A Diffuse Spectral Reflectance Library of Clay Minerals and Clay Mixtures Within the VIS/NIR Bands

A Diffuse Spectral Reflectance Library of Clay Minerals and Clay Mixtures within the VIS/NIR Bands

A thesis submitted to Kent State University in partial fulfillment of the requirements for the degree of Masters of Science

By Yvette Vlack December, 2008

Thesis written by Yvette A. Vlack B.A., Kent State University, 2004

Approved by

Joseph D. Ortiz 11-5-08 , Advisor

Daniel K. Holm 11-5-08 , Chair, Department of Geology

Timothy Moerland 11-5-08 , Dean, College of Arts and Sciences

TABLE OF CONTENTS

LIST OF FIGURES ...... IV LIST OF TABLES ...... VI ACKNOWLEDGEMENT ...... VII ABSTRACT ...... ERROR! BOOKMARK NOT DEFINED. CHAPTER 1: INTRODUCTION ...... 1 1:1 WHY SHOULD PEOPLE CARE ABOUT CLAY MINERALS ...... 1 A. The Role of Clay Minerals in Human Civilization – Past & Present ...... 1 CHAPTER 2: NATURE, STRUCTURE, AND CLASSIFICATION OF CLAYS ...... 6 2:1 OCCURRENCE AND GEOLOGY OF CLAYS ...... 6 2:2 NOMENCLATURE ...... 7 2:3 HOW ARE CLAYS MEASURED ...... 21 CHAPTER 3: RESEARCH PURPOSE AND OBJECTIVES ...... 24 CHAPTER 4: SAMPLE PREPARATION AND RESEARCH METHODOLOGY ...... 29 CHAPTER 5: DATA ANALYSIS ...... 37 5:1 PRINCIPLE COMPONENT ANALYSIS DEFINED ...... 40 5:2 STEPWISE LINEAR REGRESSION ANALYSES DEFINED ...... 40 5:3 TESTING THE LIBRARY AGAINST ITSELF ...... 42 5:4 TESTING THE LIBRARY AGAINST THE CORES ...... 45 CHAPTER 6: RESULTS AND CONCLUSIONS ...... 56

6:1 THE ASSUMPTIONS OF QXRD AND OTHER CHEMICAL ANALYSIS AS IT RELATES TO SEMI - QUANTITATIVE VERSUS QUANTITATIVE ANALYSIS ...... 58 CHAPTER 7: FUTURE RESEARCH ...... 64 REFERENCES ...... 67 APPENDIX A ...... 80 APPENDIX B...... 82 APPENDIX C ...... 84 APPENDIX D ...... 86 APPENDIX E...... 88

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LIST OF FIGURES

Figure

1 The T 2O5 structure ...... 10

2 Sheet structure ...... 11

3 Single Tetrahedron ...... 13

4 Single Octohedral...... 14

5 The Trioctahedral and Dioctahedral Layers ...... 16

6 LabSpec ® Pro FR spectrometer with High Intensity Contact Probe testing clay sample on a GF/F filter ...... 34

7 Samples air drying with PVC ring, top view ...... 35

8 Samples stored in E6 archival photography sheet ...... 36

9 Graphic presentation of the distribution difference between straight reflectance verses the first derivative using the Illite (I48W) library mixture with Chlorite (CCa) in the VIS range as the example...... 39

10 Millbrig core first component correlation of the VIS versus clay mineral library CCa-5_STx-95 and line of regression and R 2 ...... 46

11 Millbrig core third component correlation of the VIS versus clay mineral library and line of regression and R 2 ...... 47

12 Millbrig core third component correlation of the VIS versus iron bearing mineral from USGS, best fir was hematite and line of regression and R 2 ...... 48

13 MNK3 core first component correlation of the VIS versus clay mineral library, two mixtures with high correlation and line of regression and R 2 ...... 50

14 MNK3 core second component correlation of the VIS versus clay mineral library, three mixtures highly correlate, and line of regression and R 2. Dolomite gave the best R squared value ...... 51

15 MNK3 core third component correlation of the VIS versus iron bearing minerals from USGS, three mixtures highly correlate, and line of regression and R 2 ...... 52

16 DSR spectra of clay mineral library end members ...... 62

17 XRD 2D peaks of clay mineral library end members ...... 63

LIST OF TABLES

Table

1 Mineralogy Of Clays From Various Resources ...... 20

2 Testing The Library Against Itself ...... 44

3 Core Data Of Iron Content By XRF ...... 55

ACKNOWLEDGEMENT

It is only by the grace of God and the fact that “I can do all things through Christ who strengthens me” (Phil. 4:13) that this research and document has come to fruition. In the face of many obstacles, victory prevails by the work of His hands in my life (Ish. 40:31). Praise and glory be to God for his support during these studies and this research project that has led to the ability to complete this thesis. In addition, it is only through his miraculous grace that I have had the privilege of receiving such awesome mentors: mentors of tremendous wisdom and overwhelming support. Specifically, I want to thank Dr. Joseph D. Ortiz, my chief advisor, who knew just how far to push me and knew just when to encourage me, too; Dr. John T. Haynes for his Millbrig samples, which were essential for my research and his many hours on the phone teaching me the principles of clay minerals and XRD. In addition, I want to thank Dr. Earnest H. Carlson, who offered his wisdom about the structure of clays and XRD. I am truly fortunate to have such an awesome committee. Many thanks to Dr. Donna Witter for her insights; to Dr. Andrea Case for allowing me the use of the Geno Grinder; to Dr. B.

Brandon Curry for sharing his MNK3 samples and XRD/ICP data that was necessary for this project; to Beth Hart for her ‘cheerleading’ and the wonderful images she produced, which are included in this thesis; to Tiffany Saven who continuously uplifted me (Eccl 4:10). I want to thank Laura Keptner for doing a lot

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of typing and format revisions. Most importantly, when the world was tucked away warm in their beds, it was only Dr. Nancy K. Grant, that stayed by my side at all hours of the night assisting me to meet deadlines, reviewing me in statistics, and assisting in the editing and completion of many auxiliary projects (Prov. 27:17). I am forever indebted to so many people and organizations that have made this possible. In addition, many thanks are extended out to the National Science

Foundation GK12 Grant that funded this endeavor; to my GK12 advisors Dr.

Mandy Munro-Stasiuk, Dr. Scott Sheridan. Thank you Mary Lou Church in

Geography, she would do all my stipend reimbursement paperwork for GK12 and to Karen Smith who took care of all my orders and edited my proposal abstract, and to Merida Keats for her continuous assistance and maintenance with all the lab equipment. Thank you Ralph Smith for all your computer genius. I must also mention my parents, my many friends, my Bible study/family in Christ, and especially my Pastor and coach John R. Allen, all of these people have assisted me spiritually, emotionally, and financially. I am also very grateful for the many new friends and business associates that I have been introduced to over these past three years. They have been a blessing to my self esteem even though they may not realize the magnitude of their impact. I am truly grateful to these individuals (Carolyn

Green, Bill Houston, Gilbert Ankenbauer, Lynne Daniels, Akin Balogun, Mark

Hitchner, Glenn Myska, and Justice C. Percell) their enthusiasm in my abilities has given me the continuous drive to persevere, which in turn has encouraged me to pursue my dreams in the oil & gas/exploration and production industry. As a

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result, I am privilege to have been invited to be a part of the Baker Hughes (Baker

Atlas) team. I can truly say ‘my cup runneth over’ (Psl 23:5). My hopes and prayers go out to all the aforementioned, may the Lord reciprocate with multitudes of blessings pouring out over all of you. My only fear is that I may have missed someone. If I have, I extend my humblest apology.

SUMMARY

The versatility of diffuse spectral reflectance (DSR) was investigated as a complementary methodology to XRD and XRF when studying clay minerals in stratigraphic sequences. The Analytical Spectral Device (ASD) LabSpec® Pro FR UV/VIS/nIR spectrometer provides an innovative nondestructive methodology that is cost effective, portable, quick, and easy to use with samples in the lab or field. LabSpec ® Pro FR spectrometer and similar equipment are remarkable research tools underutilized in the area of clay mixtures. This study develops a new methodology that demonstrates the versatility of the LabSpec ® Pro FR and the use of DSR as a tool for generating a spectral library and then determining clay mineralogy of various core samples. Samples from two sources were evaluated: (1) sediment from core MNK3, from a slack water Pleistocene lake near St. Louis, in which stratigraphic changes in clay mineralogy occur down core, and (2) the Ordovician Millbrig K-bentonite (samples from AL, GA, KY, TN, and VA), an altered tephra in which the changes occur laterally in a single horizon. DSR spectral data is validated against XRD, ICP-MS, and XRF data. This spectral library was generated from four primary clays and clay mixtures, consisting over 231 two variable mixtures in 5% increments, by weighted percents and is augmented with spectra from the USGS spectral library. Clay mineral standards were obtained from the Clay Mineral Repository and Wards Natural Science. The aim is to close the gap that currently exists for an expanded spectral library of clay mixtures and explore the DSR variability of clay mixtures. PCA (Principal Component Analysis) was used to correlate the spectral data of the library with the two MNK3 and Millbrig sample sets. Stepwise Linear Regression (SLR) analysis was used with the composite library as an identification tool. By combining PCA analysis of unknowns with SLR against our clay mixture library, we identify our components in an objective, quantifiable way. The model predictors from the analysis gave highly significant R-squared values for the extracted PCA assemblages depending on component. One of the challenges was comparing the XRD clay percents against the predicted models. Frequently, the primary clay was predicted, but not the secondary clay. Basically, the result is an ordinal distribution of the amounts of minerals present in the mixture. Ordinal distributions, as non-parametric data, do not allow the computation of averages or proportions, but tell only relative amounts such as greater, greatest, and least. This may be because both cores represent a four component clay mixtures plus ancillary minerals, as opposed to the two component library. Predictability difficulties may also have been due to confounding factors such as the presence of iron-bearing minerals in the mixture causing what is termed by Balsam, 1999, as the ‘matrix effect’; Balsam also states that iron-bearing minerals such as hematite may be masked by illite and chlorite. The spectral clay mineral library is useful and the methodology pursued has proven successful. However, at this time there is no consistency in the predictability of the data. As a result, future research needs to eliminate intervening factors sequentially to determine various iron components and their impact on readings (Balsam et al, 1999).

