et al., 2002;

Shen et al., 1998] and iron meteorites 17826 ppb [Shen et al., 1996]. The fourth type,

achondrites, on the other hand, represent differentiated crustal or mantle material from

disrupted or impacted parent bodies and are thus relatively depleted (Os = 2.6 ppb) in

HSE’s [Mason, 1979].

Re is a siderophile element that can be used to identify meteoritic contributions in

a similar way to Os concentrations. Re is enriched in the crust relative to Os (see above)

with average concentrations of 0.5 ppb [Esser and Turekian,1993], but even more

enriched in the Earth’s mantle (55 ppb [Walker et al., 1994]) and core (see analog iron 42

meteorite samples above) due to fractionation. Achondritic, stony-iron, and iron

meteorites have average Re concentrations of 19.3 ppb [Mason, 1979], 167 [Chen et al.,

2002; Shen et al., 1998], 1305 [Shen et al., 1996], respectively. These values represent

material from the crust/mantle, core/mantle boundary, and core, respectively. Chondrites

also have high Re values (70 ppb [Walker et al., 2002]) since they originate from

undifferentiated bodies (no Re lost to the core).

Along with Re and Os concentrations, Re and Os isotopes are useful for

identifying the presence of meteoritic components in crustal materials. This is because

small meteoritic contributions (concentrated in Re or Os) can change the isotopic ratio of

crustal materials. The Re-Os system is based on the β-decay of 187Re into 187Os (half-life of 42.3±1.3 Ga [Lindner, 1989]) and 6 other stable Os isotopes. Of these stable isotopes, the current concentration of 188Os (some studies use 186Os, but 188Os is preferred because

more is produced during the decay of 187Re, and therefore 188Os is easier to measure) can be compared to current concentrations of 187Re and 187Os to determine the initial Os

isotopic 187Os/188Os ratio of a set of samples with the same age [Koeberl and Shirey,

1997]. Since crustal rocks have high Re values (relative to Os), the 187Re/188Os ratio

remains high because the decay of 187Re produces new Os isotopes slowly (half life 42

Ga). Thus continental crust has an average 187Re/188Os ratio of 34.5 [Peucker-Ehrenbrink

and Jahn, 2001]. Conversely, chondrites have higher concentrations of Os than Re

(undifferentiated abundances) so their 187Re/188Os ratios are lower (0.411) than continental crust [Walker et al., 2002]. 187Re/188Os ratios are 0.32 for stony-irons [Chen et al., 2002; Shen et al., 1998] and 0.8 for iron meteorites [Shen et al., 1996], also lower 43

than continental crust since they source from the core (or core boundary) where Os

concentrations are higher than Re (Os and Re are both very high in the core, but Os is

more abundant due to pre-differentiation concentrations). Achondrites are representative

of either crustal or mantle material (or combinations thereof) and should approximate that

value, however the average achondrite value (0.343 [Mason, 1979]) is much lower,

possibly due to incomplete or inefficient mixing during differentiation (Re and Os remain

in the crust at higher values than expected) [Day et al., 2009].

Another way to determine if a crustal sample is enriched beyond normal values is to use the ratio of 187Os to 188Os. For a crustal system, the initial concentration of Re is

high relative to Os, so the ratio of 187Os to 188Os increases as 187Re decays to Os isotopes). This results in continental crust with an average ratio of about 1 [Koeberl and

Shirey, 1997]. However meteorites contain less Re than Os [Gelinas et al., 2004] (unlike continental crust which has more Re than Os due to partial melting of the mantle) so the

187Os/188Os ratio is not as strongly affected by the conversion of 187Re into Os isotopes.

This results in a meteoritic average (chondrites, achondrites, stony-iron, and iron) ratio of

about 0.17 [Walker et al., 2002; Day et al., 2009; Chen et al., 2002; Shen et al., 1998;

Shen et al., 1996]. Both the Earth’s mantle and meteorites can contribute to enrichment,

but mantle rocks have relatively lower concentrations of Os (3.5 ppb [Chou et al., 1983])

than meteorites (resulting in higher 187Os/188Os ratios). On the other hand, higher concentrations of Os can be found in chondrites, stony-iron, and iron meteorites.

Therefore, a much larger mantle contribution would be required to produce the same

187Os/188Os ratio as high as that from a meteoritic contribution. It should be noted that 44

even though impactor contributions are normally much less than 1 wt.% [Koeberl, 1998]

this is enough to change the 187Os/188Os ratio.

