UNIVERSITY OF CINCINNATI
DATE: 08/01/2003
I, Funda Özlem TOPRAK , hereby submit this as part of the requirements for the degree of: Master of Science in: Arts & Sciences, Geology It is entitled: High-Resolution Chemostratigraphic Correlation of the Lower Silurian (Llandovery) Osmundsberg K-bentonite in Baltoscandia and Northern Europe
Approved by: Dr. Warren D. Huff Dr. J. Barry Maynard Dr. Thomas J. Algeo HIGH-RESOLUTION CHEMOSTRATIGRAPHIC CORRELATION OF THE
LOWER SILURIAN (LLANDOVERY) OSMUNDBERG
K-BENTONITE IN BALTOSCANDIA AND NORTHERN EUROPE
A thesis submitted to the
Division of Research and Advanced Studies of the University of Cincinnati
in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
in the Department of Geology of the College of Arts and Sciences
2003
by
Funda Özlem Toprak
B.S., Middle East Technical University, 1999
Committee Chair: Dr. Warren D. Huff ABSTRACT
The Lower Silurian Osmundsberg K-bentonite is a widespread ash bed that occurs throughout Baltoscandia and parts of northern Europe. The sections containing the Osmundsberg K-bentonite beds were investigated to determine if chemical composition of these beds can be used as a basis for high-resolution chemostratigraphic correlation on a regional scale. Fifteen Osmundsberg K-bentonite samples and twenty-six samples of accompanying beds from twelve different localities were analyzed for major and trace elements and the data statistically treated using discriminant function analysis to determine if the trace element data provides a unique chemical fingerprint for the Osmundsberg K-bentonite beds. Comparison with the correlation model drawn by Bergström et al. (1998) based on biostratigraphic evidence show that results from two independent techniques are generally in agreement. Results demonstrate the unique and identifiable chemical fingerprint of the Osmundsberg K-bentonite bed can provide an additional stratigraphic tool for local and regional correlation of these K-bentonites.
ACKNOWLEDGEMENTS
I gratefully acknowledge the assistance of all people who made significant contribution to this thesis. I express especial gratitude and appreciation to Dr. Warren D.
Huff, my advisor, for introducing me to the fascinating world of clays and for all his continuous support, excellent ability of instruction, help, advice, friendship, and patience without which this thesis would not have come alive. I have greatly benefited from his knowledge and experiences. Special thanks are extended to members of my advisory committee. Dr. Thomas J. Algeo and Dr. J. Barry Maynard for their constructive criticism and valuable advice that were essential to the completion of this study. I should thank to the organizations who courteously provided assistance in many forms. These include, the
Clay Minerals Society, the Ford Nuclear Reactor at Michigan, the Department of
Geology at the University of Cincinnati. I am also thankful to Evelyn Pence, Sandi
Cannell, Brian Nicklen, Burcin Inanli and Atia Huff for their patience to my endless questions and for their support and advice. Finally, I would like to thank to my mother;
Esin Toprak for sending her warm support with daily messages, my father; Yakup Toprak for his understanding and encouraging me to start this academic program in University of
Cincinnati, my brother; Mustafa Toprak for his love and support enabled me to complete this study. TABLE OF CONTENTS
ABSTRACT ...... 2 ACKNOWLEDGEMENTS...... 4 LIST OF FIGURES...... ii LIST OF TABLES ...... v INTRODUCTION...... 1 1.1. Purpose of the Study...... 5 1.2. Previous Work on K-bentonites in Europe...... 6 GENERAL GEOLOGY...... 8 2.1. Paleogeography of the Study Area ...... 8 2.2. Stratigraphy of the Study Area...... 11 METHODS OF INVESTIGATION...... 20 3.1. Electron Microprobe Analysis ...... 20 3.2. Instrumental Neutron Activation Analysis...... 22 3.3. Powder X-Ray Diffraction Analysis...... 23 3.4. Scanning Electron Microscope Analysis...... 25 RESULTS AND DISCUSSIONS...... 27 4.1. MINERALOGY ...... 27 4.1.1. Clay Mineralogy ...... 27 4.1.2. Non-Clay Mineralogy...... 32 4.2. GEOCHEMISTRY AND TECTONIC SETTING...... 39 4.2.1.Biotite Geochemistry ...... 46 4.3. BINARY DISCRIMINATION DIAGRAMS ...... 51 4.3.1. Plots of Osmundsberg K-bentonite group...... 52 4.3.2. Comparative Plots of Osmundsberg and other K-bentonite groups...... 52 4.3.3. Discussion of the Binary Discrimination diagrams ...... 57 4.4. DISCRIMINANT FUNCTION ANALYSIS...... 58 4.4.1. Results of Discriminant Function Analysis...... 62 4.4.2. Discussion of The Results of the Discriminant Function Analysis...... 84 DISCUSSION AND CONCLUSIONS...... 87 REFERENCES...... 93 APPENDIX A ...... 100 APPENDIX B...... 106 APPENDIX C...... 115
i LIST OF FIGURES
Figure 1.1. Diagram showing the correlation of the Osmundsberg K-bentonite.