CHAPTER 1: INTRODUCTION

1:1 Why Should People Care About Clay Minerals

A. The Role of Clay Minerals in Human Civilization – Past & Present

From household utensils to the cement of the Great Pyramid, the impact of clays in our life and history is awe-inspiring. Clays have been the basis for all pottery making; archeological discoveries reveal the incredible value of clays over time, and its role in the life of humans. In Mesopotamia, an area where stone was not plentiful, early Babylonian society utilized the durability of clays for building. Moreover, clays proved to the

Babylonians and other ancient civilizations to be of tremendous value as a means of preserving important records (Chiera, 1938; Bossuyt, et al, 2001). A pure and well- kneaded clay brick even unbaked will retain its shape and inscriptions for thousands of years (Chiera, 1938). Along the Euphrates River, over 25,000 tablets and cylinders were discovered, which dated back to nearly 3000 B.C. With regard to the archeologist, a great deal of knowledge has been preserved. Consequently, much knowledge will continue to be discovered because of the use of clays in ancient history (Write &

Chadbourne, 1965.

The role of clays in modern society is immense and continuously growing. Some example areas of their application include the agricultural trade such as evaluating soil moisture for plant growth and nutrient exchange. Other areas include manufacturing and

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the production of various pharmaceuticals such as medications and cosmetics. Industry and construction utilize clays as absorbers, fillers, refractory, cements, bricks and ceramics. Engineers must take into account clays for interpreting slope stability for construction. (Meunier, 2005; Grim, 1953; Nesse, 2000; Moore and Reynolds, 1989). To attempt to create a comprehensive list of all areas where clays have effected and are affecting human society would prove to be insurmountable and incomplete.

In academics and research, clays have a significant position. Researchers study clays as a means of interpreting past climates, paleoclimates (Meunier, 2005). The <2um fraction of the phyllosilicates minerals in marine sediments reveal to us the information of the composition and climate of terrains (Biscaye, 1965; Rateev, 1969). These minerals make up the main components of abyssal sediments and they are utilized as paleoclimate and paleooceanographic indicators (Petschick, et al, 1996). These minerals are the indicators of their continental derivation (Brass, et al, 1990). For example, the distribution of kaolinite in the world ocean basins correlates latitudinal, with the maximum quantities near the equator and a gradual decrease in content toward the poles.

(Rateev, M.A., et al, 1969).

As the geologist ascertains more and more about past climates, they in turn add insight as it relates to current issues such as climate change and climate variability as we look at our current climatic conditions. By utilizing stratigraphic data about clays, one can surmise past climate, marine circulation, tectonics, and other paleo-geographical information (Chamley, 1989). Mining geologists study hydrothermal areas to find economically valuable ore minerals, these areas are not always easy to find but occur in

conjunction with clay minerals (Nesse, 1991; Velde, 1995). In the oil and gas industry, clays play a catalytic role in the genesis of petroleum hydrocarbon along with acting as depositional screens (Osipov, et al, 2004; Grim, 1953). Their alteration provides crucial information about the potential generation of hydrocarbon reservoirs (Moore and

Reynolds, 1989). In hydrogeology the study of groundwater, clays can reveal the type of aquifers available and aid in the understanding of how groundwater flows (Fetter, 2001).

The structural geologist looks at the minerals and potential provenances to interpret potential past tectonic events (Chamley, 1989).

Clays have also become a source of controversy. The study of clay mineral assemblages, their distribution patterns and pathways, are also an effective proxy to reveal the sources and pathways of pollutants (Naidu, et al, 1995). Consider the article titled Northern Ocean Inventories of Organochlorine and Heavy Metal Contamination , the author Crane, et al, explain that, the four primary clay minerals (illite, chlorite, kaolinite, and smectite) absorb contaminants in the follow order: smectite>chlorite>illite>kaolinite. Since smectite has the greatest surface area, its absorption is the highest. Furthermore, the grain size is the most important factor that controls absorption and retention of contaminants, second is total organic carbon (TOC) content of sediments. The most important parameter controlling chemical adsorption is grain size. As the grain size of particles become smaller, the surface area increases logarithmically, and its ability to absorb chemicals increases accordingly (2001).

Likewise, Khim, 2003, states that, in marine and lacustrine environments, the non- carbonate sediments universally demonstrate a high positive correlation between TOC

and mean grain size. Since metals tend not to dissolve in solution but rather tend to adsorb onto particles, suspended particles can either be deposited in the ocean as bottom sediment or enter the food web via plankton (Crane, et al, 2001).

In the Arctic, Siberian rivers transport a tremendous amount of anthropogenic pollutants, including radioactive elements and heavy metals (Stein, et al, 1994). The

Laptev and Kara Seas receives the majority of heavy metals and radioactive elements due to the large number of major rivers, which discharge along the Siberian shelf.

Distribution and entrainment of these pollutants are by means of marine currents and ice- rafted debris, IRD (Siegel, et al, 2001; Schlosser, et al, 1995). Surface water and sea ice are transported into and out of the Arctic Ocean through the Fram Strait and the Bering

Strait, where transport within is by way of the Transpolar Drift, TPD, in this context it includes the contaminants. According to Pfirman, et al , 1995, drifting sea ice is a major player in the role of long-range redistribution of Arctic contaminants. Therefore, by furthering our knowledge of clay mineral assemblages, we will not only be able to map the mineralogical bathymetry of the oceans with greater precision but we will also be able to infer past changes in ocean circulation. Hence, we will be able to make more accurate statements and predictions about issues such as global climate change and ascertain the impact of anthropogenic pollutants within the food web.

On the other end of the controversial spectrum are scientists with the aim of developing ways of utilizing PDW (petroleum drilling waste). In the petroleum industry, a great deal of drilling muds and cuttings are waste by-products from drilling. These muds, either in a water or oil base, are mixed with a pozzolanic fly ash, lime and cement.

The result is a stable solid compound, which is being incorporated in the production of clay bricks and roads (Tuncan, et al, 2000; Jordan and Oliveira, 1992). As a result, contaminants are now being incorporated as a utility for the growth of society as opposed to being dumped as a contaminant into society and the environment. What was once an unstable toxin that could potentially poison the environment is now a catalyst in creating a more stable and more durable product (Swanepoel and Strydom, 2002; Meyer, 2002).

There is a great deal that leaves us bewildered about clays and many misconceptions still exist. This is especially true about mixed-layered clays (Moore and

Reynolds, 1989). For this reason, it is in this bewilderment that this research will commence

CHAPTER 2: NATURE, STRUCTURE, AND CLASSIFICATION OF CLAYS

2:1 Occurrence and Geology of Clays

Clay minerals are the ubiquitous minerals found at the Earth’s surface (Moore and

Reynolds, 1989, Meunier, 2005, Chamley, Osipov, et al, 2004). Clay minerals are also the most abundant minerals found in soils and in a variety of sedimentary rocks, such as shale (Nesse 1989; Moore and Reynolds, 1989). Moreover, clays are the most abundant minerals of the pelagic oozes covering the ocean floor (Moore and Reynolds, 1989).

The parent rock determines the nature of the clay minerals formed (Nesse, 2000).

Clay minerals form because of the interaction of aqueous solutions with rock (silicate minerals) under conditions of relatively low temperatures (Nesse, 2000; Velde, 1995).

rock + water + ions in solution = clay ± other minerals + ions in solutions

Therefore, clay minerals are unstable in anhydrous environments (Velde, 1995).

These now hydrated minerals display a lower density and higher volume state than the original minerals from which they formed along with a different physical structure.

Moreover, Velde explains that, “The proportion of water, compared to that of the solids

(rock), which interact, determines the rate and type of chemical reaction and ultimately the type of clay mineral formed” (1995). The environment for clay formation is limited in its range of temperature and time (Velde, 1995). These low temperature areas are all near the Earth’s surface, within the first several hundred meters of the crust (Velde,

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1995). Coupled with near surface environments, are temperatures that do not exceed

50˚to 80˚C, which is when clays become unstable and begin to transform into other minerals (Velde, 1995). The environments of formation and transformation include weathering, soil formation, sediment transport and deposition along with diagenesis and compaction, and hydrothermal alterations (Nesse 2000; Velde, 1995). As environmental conditions change, these formed crystals can alter or recrystallize such as in the conversion of smectite to illite during diagenesis (Nesse, 2000). Another environmental example is the formation of soils, which is highly dependent on climate. For example, kaolinite formation is favored in wet climates that experience intense weathering. Other changes that can occur are the formation of chlorite from kaolinite with increasing depth due to geothermal heating. Smectite formation is favored in dry environments. As weathering progresses, smectite converts to illite/smectite and then finally to illite (Nesse,

2000 ). Biscayne explains that mixed-layer clays show a strong geographic influence.