Methods

Analyses were performed on 17 samples previously collected from the TB in

Mauritania (West Africa) by Kah [2008] in the vicinity of Atar (Figure 1, Supergroup 1)

(Figure 12). Initial XRD analyses were used for mineralogic characterization of the TB and to determine if any samples were atypical or unlikely to be suited for Re-Os analysis due to the presence of quartz (see XRD Analysis below). From the initial 17, four were

selected for Re-Os analysis to determine if the TB contained an impact component.

Sample Preparation

Initial sample preparation for XRD and Re-Os analyses is the same, and exacting

contamination prevention steps must be taken. Very small amounts of dust or sample

fragments can cause contamination when geochemical detection limits are parts per

billion (ppb) and parts per trillion (ppt). In order to prevent sample contamination, all

metal objects (watches, jewelry) were removed and hands were washed thoroughly (a

plastic bag was placed over the metal faucet handle to prevent contact). Each sample was

photographed, described, weighed and catalogued. Each rock was scrubbed (with a

different brush for samples with and without clasts (TB), or from different formations)

and then placed in a sealed labeled plastic bag. The plastic sample preparation table was

cleaned (with paper towels and dilute Lysol) three times between each handling of a

single sample.

Next, samples were crushed with a rock hammer one at a time on a cement block 45

Figure 12. Relative stratigraphic positions and descriptions of the 13 TB samples from ATJ location near Atar (not to scale). All samples are composed of micritic limestone, 1228B also contains quartz. Four other samples from the SERIZE location, AT98-34B, AT98-37A-3, AT98-37B-2, AT98-38A-1 were

(spray painted orange so concrete contaminants can be removed – see below). Samples were positioned between five to ten layers of brown butcher paper (on each side) to minimize contact with the rock hammer and concrete during crushing. Rock specimens that penetrated the paper were removed and discarded. Approximately 10-20g of granule- 46

sized (or smaller) material was collected following crushing, and new paper was used for each new sample. Back in the lab, this finer material was then placed in a plastic tray.

Any remaining orange paint, concrete, or other non sample contaminants (such as butcher paper) were removed using a wooden toothpick and plastic tweezers while viewing the specimen through a binocular reflected light microscope.

The remaining sample was weighed and powdered to clay/silt with an agate mortar and pestle. A porcelain mortar and pestle is not suitable since clay can retain

measurable amounts of trace elements. After each sample was powdered and weighed it

was placed in a plastic vial and labeled. Amounts of sample ranged from 9-14g of

crushed material. First the original sample vial was shaken for one minute and then

stirred for one minute (using circular and vertical motions) to ensure heterogeneous

mixing. The sample was then poured onto a clean piece of paper and divided into three

aliquots with a target of 3g or more for each vial (one for XRD analyses, one for Re-Os

analyses, and one placed in reserve at the Ohio University Planetary Geology Laboratory)

weighed, and labeled. The reserve material was returned to the original vial and kept as a

backup in case any samples were lost or contaminated.

XRD Analyses

XRD was used to characterize the 17 samples from Mauritania, to determine the

nominal mineralogy of the TB. XRD was also used to search for minerals indicative of a

meteoritic component: olivine and pyroxene, (chondrites or achondrites) as well as

kamacite, taenite, troilite, and graphite, (iron or stony-iron) and to assist in selection of

samples for Re-Os analysis. 47

XRD is a process where X-rays (produced when a stream of high speed electrons

hit a metal target) are directed to collide with a sample. When X-rays diffract they obey

Bragg’s Law, and behave as a reflection. Bragg’s law shows that diffraction occurs when; n*λ=2dhkl*sinθ. Where n is the order of reflection, an integer usually set at 1, λ is wavelength, d is the distance between the atomic planes hkl in a crystal, and θ is the

incidence (or reflection) angle. Thus, when X-rays strike a powdered sample (which

contains a virtually infinite number of randomly oriented micro crystals) many crystals

satisfy the Bragg Law, and X-rays are diffracted. Counts of the amount of diffraction are

plotted for each measured angle (2 theta or 2θ = 2*angle of reflection) ranging from 20º

to 80º (covering the important peaks for the minerals of interest) for these samples and

are recorded by a diffractometer. Since different crystals have unique d spacing and the

counts are a measure of how many atoms have that specific d spacing, these values can

be compared to known reference sets. Peaks in XRD spectra were compared to those

from a database of X-ray powder diffraction files, available through the International

Center for Diffraction Data or ICDD. These values are usually given as 2θ values (2θ is

divided by two for use in calculating d with the Bragg Law) [Perkins, 2002].