Within the North Atlantic, composition changes as a function of latitude for these categories of clays (1965). For more information regarding the formation and geology of clays, refer to Grim (1953), Eberl (1984), Velde (1995), and Chamley (1989).

2:2 Nomenclature

The term clay can be a misnomer because the term is used both with respect to size and mineralogical composition. The term clay as a rock, having no genetic significance other than being the product of weathering, hydrothermal influenced, or

deposited sediment, is a natural earthy, fine-grained material that is in essence a hydrous aluminum sheet silicate (Grim, 1953, Nesse, 2000). However, the term clay is also a reference to particle size whose particle diameter is less than 2 microns. Particle size, which is a non-mineralogical stipulation, presents potential problems in view of the fact that it may include other non-clay minerals such as carbonates, quartz, and metal oxides

(Velde, 1995; Nesse, 2000).

Clays belong to the silicate subclass known as phyllosilicates, sheet or layer silicates (from the Greek “phyllon: leaf) (Nesse, 1991; Meunier, 2005; Velde, 1995).

Phyllosilicates all exhibit a flaky or platy habit (Nesse, 2000), though small, clay minerals are crystals (Meunier, 2005). These mineral have special physical properties due to their very small size and specific crystal shape (Velde, 1995). The properties of clays are dominated by the surface area. In other words, the ratio of thickness to length of a sheet-shaped clay particle is near 20, a factor of nearly three times that of a fine grain cube or sphere of the same grain size and volume (Velde, 1995).

The crystal structure of clays is the same as micas, explains Blatt and Tracy,

(1996). The clay crystal structure consists of aluminum or magnesium, silicon, oxygen and hydroxyl (OH). Depending on the clay type, various other associated cations may be included. The ions and hydroxyl groups are arranged in a two-dimensional structure or sheet (Chamley, 1989). There are two configurations (1) all members in this system exhibit one common structural feature, which is a continuous network of silicon tetrahedral or T sheet (Nesse, 2000). The tetrahedral sheet has a general composition of

T2O5, where the T is the tetrahedral cation, mainly Si, with varying Al or Fe 3+ . Figures 1 and 2, the images below illustrates the T 2O5 structure and sheet structure, respectfully.

Figure 1: The T 2O5 structure. Image provided by Beth Hart, 2008.

Figure 2: Sheet structure. Image provided by Beth Hart, 2008.

At the center of the tetrahedra is a silicon cation. Oxygen is at each of the four

corners. Three out of the four individual oxygens connect by sharing with neighboring

tetrahedra (Figure 2). This results in a hexagonal pattern. The fourth tetrahedra or apical,

points in the direction that is perpendicular (up/down) to the sheet and forms part of the

octahedral sheet, which is illustrated in (Figure 3).

(2) The octahedral sheet is composed of medium-sized cations at its center,

usually Al, Mg, Fe 2+ , or Fe 3+ , and oxygen at the six corners. Individual octahedra share oxygens with other adjacent octahedral, individual octahedral link laterally along the edges, and link vertically with the tetrahedra. Three octahedra make up the smallest unit of an octahedral sheet. An illustration is pictured in (Figure 4).

Figure 3. Single Tetrahedron Image provided by Beth Hart, 2008

Figure 4. Single Octohedral Image provided by Beth Hart, 2008

If the octahedra have three octahedral cations (bivalent ions) at their center, then the sheet is referred to as trioctahedral . Oftentimes, this is referred to as a brucite because the ideal formula is structurally the same (Nesse, 2000). Conversely, if the octahedra have only two cations with one-octahedron vacant (trivalent ions) then the sheet is referred to as dioctahedral (Chamley, 1989) and it is oftentimes referred to as a

Gibbsite, being structurally the same (Nesse, 2000). These are illustrated in Figure 5.

Trioctahedral Layer (Mg 2+ )

Dioctahedral Layer (Al 3+ )

Figure 5. The Trioctahedral and Dioctahedral Layers Images provided by Beth Hart, 2008

The method of classifying clays is by these two types of cation occupancy within the octahedral sheet (Velde, 1995). The plane between the tetrahedral and octahedral sheets consists of the apical oxygen, shared by both sheets. At the same level as the apical oxygen and at the center of each octahedral hexagonal structure, is the unshared hydroxyl group. This fundamental structural assemblage of tetrahedral and octahedral sheets is called a layer (Chamley, 1989). The bonds that exist within the sheeted-layer and among the sheeted-layer are strongly covalent. Whereas the adjacent hydrogen bonds between the sheets are weak (Blatt, 1996), allowing for excellent cleavage along the 001 plane (Moore and Reynolds, 1989), this lattice feature allows for the adsorption of metallic cations and organic substances (Blatt, 1996). There are two primary layer categories recognized. First is the 1:1 layer or T.O. layer comprised of one tetrahedral sheet and one octahedral sheet. An example of a 1:1 layer would be kaolinite. Second is the 2:1 layer or T.O.T. layer comprised of two tetrahedral sheets in the external position and one octahedral sheet between. The majority of clays are as a 2:1 layer, smectite is an example of a 2:1 layer. The space between two consecutive 1:1 layers is called an interlayer . Electrostatic neutrality occurs when all structural cations are balanced by oxygens or hydroxyl groups in the interlayer. In this case, the result is that there are no chemical elements within the interlayer. However, many clay minerals have a negative charge, which are neutralized by various interlayer cations such as K, Na, Mg, and Ca, hydrated cations, or hydroxide octahedral groups. Oftentimes, the case is where the hydroxide interlayer joins laterally forming an additional octahedral sheet. Sometimes

this sheet is referred to as a ‘brucite sheet’ having a 2:1:1 or T.O.T.O arrangement, such

as chlorite (Chamley, 1989).

A structural unit is referred to as the sum of a layer plus an interlayer. This

structural unit coincides to a precise chemical formula called a formula unit . A clay

particle is the stack of many structural units. This clay particle is recognized as the

general part of the clay fraction and by most geologic fields as the as the fraction less

than two microns (Chamley, 1989).

The tetrahedral, octahedral, and each interlinked tetrahedral and octahedral unit

layer has a constant thickness. The tetrahedral unit is 3.4 Ǻ and the octahedral unit is

slightly thinner. When the tetrahedral and octahedral units combine by shared oxygen,

their combined thickness is less than the sum of the individual units. Consequently, the

1:1, T.O., is 7 Ǻ, the 2:1, T.O.T., is 10 Ǻ, and the 2:1+1 (also referred by some as 2:1:1) is 14 Ǻ for each unit layer (Velde, 1995). In XRD studies, the 060 peak position marks the dioctahedral and trioctahderal minerals, which reflect near 1.50 Ǻ and 1.53-1.54 Ǻ,

respectfully. There is one caveat, the 7 Ǻ or 10 Ǻ unit layers can stack in a variety of ways. The outcome is crystallographic structures whose thickness is greater than the referred unit layer distance, which is also called polymorphism. Polytypism, the stacking arrangement of the layers is a special case of polymorphism. This can be either ordered or random. It is restricted to only layered structures but not restricted to silicates. When the stacking is ordered then the relation of one layer to another is by a rotation of 60˚, alternatively, if the stacking is random then one layer is rotated from another by some whole integer multiplied by 60˚. The smectite group is highly disordered, with little or

no alignment, or turbostratic, having no rotational relation of one layer to another layer

(Moore and Reynolds, 1989). Heteropolytypic is a term also used in reference to mixed- layer clay minerals (Iiyama and Roy, 1961). Mixed layering is essentially intermediate products from the reaction of two end member components, explains Srodon (1999), with most containing smectite or vermiculite as a swelling component. As a general rule, weathering reactions that produce mixed-layer clays have reversal reactions, however, this reaction is along a different path corresponding to high-temperature reactions. In other words, Srodon (1999, pg 47) states it this way, “the weathering reactions leading toward kaolinite or smectite are the reversals of the diagenetic reactions producing illite and chlorite … the reaction series are quite different, and no reaction involving mixed layering can be reversed along the same path.” For a more in-depth description of the structure of clays, refer to Chamley, 1989, Moore and Reynolds, 1989, and Velde, 1995.

For more information about the classification and nomenclature, refer to Grim, 1953,

Chen, et al, 1997, and Mackenzie, 1959. Table 1 lists the various clay names, chemical structure and mineral structure for each of the main clays in this study.

1989, p 141 REFERENCE Blatt & Tracy, 1996, 234 p Blatt & Tracy, 1996, 234 p Blatt & Tracy, 1996, 234 p Blatt &Tracy,1996, 234p Nesse, 2000, 251-253p Nesse, 2000, 253p Nesse, 2000, 253p Nesse, 2000, 253p & 254 Moore and Reynolds, 1989, 1989,p 123 Chamley, 1989; p10Moore and Reynolds,1989, 130p Moore and Reynolds, 1989, p 135 Moore and Reynolds,

COMMENTS Almost no substitution Mg may partly replace Al; interlayer Na and Ca present In illite, Mg, Fe partlyreplace octahedral Al; interlayer K present Exhibits optical characteristics Polytypism Dioctahedral & Trioctahedral Dioctahedral Dioctahedral & Trioctahedral Trioctahedral

T:O 1:1 2:1 2:1 2:1+1 1:1 2:1 2:1 2:1+1 MINERAL STRUCTURE s

. (OH)

2 10

4 2

(OH) 2 8 10 2 O (OH) 4 (Si,Al) 10 2 )(OH) nH2O )(OH) . O

4 3.2

2 8 10 )(OH) 4 4 10 O ,Si (Si,Al) 3 3 O .8 3 (OH) (OH)

(OH) (OH) 5 5 10 10 (Al 2 O O O (Al,Mg,Fe) 2 4 4 (AlSi Al 2 Al(AlSi .17 5 nH2O Si Si Si2O Si .8

2 2 2 4 .