All seventeen samples (Figure 12) were analyzed using XRD. In addition, 3

repeat analyses were run on previously analyzed samples to ensure that the XRD machine

was still properly calibrated and not changing over time. XRD analyses show that the

Tawaz Breccia is comprised primarily of calcite and lesser amounts of dolomite and

quartz. Samples with less detrital quartz are more suitable for Re-Os analysis since they

may indicate deeper and therefore calmer water. Since a majority of oceanic silica is 48

sourced from the continental crust [Tréguer et al., 1995], shallower and less calm waters

close to the shore tend to have more quartz. However, quartz can also be diagenetically

formed in the marine environment [Sharma, 1968] so the presence of quartz does not

eliminate the possibility of a deep calm environment. Sample number 1228B was

excluded from Re-Os analyses because it contains quartz of unknown origin. Since the

TB has multiple fining upward sequences of breccia, the tops of these sequences indicate

periods of lower energy between breccia deposition events. These calmer periods are a

more likely time to concentrate slowly settling impact components [Bourgeois et al.,

1988].

Re-Os (and Os Isotope Analysis)

Four (of the total 17) samples were selected for Re-Os isotopic analyses. Two of

these four were selected as background samples because they were collected from above

or below the TB. These locations are the least likely to have been influenced by a

possible impactor and were analyzed in order to determine what background Os and Re

concentrations and isotopic ratios should be. ATJ-1211 was from the ball and pillow

structures at the base of the TB, whereas ATJ-1224 was sampled from above the TB

(Figure 12). The remaining two samples were selected from locations that were most likely to have elevated Os and Re concentrations and low (meteoritic) isotopic ratios.

Since depositional energy dissipates with time, slow settling of sediments (after the high

energy event) allows for concentration of impactor components (if they are present) near

the top of a fining upward sequence in marine sediments [Bourgeois et al., 1988]. 49

Therefore, the final two samples (ATJ-1217 and ATJ-1219) were selected since they

were sampled from the top of breccia sets within the TB (Figure 12).

Samples chosen for analysis were sent to Rich Walker at The University of

Maryland. First, these samples were spiked by the addition of a specific amount of enriched isotope (190Os and 185Re) for a process called isotope dilution analysis [Walker,

2010; EPA, 2007]. When the spike is added to the sample, it alters the isotopic ratios of the sample. Given the initial isotopic abundance of the sample and spike, how much spike was added to a known amount of sample, spike concentration, and the new isotopic ratio, the concentration of the element of interest in the sample can be calculated with high precision [EPA, 2007]. Next, the samples were digested for at least 2 days in reverse aqua regia (HCl:HNO3 = 1:3) in Carius tubes (sealed thick walled Pyrex tubes) at 230 °C.

Separation/purification of Os was then performed by solvent extraction [Shirey and

Walker, 1995; Gelinas et al., 2004; Lee et al., 2006; Walker et al., 2002] and Os was

analyzed by negative thermal ionization mass spectrometry (N-TIMS) [see Walker et al.,

2002 for additional details]. N-TIMS involves vaporizing the purified sample (Re and Os

done separately) by heating in a vacuum and analyzing it with a 30cm radius of

curvature, 68º sector mass spectrometer (The Bobcat 1, at the University of Maryland).

Re was measured using an electrometer and a Faraday cup, and Os was measured using a

Faraday cup and a 17-stage electron multiplier [Walker et al., 1994].

Results

The results of XRD (Figure 13) show that the majority of the samples are calcite

(one contains quartz) and appropriate for Re-Os analysis. No minerals were identified 50

that were indicative of a meteoritic component (e.g. olivine, pyroxene, kamacite, taenite, troilite, or graphite).

Concentrations of Os in the four TB samples analyzed range from ~0.004 to 0.008 ppb (Table 6 and Figure 14), much lower than normal chondritic values (~600 ppb

[Gelinas et al., 2004]), stony-irons (2212 ppb [Chen et al., 2002; Shen et al. 1998]), iron

meteorites (17826 ppb [Shen et al. 1996]), and achondrites (2.6 ppb [Mason, 1979]).

Table 6. Re-Os concentrations and isotopic ratios

ATJ-1211 187Os/188Os ratio is erroneous, this ratio is not possible and indicates that Re was lost (or Os was added) sometime in the samples history.

Furthermore, the Os concentrations of the TB are even lower than typical continental crust and oceanic crust Os values on Earth (<0.05 ppb Os [Morgan and

Lovering, 1967; Walker et al., 2002]) and average marine sedimentary rocks (0.831 ppb

[Rooney et al., 2010; Cohen et al., 1999; Dalai and Ravizza, 2006]. However Os concentrations are within known ranges of ocean island basalts, oceanic crust, and fluvial sediments (Figure 14). 51

Figure 13. XRD: All 17 Mauritania samples, ATJ-1211 at actual position, each offset by +400 count. Prominent peak (all samples) is calcite, minor dolomite peaks in 1220, 1228B, and 34B, and a quartz peak in 1228B. 38A, 37B, 37A, and 34B are from a different section than the other samples, and were not used for Re-Os analyses. 52

Figure 14 53

Figure 14 (previous page). Os and Re concentrations in parts per billion (ppb) and isotopic ratios for 187Os/188Os and 187Re/188Os, for various terrestrial and extraterrestrial sources. Points represent averages, and bars show total ranges of known values. Chondritic [Walker et al., 2002], stony-iron [Chen et al., 2002; Shen et al.,1998], iron (ppb) [Shen et al., 1996], iron (ratios) [Shen et al., 1996], achondrites [Mason, 1979] except for 187Os/188Os [Day et al., 2009], K-T Boundary [Koeberl and Shirey, 1997] except for Re [Frei and Frei, 2002], Ivory Coast Tektites (Bosumtwi) [Koeberl and Shirey, 1997], ocean island basalt [Becker, 2000], Mid Ocean Ridge [Blusztajn et al., 2000]. Continental crust (Os low) [Esser and Turekian, 1993] other Os (high and mean) [Morgan and Lovering, 1967], Re low [Walker et al, 1991] other Re (high and mean) [Esser and Turekian, 1993], 187Os/188Os [Koeberl and Shirey, 1997] and 187Re/188Os [Peucker-Ehrenbrink and Jahn, 2001]. Mantle [Walker et al., 1994] except for Os average [Chou et al., 1983]. Marine sediments; averages of shale [Rooney et al., 2010], mudrock [Cohen et al., 1999], and carbonates (Os) [Dalai and Ravizza, 2006]. Fluvial sediments; averages of paleo [Chesley et al, 2000] and modern [Singh et al., 2003]. Shaded area represents TB sample ranges.

Re concentrations for the TB range from 0.004 to 0.057 ppb (Table 6), these values are within the range of impact mixtures, ocean island basalts, and fluvial sediments (Figure 14). Of these concentrations, only the Ivory Coast tektites have an average that falls within the range of the TB (0.0097 ppb [Koeberl and Shirey, 1997]), while the K-T boundary, ocean island basalts and fluvial sediments have averages that are higher; 0.243 [Frei and Frei, 2002], 0.399 [Becker, 2000], and 0.200 [Chesley et al,

2000; Singh et al., 2003] ppb, respectively (Figure 14).

187Os/188Os ratios of the TB samples range from 0.27 to 0.9 (Table 6), correlating

with the average for stony-iron (0.30 [Chen et al., 2002; Shen et al. 1998]) and within

known ranges for iron meteorites, ocean island basalts, mantle, oceanic crust, continental

crust, marine sediments and fluvial sediments (Figure 14). 54

187Re/188Os ratios for the TB range from 4.76 to 78.06 (Table 6), correlating with

the averages for the K-T boundary, continental crust, and fluvial sediments; and within

known ranges for ocean island basalts, oceanic crust and mantle (Figure 14).

Discussion

Low Os and Re concentrations demonstrate that, if an impactor component is

present, then it is very minimal and has been diluted by mixing with crustal rocks.

Furthermore, these concentrations are too low to allow impactor contributions to be distinguished from terrestrial sources of Re and Os. Re concentrations and 187Re/188Os ratios of the TB correspond to impact mixtures (Figure 14), as well as the ranges of ocean island basalts, fluvial sediments and others. Because of this overlap, the specific source of

Re (or 187Re/188Os) cannot be determined. For 187Os/188Os, the ratio of the TB does

intersect iron and stony-iron meteorites, but this signal also corresponds to ocean island

basalts, oceanic crust, continental crust, mantle, marine sediment and fluvial sediment

ranges, so again meteoritic contributions cannot be unequivocally confirmed.