Al Al CHEMICAL FORMULA Al Al Mg ~Ca (Mg,Fe,Al) KAl Unit formula T (Mg,Fe,Al)3(OH)6 ~K ‘Smectite is the princess & illite is the yeoman’ Table 1: Mineralogy of Clays from Various Resource CLAY MINERALS Kaolinite Montmorillonite Illite (Muscovite) Chlorite Chlorite Illite Kaolinite Smectite Kaolin minerals Illite Smectite Chlorite

2:3 How are clays measured

Historically clays were analyzed predominantly by chemical analysis or by the utilization of the polarizing microscope (Moore and Reynolds, 1989; Write, 1916). The difficulty with the polarizing microscope is that it is nearly impossible to distinguish clay mineral species or even broad groups of clay even though many optical properties have been obtained about different clays with this methodology (Nesse, 1991).

With the discovery of X-rays by Rontgen in 1895 and the experiments leading to an understanding of diffraction of X-rays within a crystal structure by von Laue in 1912, the Braggs, working from the previous discoveries of von Laue, invented X-ray crystallography. This resulted in X-ray diffraction or XRD, as it will be referred to from here on, as the primary methodology for determining clay mineralogy (Moore and

Reynolds, 1989), as also predicted by Braggs (1913).

X-rays are part of the electromagnetic spectrum; hence, they exhibit properties of a wave and of a particle. When an X-ray interacts with a medium, only part of the X-ray is transmitted. The remaining part can be refracted, scattered and/or absorbed in various amounts (Moore and Reynolds, 1989). Since each element emits radiation that is characteristic of its atomic number, it in turns absorbs different wavelength of radiation that is also characteristic of that element (Moore and Reynolds, 1989). What von Laue discovered was that the spacing’s within a crystal structure serves as a diffracting grid

(Moore and Reynolds, 1989). Bragg’s Law is the understanding that the angle of incidence equals the angle of diffraction. Often this is referred to as the angle of

reflection, which is measured from the diffracting plane for XRD and is not the normal.

Therefore, the diffracted beam is in relation to the unit cell, measuring the kinds of atoms and their position within the unit cell (Moore and Reynolds, 1989).

There are a host of negatives in utilizing XRD, including equipment cost, the need for training to operate the equipment, the need for special monitoring and advanced technical maintenance of the equipment. Arduous procedures for sample preparation are also required. These involve multiple steps, which are time-intensive and often costly

(Arndt, 2001; Viscarra Rossel, et al, 2006). There is a potential for error to occur at any step, potentially reducing the reliability of the data. Textbooks are written with lengthy chapters covering just these areas of operation, preparation and procedure. In spite of the list of negatives, XRD is considered the premier methodology in determining clay mineralogy due to its precision. For a more detailed description regarding the historical development of XRD, which includes current approaches to using the equipment and sample preparation methodologies with XRD, refer to Moore and Reynolds, 1989.

Scientists are now looking toward a newer, less expensive, rapid, and nondestructive approach to studying clay minerals: diffuse spectral reflectance (DSR) is the answer. Current research is building on the work of Balsam, et al, 1996, 1997, 1998,

1999, 2003; Clark, et al, 1990, 1995, 1999; Gaffey, 1986, 1995; Giozan, et al, 2002;

Goetz, et al, 1985, 2001; Hubbard, 2005; Hunt, et al, 1970, 1971, 1973, 1979; Jarrard and

Vanden Berg, 2006; Lindberg and Snyder, 1972; Ortiz, et al, 1999, 2004; Sgavetti, et al,

2006; Shepherd and Walsh, 2002; Stefano, et al, 2003; and, Vanden Berg and Jarraad,

2006, who are but a few of the many researchers who have and are currently

incorporating DSR in their mineralogical research. Viscarra Rossel, et al, utilizes DSR for determining soil composition, and points out that, “the commonly used X-ray diffraction (XRD) techniques are primarily qualitative. Although quantitative modifications of the XRD methodology exist, e.g. X-ray powder diffraction (XRPD), they are usually involved, time-consuming and expensive (2006, p 70).”

DSR has the potential of being the ‘scientific revolution’, as Kuhn would phrase it, for our time, as XRD was in the early 1900’s.

CHAPTER 3: RESEARCH PURPOSE AND OBJECTIVES

The objective of this study is to demonstrate the versatility of active

electromagnetic sensing techniques, specifically that of diffuse spectral reflectance (DSR)

as a complementary methodology to XRD and XRF when studying clay minerals in

stratigraphic sequences. To support interpretation of the DSR data, I have employed the

Analytical Spectral Device (ASD) LabSpec ® Pro FR UV/VIS/nIR Spectrometer, which provides an innovative nondestructive methodology that is cost effective, portable, quick, and easy to use with samples in the lab or field. This research is to create a spectral library with the LabSpec ® Pro FR UV/VIS/nIR Spectrometer by Analytical Spectral

Devices (hereafter referred to as LabSpec ® Pro FR and ASD, respectfully) of four

primary clays as two component mixtures

This library consists of over 231 two variable mixtures in 5% increments, by

weighted percents. Clay mineral standards were obtained from the Clay Mineral

Repository and Wards Natural Science. Only those clay mixtures that are most prevalent

in sedimentary sequences were studied. The aim of this study is to close the gap that

currently exists for an expanded spectral library of clay mixtures and explore the DSR

variability of clay mixtures.

The foundation for this study rests on the tremendous amount of information

about clay mineralogy, its crystal structure, its formation and transformation under

various environmental conditions currently available (Chamley, 1989; Chang, H. K.,

24 25

1986; Nadeau, et al, 1985; Velde, 1995). The strict control of sample processing in

laboratory conditions ensures the research is accurate and replicable.

The advanced analytical equipment, in this research demonstrates how

technological advances continue to contribute to the accuracy of science and the growth

of knowledge. As technology advances and more accurate measures of clays are made

possible, the information can then be applied to look at prior research to validate findings.

One of the major areas of contribution is how a new way of examining clay mineralogy

can be used to validate or challenge theories. Even more exciting is to look at how the

information that I put together in a reference library format can then be used to address

some of the unanswered questions in various fields of sciences. For example, the library

can be used: 1) to examine the origin of the material in the Great Pyramid, contributing to

archeology and geology; 2) to identify the origin of clay tablets, contributing to

anthropology and history; 3) to determine the depth of layers of soil containing clays that

are exposed after a landslide, contributing to the study of geology, geography, and

enhancing the effectiveness of emergency management; and, 4) to study mixed-layer

clays from cores to determine their potential as a petroleum reservoir.

My literature review prior to developing this library demonstrated that the

LabSpec ® Pro FR spectrometer and similar equipment is proven a remarkable research tool, especially in the area of identifying pure minerals (Balsam, et al, 1996, 1997, 1998,

1999, 2003; Clark, et al, 1990, 1995, 1999; Gaffey, 1986, 1995; Giozan, et al, 2002;

Goetz, et al, 1985, 2001; Hubbard, 2005; Hunt, et al, 1970, 1971, 1973, 1979; Jarrard and

Vanden Berg, 2006; Lindberg and Snyder, 1972; Ortiz, et al, (1999, 2004), Sgavetti, et al,

2006; Shepherd and Walsh, 2002; Stefano, et al, 2003; and Vanden Berg and Jarraad,

2006). However, the literature review also revealed that there is little data available in the area of clay mixtures (Balsam, et al, 1996, 1997, 1998, 1999, 2003; Clark, et al, 1990,

1995, 1999; Gaffey, 1986, 1995; Giozan, et al, 2002; Goetz, et al, 1985, 2001; Hubbard,

2005; Hunt, et al, 1970, 1971, 1973, 1979; Jarrard and Vanden Berg, 2006; Lindberg and

Snyder, 1972; Ortiz, et al, 1999, 2004; Sgavetti, et al, 2006; Shepherd and Walsh, 2002;

Stefano, et al, 2003; and Vanden Berg and Jarraad, 2006). Whether the focus is on planetary studies, soil science, or marine sediments, the majority of authors mention the difficulty interpreting clay mixtures and the need for more spectral data (Balsam, et al,

1996, 1997, 1998, 1999, 2003; Clark, et al, 1990, 1995, 1999; Gaffey, 1986, 1995;

Giozan, et al, 2002; Goetz, et al, 1985, 2001; Hubbard, 2005; Hunt, et al, 1970, 1971,

1973, 1979; Jarrard and Vanden Berg, 2006; Lindberg and Snyder, 1972; Ortiz, et al,

(1999, 2004), Sgavetti, et al, 2006; Shepherd and Walsh, 2002; Stefano, et al, 2003;

Viscarra Rossel, et al, 2006; and Vanden Berg and Jarraad, 2006). Moreover, most only identify the primary clay present in the each sample, then utilize other more tedious and expensive means to determine proportions. My research will help fill this knowledge and methodological inefficiency. The research results will produce a spectral library of intimate clay mixtures. In this context, intimate, as defined by Clark (1999) is, “an intimate mixture occurs when different materials are in intimate contact in a scattering surface, such as the mineral grains in a soil or rock. Depending on the optical properties of each component, the resulting signal is a highly non-linear combination of the end- member spectra.” In addition, Clark (1999) notes that, “In an intimate mixture, the

darker of the two spectral components tends to dominate.” This library can be used as a reference for other researchers who will not need to replicate my work to analyze their samples; instead, they can incorporate this new spectral library into their data analysis.