In the marine environment, the majority of Os is contributed by fluvial sources

(70%; of which 5% is from ocean island basalts and the rest from continents), meteoritic

material (14%), leeching of ultramafic rocks in the oceans (16%), and a negligible

amount from aeolian (wind born) dust [Levasseur et al., 1999]. Little is known about

sources of marine Re (especially in carbonates). Since fluvial sediments contain minor Re

and Os contributed from continental sources [Esser and Turekian, 1993], it is possible that the Re and Os present in the Tawaz Breccia is a mixture of components; contributed by continental fluvial sources into the epicontinental Taoudenni Basin. 55

However, the possibility of an impact mixture cannot be excluded because an impact mixture could also produce the low Re and Os concentrations and isotopic ratios determined for the TB. If the TB is an impact mixture, then the 187Os/188Os ratio corresponds to stony-iron and iron meteorites, while the 187Re/188Os ratio corresponds to the impact horizon at the K-T boundary, a collision which was produced by a chondritic impactor [Kyte, 1998; Trinquier et al., 2006]). Furthermore, the Re concentrations correlate with the chondritic K-T boundary concentrations and the Ivory Coast Tektites

(an iron or chondritic impactor [Koeberl and Shirey, 1997]). Os concentrations of the TB do not correlate with meteorites or impact mixtures, but this low range may be a result of dilution of impact materials. It is also possible that the modern micrometeorite flux

(background flux to the Earth) is higher [Goswami and Lal, 1977] than it was in the

Mesoproterozoic, which could explain why the TB samples have low values relative to modern values. Since the 187Os/188Os ratio suggests stony-iron or iron while the Re concentrations suggests chondritic or iron, perhaps the correlation of iron meteorites in both suggests that if the TB is an impact mixture then the Re and Os could be sourced from an iron meteorite.

Of the samples used (Figure 12), two (ATJ-1217 and ATJ-1219) were selected from the tops of fining-upward sequences in the Tawaz Breccia, which should be concentrated in impactor components by settling (through a water column) [Bourgeois et al., 1988]. Two others were analyzed, and predicted to have lower concentrations; one near the top (ATJ-1224) and another near the bottom (ATJ-1211) of the TB. However, Os was most concentrated in ATJ-1211 (the ball and pillow structures at the base of the TB), 56

while 187Os/188Os was the highest in ATJ-1219 (a fluid aligned breccia at the top of a

fining-upward sequence). Conversely, Re and 187Re/188Os were the highest in ATJ-1224

(a breccia at the top of the TB). It is unclear why Re and Os concentrations and the

187Re/188Os ratios are not the highest near the tops of fining upward sequences, however

the differences in concentrations are very small from sample to sample, and no clear

impact horizon is apparent. Since this study only used four samples from field

reconnaissance, future Re-Os analyses should be of samples from a more thoroughly- defined stratigraphic section of the TB. Cores have also been drilled in locations that should intersect the TB, analyses and sampling of fining-upward sequences in these cores could also be useful. These new samples combined with analyses of samples known to not be affected by the TB event (above and below) and samples of other marine carbonates from around the world (in order to determine the normal marine carbonate, meteoritic input) would allow for better measure of baseline Mesoproterozoic Re and Os concentrations and isotopes. Samples have been prepared from The Belt Supergroup

(Helena Formation, 1.44-1.45 Ga) recently collected by the author, as well as samples from the Beck Springs Dolomite, (0.9-1.1 Ga), The Bylot Supergroup, Baffin Island

(Victor Bay, and Athole Point Formations, 1.2 Ga) and samples from India (The

Sarangarh also known as the Charmuria Limestone, late Mesoproterozoic).The

combination of baseline, core, and more targeted TB samples could lead to possible

confirmation of an impact horizon in the TB. 57

CONCLUSION

Correlation analyses #1 and #2, along with a qualitative analysis of breccia types

(and their distinguishing characteristics) demonstrated that the TB was likely formed by a tsunami mechanism. In order to distinguish between impact tsunami and non-impact tsunami, Re-Os analyses were used to determine if impactor components were present.

Preliminary analyses have determined that if impactor components are present, then they have been diluted below recognizable concentrations and ratios; but that impact mixtures

(possibly from an iron meteorite component) cannot be eliminated as a possibility. 58

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46 Sangster, D.F. (1988), Breccia-Hosted Lead-Zinc Deposits in Carbonate Rocks in Paleokarst, eds: Choquette, P.W., and James, N.P., Springer-Verlag, 102-116.

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47 Desrochers, A. and James, N.P. (1988), Early Paleozoic Surface and Subsurface Paleokarst: Middle Ordovician Carbonates, Mingan Islands, Québec, Rocks in Paleokarst, eds: Choquette, P.W., and James, N.P., Springer-Verlag, 183-210.

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49 Sando, W.J. (1988), Madison Limestone (Mississippian) Paleokarst: A Geologic Synthesis, in Paleokarst, eds: Choquette, P.W., and James, N.P., Springer-Verlag, 256- 277.