However, since the validity of my methodology using the DSR is presented in my research, I believe that the methodology as well as the resulting library will have value for other researchers.

The concept undergirding the development of this spectral library was not only to create a reliable spectral library as a resource, but also to test it with samples that can be compared to ‘in the field’ data. In order to accomplish this, I tested and prepared core samples using the methodology explained in chapter 4. These samples still contain all the other constituents within the core samples such as carbonates, organics, iron oxides, etc.; nothing is removed from the sample prior to analysis. This procedure tests the spectral library in one of the most challenging domains, multiple components within a spectrum. It also attempts to remain within the instrument’s descriptive domain of

‘quick, easy, and non-destructive’.

The spectral library that was created was validated by analysis of samples from two different locales; one is a core of a slack water lake sampled at the MNK3 section provided by Dr. Curry at the Illinois Geological Survey. This is a fossiliferous slack water lake deposit from the last glacial period (Curry and Grimley, 2006). The other sample is from the Millbrig K-bentonite, an Ordovician biotite-rich tephra called

Millbrig, which has been obtained from Dr. Haynes, James Madison University in

Virginia. The study of this sample covers over five states (Haynes, 1992, 1994). Both cores have XRD and XRF data to compare to the DSR data.

CHAPTER 4: SAMPLE PREPARATION AND RESEARCH METHODOLOGY

One of the key ingredients in resolving a problem is asking the right questions to

arrive at a logical, sound and timely, resolution. Moreover, it is imperative to address

questions that may not arise directly but by implication. Often, the research will resolve

a question but fails to answer all the questions especially those that are not directly stated.

It is these questions that may have significant ramification. For that reason, much of the

first few months of the research began with addressing preliminary questions. The list is

not exhaustive, for the goal was not to overburden the research question, but to address

the potential of answering adequately the worthiness of the methodology and data

contribution involved.

The application of new technology to the study of a relatively new area of clay

mineralogy made it necessary to complete two literature reviews. The first literature

review dealt with clays, mixed-layer clays, and clay mixtures, including their uses, areas

of existence, and relationship to building knowledge. This is a necessary foundation to

the study of the composition of clay mixtures and the innovative methodological

procedures. The other literature review focused on the development of and use of DSR

and similar advanced technology because this research utilizes both DSR and a newer

tool or instrument developed by ASD. Initially, I completed a broadly focused literature

review to assess a wide range of material regarding the use of the LabSpec ® Pro FR spectrometer and/or similar equipment. It was important to build a basic frame of

29 30

reference about those researchers who have used this type of technology and the

methodology used in their research, in addition to what types of issues this type of

technology can address and resolve (Balsam, et al, 1996, 1997, 1998, 1999, 2003; Clark,

et al, 1990, 1995, 1999; Gaffey, 1986, 1995; Giozan, et al, 2002; Goetz, et al, 1985, 2001;

Hubbard, 2005; Hunt, et al, 1970, 1971, 1973, 1979; Jarrard and Vanden Berg, 2006;

Kruse, 1994, 1996, 2004; Lindberg and Snyder, 1972; Ortiz, et al, 1999, 2004; Sgavetti,

et al, 2006; Shepherd and Walsh, 2002; Stefano, et al, 2003; and Vanden Berg and

Jarraad, 2006). This places my methodology for the use of the LabSpec ® Pro FR

spectrometer within the context of prior research. The types of accomplishments that

have been made with this technology and their methodology are the foundation that led

me to explore what can be built upon the work of all these pioneers. In developing the

modifications used in this research, I needed to identify any limitations, to what degree

these limitations occur, and how these limitations might hinder the integrity of the

spectral library being produced. These challenges are addressed in the pilot tests run as

part of the development of the methodology developed and used in this research.

An in-depth literature review was conducted to learn more about the many

different areas where DSR is employed. The majority of articles revealed that the

information was primarily in the area of mineralogy – directly or indirectly, anything

ranging from mineral identification, planetary studies, to soil property analysis. While

few explicitly explained their technique, detailing all the components of their

methodology, those utilizing the LabSpec ® Pro FR spectrometer, stated they did follow the recommendation of the manual, either in using the equipment or in their data analysis

(Shepherd and Walsh, 2002; Jarraard and Vanden Berg, 2006). However, in most cases

where spectral reflectance in being employed, the data produced by the equipment was

being utilized in a unique manner, beyond the scope of the references cited. This initial

information on how the equipment is used by others led to the refinement of tangential

methodology questions that needed to be resolved in order to ensure the accuracy of the

results.

As a result of preliminary tests, the final methodology listed below was used with

the LabSpec ® Pro FR spectrometer for the analysis of clay sediments. Many tests were run in order to develop and maintain a certain degree of consistency and reproducibility in each sample preparation while at the same time producing a spectrum with minimal noise and minimal error. The steps of the specimen treatment and analysis methodology follow.

1. Samples were homogenized in one of three ways. All clay rocks were machine

ground for 2-3 minutes at 1500 rpm using the Geno/Grinder 2000 ® from OPS

Diagnostics, LLC. These samples were then dry sieved at <63 microns. This

allows for a homogenous grain size, which is important because grain size affects

reflectance. Aqueous clay suspensions, were sieved at <63microns, dried, hand

crushed in a crucible, and dry sieved again at <63 microns. The only exceptions

are those samples received as powders from the CMS (Clay Mineral Society).

Grain size for each powder was confirmed with the Mastersizer 2000 ® by

Malvern and spectrally compared to the same samples machine ground and dry

sieved <63um..

2. All clays are suspended in deionized water, run through a Millipore Sterifil ®

Aseptic System and Sterifil ® 47 mm Filter Holder onto the Whatman Binder-Free

Glass Microfiber Filters: Type GF/F (or GF/F). The GF/F is a glass micro-fiber

filter with a filtering capacity to .7um and is 47mm in diameter.

3. The minimum amount of clay to be suspended onto the GF/F is no less than

300mg. The paper filters can hold more sediment than the membrane filters. This

ensures that the platform background or filter is not causing interference in the

spectrum. Concomitantly, all filters will be placed on the Spectralon (or

instrument calibrator) for spectral analysis. See Figure #6.

4. The LabSpec ® Pro FR spectrometer is set at no less than an average of 800 counts

per spectral reading to ensure minimal noise in the spectral output.

5. The LabSpec ® Pro FR spectrometer is recalibrated between each spectral reading

to ensure spectral consistency. When reading GF/F filters with clays, use the

High Intensity Contact Probe (HICP). Pilot testing demonstrates there was

greater consistency with less noise with the HICP versus the Fiber Optic Probe.

6. The lens of the HICP is wiped with a lens paper between each use to keep the lens

clean.

7. All GF/F filters are air-dried for no less than 48 hours (two days), on a metal rack.

A PVC ring is used to prevent curling and if necessary a weight is placed on top.

Air drying prevents over fracturing. The PVC ring keeps the filter from curling,

which in turn prevents the sample from breaking or tearing away from the filter.

See Figure #7.

8. All samples are then placed in a sixty degree Celsius oven for at least one hour

and no more than two hours. The samples are then left sitting for fifteen minutes

to cool before removing the PVC ring in order to remove any moisture due to

changes in relative humidity within the lab.

9. Samples are labeled and placed in E6 photo archival sheets for storage. See

Figure #8.

Figure 6: LabSpec ® Pro FR spectrometer with High Intensity Contact Probe testing clay sample on a GF/F filter.

Figure 7: Samples air drying with PVC ring, top view

Figure 8: Samples stored in E6 archival photography sheet.

CHAPTER 5: DATA ANALYSIS

Reflectance spectrometry is based on measuring the percent of reflectance as a function of wavelength (Balsam, et al, 1996, 1997, 1998, 1999, 2003; Clark, et al, 1990,

1995, 1999; Hunt, et al, 1970, 1971, 1973, 1979). The embedded spectrometric application program generates a 1nm resolution 350nm to 2500nm spectral data file as an .asd format file. This proprietary output file is then converted to a standard ASCII text file (.txt) which is then uploaded from the spectrometer laptop to any other notebook or

PC. This ASCII data file is then imported into Microsoft Excel ®. Now in spreadsheet

format the dataset is pruned to 10nm resolution from the original 1nm resolution

records to yield a more manageable file. The resultant 10 nm resolution spectral subset is

experimentally consistent with the 1nm resolution spectral data.

To test the applicability of the methodology, we evaluated two data sets: (1) sediment from core MNK3, from a slack water Pleistocene lake near St. Louis, in which stratigraphic changes in clay mineralogy occur down core, and (2) the Ordovician

Millbrig K-bentonite (samples from AL, GA, KY, TN, and VA), an altered tephra in which the changes occur laterally in a single horizon.

The spectra of sediment tends to be smooth and change gradually, appearing to be featureless. Using the first derivative enhances specific features of the spectrum by increasing the variability and lessening the affect of co-linearity (Balsam, et al, 1996,

1997, 1998, 1999, 2003; Ortiz, personal correspondence; Shepherd and Walsh, et al,

37 38

2002). This clay mixtures library, and core samples, use the first derivative pretreatment

for the visible (VIS) range (400-700 nm), the near infrared (nIR) range (700-2500 nm),

and full spectrum (VnIR) range (400-2500 nm) at 10 nm resolution, see Figure 9. This

clay mixtures library and core samples are then inputting that data into SPSS ® (Statistical

Program for Social Sciences) for regression analysis (Smith, 2002; Dale, J.M. and Klatt,

L.N., 1989).

DSR can provide accurate estimates about the mineral composition. For a detailed discussion on spectroscopy as it relates to rocks and minerals, refer to Clark

(1999). Deaton and Balsam (1991) used DSR to measure marine sediment composition.

They observed that the first derivative peaks functioned very much like the XRD peaks, being both a function of concentration of a substance and composition of the matrix

(Balsam, et al, 2003). DSR spectral data is then validated against XRD, ICP-MS, and

XRF data. PCA (Principal Component Analysis) was used to correlate the spectral data of the library with the MNK3 and Millbrig core samples. Stepwise Linear Regression

(SLR) analysis was used with the composite library as an identification tool.

By combining PCA analysis of unknowns with SLR against our clay mixture library, we identify our components in an objective, quantifiable way.

Illite (IW48) Versus Chlorite (CCa) in the VIS

0.8 I48W-0_CCa2-100 I48W-5_CCa2-95 0.7 I48W-10_CCa2-90 I48W-15_CCa2-85 0.6 I48W-20_CCa2-80 I48W-25_CCa2-75 I48W-30_CCa2-70 0.5 I48W-35_CCa2-65 I48W-40_CCa2-60 0.4 I48W-45_CCa2-5 I48W-50_CCa2-50

Reflectance I48W-55_CCa2-45 0.3 I48W-60_CCa2-40 I48W-65_CCa2-35 0.2 I48W-70_CCa2-30 I48W-75_CCa2-25 I48W-80_CCa2-20 0.1 I48W-85_CCa2-15 I48W-90_CCa2-10 0 I48W-95_CCa2-5 400 450 500 550 600 650 700 I48W-100_CCa2-0 Wavelegth

First Derrivatives of Illite (IW48) Versus Chlorite (CCa) in the VIS

0.5 I48W-0_CCa2-100 I48W-5_CCa2-95

0.4 I48W-10_CCa2-90 I48W-15_CCa2-85 I48W-20_CCa2-80 I48W-25_CCa2-75 0.3 I48W-30_CCa2-70 I48W-35_CCa2-65 I48W-40_CCa2-60 0.2 I48W-45_CCa2-5 I48W-50_CCa2-50 Reflectance I48W-55_CCa2-45

0.1 I48W-60_CCa2-40 I48W-65_CCa2-35 I48W-70_CCa2-30 I48W-75_CCa2-25 0 I48W-80_CCa2-20 I48W-85_CCa2-15 I48W-90_CCa2-10 -0.1 I48W-95_CCa2-5 0 5 10 15 20 25 30 35 I48W-100_CCa2-0 Wavelength

Figure 9. Graphic presentation of the distribution difference between straight reflectance versus the first derivative using the Illite (I48W) library mixture with Chlorite (CCa) in the VIS range as the example.

5:1 Principle Component Analysis Defined

PCA (Principal Component Analysis) allows for the reduction of a complex data set to reveal hidden information in a more simplified relevant data set. In other words,

PCA extracts the underlying data that in what seems to be complex and large by identifying patterns in a set of data. These types of high dimension data sets are often confusing to interpret. Therefore, once the patterns are identified, the data can be compressed and expressed in a smaller dimension without a great deal of loss of information (Shlens, 2005; Smith, 2002). The DSR is extremely sensitive to characteristics of clay mixtures such as grain size, color, and mineralogical differences

(Jaraard, 2006 ; Clark, 1995), and thus can recognize the difference between a mixed- layer clay (MLC) and a clay mixture, relaying different spectrums. For this project, the use of PCA reduces the resulting spectral data into a useable format that more readily portrays the mineral composition.

5:2 Stepwise Linear Regression Analyses Defined

Multiple regression analysis is a statistical technique that allows the researcher to examine the impact of multiple variables acting together on the dependent variable.

Thus, knowing the value of the independent variables enables one to predict the value of the dependent variable. Stepwise multiple regression lets you describe the relationship

between one 'predicted' (dependent) variable and a selection of 'predictor' variables

(independent). (Sweet, 1999; Kutz 1999)

Stepwise regression refers to regression models in which the choice of predictive variables is carried out by an automatic procedure. It is especially relevant where the research is seeking to identify relationships between variables rather than to test theory of expected occurrence. Thus in this research, as we attempt to determine what minerals can be used to predict the presence of others, the stepwise linear regression format is most appropriate. Stepwise regression is a combination of forward and backward procedures based on probability. Independent variables are added variables one-at-a-time as long as they are statistically significant and are removed if they are non-significant.

Thus, the independent/dependent P values are compared to 0.05. With forward selection, we are looking at the smallest P value to decide what to include. With backwards elimination, we are looking at the largest P value to decide what to remove. However, these are not special P values. They are the same ones used to assess a single predictor in a multiple regression equation. Specifically, the steps used in SPSS are:

Step 1: The first predictor variable is selected in the same way as in forward selection. If the probability associated with the test of significance is less than or equal to the default

.05, the predictor variable with the largest correlation with the criterion variable enters the equation first. Step 2: The second variable is selected based on the highest partial correlation. If it can pass the entry requirement (PIN=.05), it also enters the equation.

Step 3: From this point, stepwise selection differs from forward selection: the variables already in the equation are examined for removal according to the removal criterion

(POUT=.10) as in backward elimination. Step 4: Variables not in the equation are examined for entry. Variable selection ends when no more variables meet entry and removal criteria. (Dallal, 2008)

The regression is presented graphically with a line that identifies the best fit for the independent and dependent variables based on the least sum of squares. The residual is the line that minimizes the sum of the squared errors (error = distance between actual and predicted values). In the model summary table the R Square variable gives the proportion of variance that can be predicted by the regression model using the data provided. It is commonly reported as a percentage. The Adjusted R Square value gives the proportion of variance that can be predicted using the regression model on a new set of data. It is generally reported as a percentage. Statistical significance can be checked by an F-test of the overall fit, followed by t-tests of individual parameters. (Dallal, 2008)

5:3 Testing the Library against Itself

One of the first steps in analysis was to test the library against itself in order to test its accuracy. For example, if a mixture of 70% Kaolinite & 30% Ca-Smectite is chosen, what predictors does the linear regression model select as a best fit? Will it pick something close to that mixture with a high R squared value or combinations? Random sampling of multiple combinations of the data resulted in predictability within an average of 5% but no greater than 10%. On the other hand, for certain chlorite mixtures, as the

amount of chlorite increased the predictability of the model was less reliable. This was noticed more in the Chlorite_Ca-Smectite library rather than in other Chlorite mixtures.

Table 2 illustrates this point.

5:4 Testing the Library against the Cores

One of the challenges was comparing the XRD clay percents against the predicted models. Frequently, the primary clay was predicted, but not the secondary clay. This may be because both cores represent a four component clay mixtures plus ancillary minerals, as opposed to the two component library. Inter-correlation of the VIS could also limit determining relative proportions of components in the mixtures. In the full spectral analysis, VIS, VnIR, and nIR analysis was conducted on the Millbrig and once again only the primary clay was predicted. Therefore, PCA analysis was run on both samples. Multiple components from each core demonstrated high iron content. SLR analysis resulted in a repeated appearance of models containing Chlorite and a Chlorite-

Fe-bearing Illite model. As a result, SLR was conducted a second time with the inclusion of various Fe-bearing spectra from USGS, specifically hematite, goethite, pyrite, magnetite, and biotite. Illustrations of this finding are presented in the following figures.

Principle component one (C1R) and three (C3R) are illustrated and compared with spectra from USGS demonstrating their high iron content. All remaining components are illustrated in the appendix.

Correlation Results for Millbrig C1R Cca-5_STx-95 = .94

1.2 -0.08

-0.06 1

-0.04

0.8 -0.02

0.6 0

0.02 MBRIG_C1R 0.4 0.04 C5_ST95 Reflectance

0.2 0.06

0.08 0

0.1

-0.2 400 450 500 550 600 650 700 0.12

-0.4 0.14 Wavelength

R2 = .89

0.14

0.12

0.1

0.08

0.06

0.04 C5_ST95 0.02 Reflectance

0 R2 = 0.8872

-0.02

-0.04

-0.06 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 -0.08 Millbrig C1R

Figure 10: Millbrig core first component correlation of the VIS versus clay mineral library CCa-5_STx-95 (top) and line of regression and R 2 (bottom).

Millbrig C3R Correlation ResultsCorrelation Results for for IW40_C60IW40_C60 = .97 = .97

1.2 0.15

0.1 0.8

0.6 0.05

0.4 MBRIG_C3R IW40_C60 Reflectance 0 0.2

0 -0.05

-0.2

400 450 500 550 600 650 700 -0.4 -0.1 Wavelength

R2 = .94

0.15 R2 = 0.9386

0.1

0.05

IW40_C6 0

Reflectance 0

-0.05

-0.1 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 Millbrig C3R

Figure 11: Millbrig core third component correlation of the VIS versus clay mineral library (top) and line of regression and R 2 (bottom). The correlation is .97.