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52 Blair, T.C., and McPherson, J.G. (1999), Grain-Size and Textural Classification of Coarse Sedimentary Particles. Journal of Sedimentary Research 69, 6-19.

53 Sugawara, D., Minoura, K., and Imamura, F. (2008), Tsunamis and Tsunami Sedimentology, Ch. 3 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 9-49.

54 Gelfenbaum, G., and Jaffe, B. (2003), Erosion and Sedimentation from the 17 July, 1998 Papua New Guinea Tsunami. Pure appl. geophys. 160, 1969-1999.

55 Fujiwara, O. (2008), Bedforms and Sedimentary structures Characterizing Tsunami Deposits, Ch. 4 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 51-62.

56 Nanayama, F. (2008), Sedimentary Characteristics and Depositional Processes of Onshore Tsunami Deposits: An Example of Sedimentation Associated with the 12 July 1993 Hokkaido-Nansei-Oki Earthquake Tsunami, Ch. 5 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 63-80.

57 Goto, K., Imamura, F., Keerthi, N., Kunthasap, P., Matsui, T.,, Minoura, K., Ruangrassamee, A., Sugawara, D., and Supharatid, S. (2008), Distribution and Significance of the 2004 Indian Ocean Tsunami Deposits: Initial Results from Thailand and Sri Lanka, Ch. 7 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 105-122.

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58 Cita, M.B. (2008), Deep-Sea Homogenites: Sedimentary Expression of a Prehistoric Megatsunami in the Eastern Mediterranean. Ch. 12 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 185-202.

59 Shiki, T., and Cita, M.B. (2008), Tsunami-Related Sedimentary Properties of Mediterranean Homogenites as an Example of Deep-Sea Tsunamiite. Ch. 13 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 203-215.

60 Albertão, G.A., Martins, Jr., P.P., and Marini, F. (2008), Tsunamiites-Conceptual Descriptions and a Possible Case at the Cretaceous-Tertiary Boundary in the Pernambuco Basin, Northeastern Brazil. Ch. 14 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 217-250.

61 Goto, K., Tada, R., Tajika, E., and Matsui, T. (2008), Deep-Sea Tsunami Deposits in the Proto-Caribbean Sea at the Cretaceous/Tertiary Boundary. Ch. 15 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 251-275.

62 Scheffers, A. (2008). Tsunami Boulder Deposits. Ch. 17 from: Tsunamiites Features and Implications ed. Shiki, T., Tsuji, Y., Yamazaki, T., and Minoura, K., Elsevier, New York, 299-317.

63 Dypvik, H., Gadlaugsson, S.T., Tsikalas, F., Attrep, Jr., M. Ferrell, Jr., R.E., Krinsely, D.H., Mørk, A., Faleide, J.I., and Nagy, J. (1996), Mjølnir structure: An impact crater in the Barents Sea, Geology 24, 779-782.

64 Osinski, G.R., Spray, J.G., and Grieve, R.A.F. (2008), Impact melting in sedimentary target rocks: An assessment, in Evans, K.R., Horton, J.W., Jr., King, D.T., Jr., and Morrow, J.R., eds. The Sedimentary Record of Meteorite Impacts: Geological Society of America Special Paper 437, 1-18.

65 Levson, V.M., and Rutter, N.W. (1988), A lithofacies analysis and interpretation of depositional environments of montane glacial diamictons,Jasper, Alberta, Canada. In, Classification of Glacigenic Deposits, (eds.) Goldthwait & Matsch, Balkema, Rotterdam, 117-140.

66 McMurtry, G.M., Fryer, G.J., Tappin, D.R., Wilkinson, I.P., Williams, M., Fietzke, J., Garbe-Schoenberg, D., and Watts, P. (2004), Megatsunami deposits on Kohala volcano, Hawaii, from flank collapse of Mauna Loa. Geology 32, 741-744.

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67 Anderson, S.P., and Anderson, R.S. (1990), Debris-flow benches: Dune-contact deposits record paleo-sand dune positions in north Panamint Valley, Inyo County, California. Geology 18, 524-527.

68 Blenkinsop, T.G. (2007), Auriferous breccia, Kainantu, Papua New Guinea. Journal of Structural Geology 29, 923.

69 Blenkinsop, T.G., Oliver, N.H.S., and Cihan, M. (2007), Quantifying Fabric Anisotropy in Breccias: Insights Into Brecciation in a Mineralizing Environment. In: Eos Transactions, American Geophysical Union, Fall Meeting Supplement MR21A-05. American Geophysical Union, 52.