Millbrig C3R Correlation Results for Hematite = -.83

1.2 -0.04

-0.02 1

0 0.8

0.02 0.6

0.04

0.4 MBRIG_C3R 0.06 Hematite Reflectance 0.2 0.08

0 0.1

-0.2 0.12

400 450 500 550 600 650 700 -0.4 0.14 Wavelength

R2 = .7

0.14

0.12

0.1

0.08

0.06 Hematite 0.04 Reflectance

0.02

0 R2 = 0.6963

-0.02 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 -0.04 Millbrig C3R

Figure 12: Millbrig core third component correlation of the VIS versus iron bearing mineral from USGS, best fit was hematite (top) and line of regression and R2 (bottom).

As for the MNK3, PCA analysis was also run on the 3 strongest independent variable components from 58 samples. Analysis of the first component proved it to be of a high Fe content, as was the third component. Stepwise linear regression was conducted again with the inclusion of various Fe-bearing spectra from USGS, in particular hematite and goethite. The first component had a high correlation with chlorite and various other clay minerals and the third component appears to be a combination of hematite and goethite. The second component appears to be ancillary minerals such as dolomite plus other clay minerals.

MNK3 C1R Correlation Results for CCa2-75_IMt1-25 = .89 I48W-10_CCa2-90 = .89 1.2 0.1 both represent a high Fe content

1 0.08

0.06 0.8

0.04 0.6

0.02 MNK3_C1R 0.4 CCa2-75_IMt1-25 0 I48W-10_CCa2-90 Reflectance 0.2 -0.02

0 -0.04

-0.2 -0.06

-0.4 -0.08 400 450 500 550 600 650 700 Wavelength

R2 = .8

0.1

0.08

0.06 R2 = 0.7995

0.04

0.02 CCa2-75_IMt1-25 0 Reflectance

-0.02

-0.04

-0.06 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2

-0.08 CCa2-75_IMt-25

Figure 13: MNK3 core first component correlation of the VIS versus clay mineral library, two mixtures with high correlation (top) and line of regression and R 2 (bottom).

MNK3 C2R Correlation Results for KGa1b-45_STx1b-55 = .63 IMt1-5_STx1b-95 = .66 1 0.25 Dolomite = .59

0.8 0.2

0.6 0.15

MNK3_C2R KGa1b-45_STx1b-55 0.4 0.1 IMt1-5_STx1b-95 Dolomite Reflectance

0.2 0.05

0 0

-0.2 -0.05 400 450 500 550 600 650 700 Wavelength

R2 = .44

0.12

0.1

0.08

0.06 Dolomite Reflectance

0.04 R2 = 0.4357

0.02

0 -0.2 0 0.2 0.4 0.6 0.8 1 MNK3 C2R

Figure 14: MNK3 core second component correlation of the VIS versus clay mineral library, three mixtures highly correlate (top), and line of regression and R 2 (bottom). Dolomite gave the best R squared value.

MNK3 C3R Correlation Results for Goerthite = .37 1.2 0.14 Hematite = .87

0.12 1

0.1 0.8

0.08 0.6

0.06 MNK3_C3R 0.4 Goethite 0.04 Hematite Reflectance 0.2 0.02

0 0

-0.2 -0.02

400 450 500 550 600 650 700 -0.4 -0.04 Wavelength

R2 = .7

0.14

0.12

R2 = 0.6983 0.1

0.08

0.06 Hematite 0.04 Reflectance

0.02

-0.02

-0.04 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 MNK3 C3R

Figure 15: MNK3 core third component correlation of the VIS versus iron bearing minerals from USGS, three mixtures highly correlate (top), and line of regression and R 2 (bottom).

These predictability difficulties may also be due to small changes in lightness and darkness that can result in substantial deviations (Balsam et al, 1997). Given that another confounding factor may be the presence of iron-bearing minerals in the mixture, I considered the ‘matrix effect’ (Balsam et al, 1999). Iron-bearing minerals such as hematite may be masked by illite and chlorite. Iron gives a very strong signal even in small amounts and may be skewing the results.

The MNK3 had two different sample sets run at two different occasions. One set resulted in data information for a large number of the samples; however, the second set of samples had many ‘insufficient sample’ or ‘N/A’ readings within the data results. The reason for the lack of data points was not reported. Therefore, since this information was prepared outside of my observation and has no explanation for missing information, I find myself questioning the validity of the analysis as it pertains to accuracy and precision of the data sets. In other words, the flaw may be in the MNK3 analysis or sample.

Another possible explanation is in the comparative methodologies for the

Millbrig. The qualitative determination of content examined in 1994 used thin sections in

X-Ray diffractions for the determination of content along with petrography of whole rock thin sections. Balsam, 2000, discovered that “regardless of the matrix, DSR is an order of magnitude more sensitive than visual observations,” which explains the difference in findings of qualitative versus quantitative methodology for determining clay content and ancillary iron bearing minerals. Iron-bearing minerals are present within the Millbrig, this is confirmed by XRF. Hence, again I considered the ‘matrix effect’ and the concealing

effect of illite and chlorite on iron-bearing minerals such as hematite. However, it is my experience with DSR that iron gives a very strong signal even in small amounts and may be skewing the results. Consequently, small changes in lightness and darkness would occur from these iron-bearing minerals, which in turn, can result in substantial deviations as mentioned earlier by Balsam et al, 1999.

55 . Table 3:Table Core Data iron of content by XRF Analysis

CHAPTER 6: RESULTS AND CONCLUSIONS

On the basis of my research, I know that one clay mineral is present in greater quantities than the second, which allows me to determine the relative presence of the various clay minerals. Thus, I can identify the presence or absence and amounts of the clay minerals relative to one another, but not proportionate amounts, probably due to the use of a four component clay mixture with other ancillary minerals. Basically, the result is an ordinal distribution of the amounts of minerals present in the mixture. Ordinal distributions, as non-parametric data, do not allow the computation of averages or proportions, but tell only relative amounts such as greater, greatest, and least. The spectral clay mineral library is useful and the methodology pursued has proven successful. However, at this time there is no consistency in the predictability of the data.

As a result, future research needs to eliminate intervening factors sequentially to determine various iron components and their impact on readings.

In summary, we have the two samples that were tested, for which we have mineralogical XRD, ICP and XRF data, were compared to the clay mineral mixture spectral library in order to compare mineral information. This was to validate the spectral library in that the spectral information from the XRD is expected to correlate with the data produced by the DSR analysis. Clay mineral mixtures were assessed against interstratified mixed-layered clay mineral data. Fundamentally, this was a comparison of known stoichiometric mixed-layered clays against the proportionally

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equivalent mixture of clay minerals from the spectral library. We already know the XRD

can interpret the differences between a mixed-layer clay which is a structural bonding

versus a mixture of clays. However, it is here that I tend to part-company on

methodologies and interpretation of data. The intention of this project was to produce a

better understanding of the differences and facilitate more precise interpretation. The

library has enabled us to identify the proportion of various elements in a clay sample

according to the spectrum produced by the DSR. I anticipated a more positive

relationship that validates the usefulness and accuracy of my clay mixture spectral

library. But, it is too early to present factual data at this point in the research process due

to the quality of the quantification of the XRD data.

Concomitantly, a low R 2 value does not invalidate the usefulness of the library.

This only demonstrates that the correlation is not linear. Perhaps, the next step is to compute the logistic regression, which anticipates sigmoidal or S-curve relationships, rather than linear relationships of the data. If the relationship is a curve the log will resolve this dilemma (Sweet, 1999).

In an attempt to determine whether non-parametric analysis would result in more consistent and accurate correlation information, Spearman’s Rho was calculated on the visible spectrum clay mixtures against the wavelength and identified components.

Pearson’s R had been used but because some of the graphs show curvilinear relationship,

I ran the Spearman’s Rho, based on rakings of data rather than raw values, to see if the correlation measures were the same. In fact, the strongest correlations did not match those identified either with Pearson’s R or in the Regression analysis, which were the

same in rank order of strength. One possible explanation is that with QXRD the quantitative measures using the raw data are more accurate and it is, therefore, not necessary to use non-parametric analysis. However, future research or analysis might look at these comparisons more closely in order to account for the curvilinear scatterplots of the regression equations.

6:1 The assumptions of QXRD and other chemical analysis as it relates to Semi-quantitative versus Quantitative Analysis

In a later article review on Quantitative XRD (QXRD), some researchers use the term “semi-quantitative” to address the fact that the data gathered cannot be strictly analyzed using standard statistical or mathematical techniques. Let it be noted that in fact, however, there is no such thing as “semi-quantitative” data or analysis.

Methodology is either quantitative or qualitative. If, however, non-parametric data

(which are nonetheless quantitative) are being gathered, the ability to analyze the data is limited in that it is neither interval nor ratio in character and therefore cannot be analyzed using parametric based techniques. For example, Spearman’s Rho may be more accurate in determining correlation than Pearson’s R because it uses rank order of measures rather than the raw data measurements.

What seems to be occurring is that the quantitative data is compromised or limited in terms of its accuracy or universal application. Thus, the interpretation relies on qualitative methods of analysis that are not included in the quantitative measures. Rather than addressing this limitation or identifying the qualitative considerations used in the

interpretation (which are not always defined or clear), some researchers use the non-term:

“semi-quantitative” such as Rider (1991) in his tables. What would be more helpful in developing reproducible methodology and interpretation, would be a careful analysis of exactly what is being taken into consideration in the interpretation of the quantitative data gathered through well logging. Some of the factors involved may be along the lines of

“gut instinct’ based on experience; but the definition of exactly what is providing the clues for the interpretation would be of more benefit to transferable knowledge and information than simply referencing the reality that some non-quantitative determinants are used in the interpretation of analysis. (Grant, 2008, personal correspondence).