70 Burnham, K. (2009), Predictive model of San Andreas fault system paleogeography, Late Cretaceous to early Miocene, derived from detailed multidisciplinary conglomerate correlations. Tectonophysics 464, 195-258.

71 Wendorff, M. (2005), Sedimentary genesis and lithostratigraphy of Neoproterozoic megabreccia from Mufulira, Copperbelt of Zambia. Journal of African Earth Sciences 42, 61-81.

72 Gianfreda, F., Mastronuzzi, G., and Sanso, P. (2001), Impact of historical tsunamis on a sandy coastal barrier: an example from the northern Gargano coast, southern Italy. Natural Hazards and Earth System Sciences 1, 213-219.

73 Laznicka, P. (1988), Breccias and coarse fragmentites, Developments in Economic Geology 25, Elsevier, New York, 1-832.

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75 von Engelhardt, W. (1997), Suevite breccia of the Ries impact crater, Germany: Petrography, chemistry and shock metamorphism of crystalline rock clasts. Meteoritics & Planetary Science 32, 545-554.

76 von Engelhardt, W. (1990), Distribution, petrography and shock metamorphism of the ejecta of the Ries crater in Germany—a review. Tectonophysics 171, 259-273.

77 Curtis, G.H. (1954), Mode of Origin of Pyroclastic Debris in the Mehrten Formation of the Sierra Nevada. University of California publications in geological sciences 29, 453-502.

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78 Johnson, K.S. (2008), Evaporite-karst problems and studies in the USA. Environ Geol 53, 937-943.

79 Martinez, J., Johnson, K., and Neal, J. (1998), Sinkholes in Evaporite Rocks. American Scientist 86, 38-51.

80 Freundt, A., Wilson, C.J.N., and Carey, S.N. (2000), Ignimbrites and Block-And- Ash Flow Deposits, in Encyclopedia of Volcanoes: eds. Sigurdsson, H., Houghton, B., McNutt, S.R., Rymer, H., and Stix, J., Academic Press, New York, 581-599.

81 McBride, E.F. (1962), Flysch and Associated Beds of the Martinsburg Formation (Ordovician), Central Appalachians. Journal of Sedimentary Petrology 32, 39-91.

82 Middleton, G.V. (1961), Evaporite Solution Breccias from the Mississippian of Southwest Montana. Journal of Sedimentary Petrology, 31, 189-195.

83 Morton R.A., and Sallenger, Jr., A.H. (2003), Morphological Impacts of Extreme Storms on Sandy Beaches and Barriers. Journal of Coastal Research 19, 560-573.

84 Hinchliffe, S., Ballantyne, C.K., and Walden, J. B. (1998), The Structure and Sedimemntology of Relict Talus, Trotternish, Northern Skye, Scotland. Earth Surface Processes and Landforms 23, 545-560.

85 Dussauge-Peisser, C., Helmstetter, A., Grasso, J.R., Hantz, D., Desvarreux P., Jeannin, M., and Giraud, A. (2002), Probabilistic approach to rock fall hazard assessment: potential of historical data analysis. Natural Hazards and Earth System Sciences 2, 15-26.

86 Black, T.J. (2003), Evaportie Karst in Michigan. Oklahoma Geological Survey 109, 315-320.

87 Ormö, J., and Lindström, M. (2000), When a cosmic impact strikes the sea bed. Geol. Mag. 137, 67-80.

88 Brodie, K., Fettes, D., Harte, B., and Schmid, R. (2007), Recommendations by the IUGS Subcommission on the Systematics of Metamorphic Rocks Ch. 3 Structural terms imcluding fault rock terms, in Metamorphic Rocks: A Classification and Glossary of Terms, eds. Fettes, D., and Desmons, J., New York, 243p.

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90 Milton, D.J., and MacDonald, F.A. (2005), Goat Paddock, Western Australia: an impact crater near the simple-complex transition. Australian Journal of Earth Sciences 52, 1-8.

91 Hansen, W.R. (1986), History of Faulting in the Eastern Uinta Mountains, Colorado and Utah. Rocky Mountain Association of Geologists Symposium, 5-17.

92 Mitchell, N.C. (1996), Creep in pelagic sediments and potential for morphologic dating of marine fault scarps. Geophysical Research Letters 23, 483-486.

93 White, J.D.L., and Houghton, B.F. (2006), Primary volcaniclastic rocks. Geology 34, 677-680.

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76

APPENDIX B: BRECCIA SPREADSHEETS ll 62 characteristics present. present. ll 62 characteristics Analysis #1, no exclusions, a

77

14, and negative correlations, aracteristic (row) sums of 0, . ) 48 characteristics remain Analysis #1, exclusion of ch (

78

14, and negative correlations, aracteristic (row) sums of 0, . ) 56 characteristics remain Analysis #2, exclusion of ch (

79

14, negative correlations, and . ) aracteristic (row) sums of 0, 50 characteristics remain 50 characteristics (

, h and one low bin g one hi Analysis #2, exclusion of ch

80

14, negative correlations, and . ) 36 characteristics remain 36 characteristics aracteristic (row) sums of 0, (

, h and two low bins g two hi Analysis #2, exclusion of ch

81

14, negative correlations, and . ) 29 characteristics remain 29 characteristics (

, aracteristic (row) sums of 0, h and three low bins g three hi Analysis #2, exclusion of ch

82

14, negative correlations, and . ) 19 characteristics remain 19 characteristics ( aracteristic (row) sums of 0,

, h and four low bins g four hi Analysis #2, exclusion of ch

83

14, negative correlations, and . ) 14 characteristics remain 14 characteristics aracteristic (row) sums of 0, (

, h and five low bins g five hi Analysis #2, exclusion of ch

84

14, negative correlations, and . ) aracteristic (row) sums of 0, 7 characteristics remain (

, h and six low bins g six hi Analysis #2, exclusion of ch

85

APPENDIX C: TAWAZ BRECCIA SAMPLE DESCRIPTIONS AND PHOTOS

Note: all ATJ- samples collected from near Atar in 2003, all AT98- samples collected from SERIZE in 1998. All samples react to 5% HCl, appear sandblasted, and have saw marks. Samples weighed with a digital scale that has a limit of 80.4g. Analyses require more than 9.0g of sample. From thin-section work; samples are 99% clean fine-grained carbonate micrite or microspar, very little clay material, very few opaques (rare digenetic pyrite) [Kah, 2009]. From XRD; all samples dominated by calcite, minor dolomite peaks in 1220, 1228B, and 34B, and a quartz peak in 1228B.

ATJ-1211, micritic limestone, dominated by calcite, from ball and pillow structures near base of unit, way up unknown, 12.248g.

86

ATJ-1212, matrix supported breccia, dominated by calcite, tabular to rounded clasts, way up unknown, >80.4g.

87

ATJ-1213, micritic limestone, dominated by calcite, no clasts, way up known, 31.560g

88

ATJ-1214, matrix supported breccia, dominated by calcite, way up unknown, >80.4g.

89

ATJ-1215, micritic limestone, dominated by calcite, no clasts, white band, way up known, 72.050g.

90

ATJ-1217, micritic limestone, dominated by calcite, no clasts, top of fining up set (1212- 1217), way up known, 27.327g.

91

ATJ-1218A, molar tooth breccia, dominated by calcite, way up known, >80.4g.

92

ATJ-1218B, clast supported breccia, dominated by calcite, way up unknown, 55.272g.

93

ATJ-1219, matrix supported, fluid aligned breccia, dominated by calcite, tabular angular clasts, way up known, 51.322g.

94

ATJ-1220, agglomerated molar tooth breccia clast, dominated by calcite, some dolomite, way up unknown, 35.413g.

ATJ-1221, micritic limestone, dominated by calcite, no clasts, conical columnar stromatolites [Stagner, 2003] way up known, 25.195g.

95

ATJ-1224, molar tooth breccia, dominated by calcite, way up unknown, 50.403g.

96

ATJ-1228B, clast to matrix supported breccia, dominated by calcite, some dolomite, matrix different color; tan to brown (all previous grey), according to XRD only sample with quartz, cross-beds directly above; may indicate a barrier isle or beach facies [Stagner, 2003], way up unknown, 56.470g.

97

AT98-34B, no photo, powdered by previous lab worker, no un-powdered sample remaining, dominated by calcite, contains some dolomite, 15.865g.

AT98-36A, molar tooth breccia, dominated by calcite, way up known, 3.389g (too small for analyses).

AT98-37A, molar tooth breccia, dominated by calcite, way up known, 5.830g (too small for analyses). 98

AT98-37A-3, molar tooth breccia, dominated by calcite, way up known, 22.853g.

99

AT98-37B-2, molar tooth breccia, dominated by calcite, way up known, 15.458g.

AT98-38A, possible clast edges (breccia), dominated by calcite, way up known, 4.158g (too small for analyses).

100

AT98-38A-1, molar tooth breccia with stylolitic like contacts, dominated by calcite, way up unknown, 20.300g.

101

AT98-40A, clast supported breccia, dominated by calcite, angular clasts, way up unknown, 4.702g (too small for analyses).