The difficulty occurs when conducting an article review and coming to the realization that most of the quantitative analysis is in actuality a combination of quantitative and qualitative. Classification of materials came as a result of X-ray diffraction techniques, which is still only “semi-quantitative”. In an article where XRD was incorporated in order to make the well logging data more quantitative Doveton states it best, “In reality, the link between rock composition and log responses is both underdetermined and nonlinear. . . Furthermore, the difficulties in specifying many of the input parameters means that mathematical optimality does not necessarily means geological reality” (1992, Pg. 289).

This infers that one must first make assumptions and then a qualitative decision based on those assumptions before one can determine what is present. This is not quantitative, even though it may be predictive and repeatable it is still only qualitative. It represents precision or repeatability but it does not represent accuracy, which is how

close your answer is to the correct answer. Both, precision and accuracy, are needed to

be statistically reliable.

Based on my lengthy article reviews for my research project on Diffuse Spectral

Reflectance (DSR), for my course Marine Sediment Transport (MST), and my course

Wire Line Well Logging (WLWL), that incorporated X-ray diffraction in order to

determine clay mineral content, I discovered that these terms were used very ‘loosely’.

In many articles in the category of ‘methodology’, they often stated that clay minerals

were determined by QXRD. However, in the final analysis, within their conclusions,

many admitted in the end that only a qualitative argument could be made. For instance,

the article by deMenocal, et al, stated “We conducted quantitative XRD on bulk samples

to evaluate the mineralogy of this material….While these are quantitative estimates they

are probably not accurate weight percent values” (1992, pg 402 & 403; italics not in the

original).

With all the limitations of QXRD, it is within my research of DSR that I believe

the argument of limitations is on equal footing. DSR is a rapid, non-destructive, cost

effective, safe, and useful technique for characterizing clay minerals and it has proven

useful for determining mineral composition. At this point in history, DSR has the

potential of being the ‘scientific revolution’, as Kuhn would phrase it, for our time, as

XRD was in the early 1900’s.

The Figures #16 illustrates the distinctive quantitative characteristics of clay minerals by diffuse spectral reflectance. Although, my experience with XRD is extremely limited, I am sure with practice the reflectance is easy to determine from clay

to clay. However, in my opinion, it appears that the reflectance of XRD does not appear as robust for differentiating various clays as it is with DSR, see Figure 17. It is my hopes that in the future, DSR with make gains in the field of quantitative analysis for clay minerals.

CHAPTER 7: FUTURE RESEARCH

I find that my contributions may have a wider impact than initially thought. My first concept was that the information I produced would be useful for people who study clays. As I learned more about the use of clays and research that focuses on clays, I realized that the information I am producing can be used in a variety of areas I had not initially recognized. This is very exciting as I believe I am making a contribution to the field of geology that will be used a lot. I would expect that there are several areas of future research made possible by the results of my research. The most direct area of future study is to analyze more and more clay mixtures in order to expand the initial spectral library. The results of the regression analysis demonstrate a need for more spectral data of iron bearing minerals. New mixtures in three rather than two components will also give more useable information. There are other more esoteric research areas that can build upon my research. Interplanetary studies can use the information to more accurately determine planet composition. Remote sensing and economic geology can use the information as well as the methodology to better identify the location of various clay compositions. This information can be used to identify potential deposits of useful minerals and to better determine slope and ground stability.

The methodology and library can be useful in other disciplines as well. For example, I discovered that researchers could take the LabSpec® Pro FR spectrometer to the Great Pyramid to determine the materials used in construction and the original source

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of these materials (Davidovits, 1984, 2004; Folk, 1990, 1992; Ghorab, et al, 1986;

Harrell, et al 1993, 1994; Ingram, et al, 1993; McKinney, 1993; Morris, 1992, 1993,

1994; Ragai, et al, 1987). This research would make a great contribution to archeology

and anthropology. Analysis of the Aswan quarry using GIS and LabSpec® Pro FR

spectrometer would help identify the original source of building materials, which would

then enable researcher to hypothetically reconstruct the route materials had taken in order

to be used in the final structures. The new LabSpec® Pro FR technology coupled with

GIS makes this research possible because the Egyptian Department of Antiquities

restricts taking samples from the site for analysis. Since the LabSpec® Pro FR

spectrometer can be used on site without damaging the historical structures, it opens a

new door to information about the workings of an ancient civilization. This type of

research illustrates the breadth of application of future research based on my studies. Not

only will this contribute to continued expansion of the knowledge base in geology, but it

has the potential to open doors of knowledge in other sciences as well.

Back in the mid 1980’s and 1990’s, innovative research methodologies were being introduced and conducted in utilizing remote sensing such as Landsat MMS and

Thermatic Mapper (TM), which was implemented to detect three main categories of hydro-carbon micro-seeps, HCMS. These geo-chemical changes include (1) red bed bleaching (ferric iron reduction) (2) the conversion of feldspars and mixed layer clays to kaolinite, and (3) incongruity in vegetation spectral reflectance. Observing the effects of

HCMS on clays and soils and finding these changes in mapping reveals clues in HCMS

migration and its near surface expression (Schumager and Abrams, 1996; Conel and

Alley, 1985).

I am confident that the DSR has proven to be a versatile tool, being accurate at determining the qualitative character of the absorption/reflectance of the clay minerals.

Furthermore, I am confident that the results of this study will convince other researchers to consider DSR as a primary methodology for interpreting clay mineralogy. Other researchers, for example, who currently rely on DSR for their work in aerial and satellite imaging for mineral mapping will find this spectral library as a helpful resource addition.

Archeological research and soil studies will also find this spectral library as a useful research tool. Thus, I believe my contribution will be in two primary areas, one being the methodology and use of the DSR and the other being the development of the spectral library.

As noted earlier, the initial literature review revealed that regardless of whether the subject is planetary studies, soil science, or marine sediments, the majority of the studies discuss the difficulty interpreting clay mixtures and the need for more spectral data.

It is my hope that in the near future, I will be able to continue developing more spectral resources of mixtures, which will include Fe-bearing spectral data.

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APPENDIX A

MILLBRIG – TOTAL VARIANCE AND SCREE PLOT OF PRINCIPLE COMPONENTS

80 81

A: Millbrig - Total Variance Explained & Scree Plot of Principle Components

MILLBRIG Scree

8 Series1 Eigen Val Eigen

0 0 2 4 6 8 10 12 Component

APPENDIX B

MNK3 – TOTAL VARIANCE AND SCREE PLOT OF PRINCIPLE COMPONENTS

82 83

B: MNK3 - Total Variance Explained & Scree Plot of Principle Components

MNK3 Scree

10 Series1 Eigen Val Eigen 8

0 0 2 4 6 8 10 12 Components

APPENDIX C

MILLBRIG CORE SECOND COMPONENT CORRELATION OF THE VIS VERSUS CLAY MINERAL LIBRARY AND LINE OF REGRESSION AND R2

84 85

C: Millbrig core second component correlation of the VIS versus clay mineral library (top) and line of regression and R 2 (bottom).

Millbrig C2R Correlation Results for K0_ST100 = .69

1 0.25

0.8 0.2

0.6

0.15

MBRIG_C2R 0.4 K0_ST100

Reflectance 0.1

0.2

0.05 0

400 450 500 550 600 650 700 -0.2 0 Wavelength

R2 = .47

0.25

0.2

R2 = 0.4733

0.15

K0_ST100 Linear (K0_ST100) Reflectance 0.1

0.05

0 -0.2 0 0.2 0.4 0.6 0.8 1 Millbrig C2R

APPENDIX D

MILLBRIG CORE SECOND COMPONENT CORRELATION OF USGS BIOTITE AND LINE OF REGRESSION AND R2

86 87

D: Millbrig core second component correlation of the VIS versus USGS Biotite (top) and line of regression and R 2 (bottom).

Millbrig C2R Correlation Results for Biotite = .7

1 0.07

0.06

0.8 0.05

0.04 0.6

0.03

7 0.4 0.02

Reflectance 0.01

0.2 MBRIG_C2R 0 Biotite

-0.01 0

-0.02

400 450 500 550 600 650 700 -0.2 -0.03 Wavelength

R2 = .47

0.25

0.2 R2 = 0.4733

0.15

Reflectance 0.1

0.05

0 -0.2 0 0.2 0.4 0.6 0.8 1 Millbrig C2R

APPENDIX E

MILLBRIG CORE FOURTH COMPONENT CORRELATION OF THE VIS VERSUS CLAY MINERAL LIBRARY AND LINE OF REGRESSION AND R2

88 89

E: Millbrig core fourth component correlation of the VIS versus clay mineral library (top) and line of regression and R 2 (bottom).

Correlation Results Millbrig C4R K25_SW75 = -.72

1 0

0.02 0.8

0.04

0.6 0.06

0.4 0.08 MBRIG_C4R 0.1 0.2 K25_SW75 Reflectance

0.12 0

0.14

-0.2 0.16

-0.4 0.18 400 450 500 550 600 650 700 750 Wavelength

R2 = .52

0.2

0.15

0.1

Reflectance 0.05

R2 = 0.5195

-0.05 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Millbrig C4R