Figure 2.1. Sketch-map of paleogeography of the northern Iapetus region in Lower
Silurian (Llandovery) time
Figure 2.2. Sketch map showing geographic location of investigated Telychian K-
bentonite sections in Baltoscandia and the British Isles.
Figure 2.3. K-bentonite succession in the Osmundsberget section, the type section of
the Osmundsberg K-bentonite.
Figure 2.4. The type section of the Osmundsberg K-bentonite Figure 2.5. The lower parts of the type section of the Osmundsberg K-bentonite
Figure 2.6. 115 cm thick Osmundsberg K-bentonite in its type section in the Siljan
district, Dalarna
Figure 4.1. Representative powder X-ray diffraction tracings of ethylene glycolated,
< 2mm fraction of the Osmundsberg K-bentonite beds
Figure 4.2. Mixed layer illite-smectite X-ray diffraction tracings of the Osmundsberg
K-bentonite from its type locality by using computer program NEWMOD
Figure 4.3. Mixed layer illite-smectite X-ray diffraction tracings of the some
representative Osmundsberg K-bentonite samples using NEWMOD.
Figure 4.4. Photomicrographs of characteristic phenocrysts from the Osmundsberg K-
bentonite bed
ii Figure 4.5. SEM images of primary volcanogenic phenocrysts from the Osmundsberg
K-bentonite bed
Figure 4.6. General chemical profile of fresh biotite phenocrysts of the Osmundsberg
K-bentonite bed
Figure 4.7. General chemical profile of zircon crystals of the Osmundsberg K- bentonite bed Figure 4.8. General chemical profile of apatite crystals of the Osmundsberg K- bentonite bed Figure 4.9. Plot of fifteen Osmundsberg samples on a granite discrimination diagram Figure 4.10. Th/Yb versus Ta/Yb plot of the Osmundsberg K-bentonite samples Figure 4.11. Plot of Osmundsberg K-bentonite samples on a Th-Hf-Ta tectonic
discrimination diagram
Figure 4.12. Chondrite normalized REE patterns for fifteen Osmundsberg K-bentonite
samples.
Figure 4.13. Plot of Mg # versus MgO for the biotites of the Osmundsberg K-bentonite Figure 4.14. Plot of the biotites from the Osmundsberg K-bentonite samples on a
FeO*- MgO discrimination diagram
Figure 4.15. Ternary plot of the biotites of the Osmundsberg K-bentonite Figure 4.16. (a) Ti/1000-V covariance plot of the Osmundsberg K-bentonite samples
(b) Hf-Dy covariance plot of the Osmundsberg K-bentonite samples
(c) Yb-Hf/Ta covariance plot of the Osmundsberg K-bentonite samples
(d) Sc-Eu covariance plot of the Osmundsberg K-bentonite samples
iii Figure 4.17. (a) Eu-Th/Yb covariance plot of the Osmundsberg, Nova Scotia and
Podolia K-bentonite samples
(b) Zr-Eu covariance plot of the Osmundsberg, Wales and Podolia K-
bentonite samples
Figure 4.18. Graphical representation of the two linear discriminant functions Figure 4.19. Territorial map constructed from the two discriminant functions calculated
for the first model
Figure 4.20. Territorial map constructed from the two discriminant functions calculated
for the second model
Figure 4.21. Territorial map constructed from the two discriminant functions calculated
for the third model
Figure 4.22. Territorial map constructed from the two discriminant functions calculated
for the fourth model
Figure 4.23. Territorial map constructed from the two discriminant functions calculated
for the fifth model
Figure 4.24. Territorial map constructed from the two discriminant functions calculated
for the sixth model
Figure 5.1. Diagram showing the revised correlation of the Osmundsberg K-bentonite
iv LIST OF TABLES
Table 4.1 K-bentonite groups and their associated samples used to construct the binary diagrams Table 4.2. Samples and their pre-assigned groups of the first model used in the discriminant analysis Table 4.3. Samples and their pre-assigned groups of the second model used in the discriminant analysis Table 4.4. Samples and their pre-assigned groups of the third model used in the discriminant analysis Table 4.5. Samples, their pre-assigned groups and unknowns of the fourth model used in the discriminant analysis Table 4.6. Samples, their pre-assigned groups and unknowns of the fifth model used in the discriminant analysis Table 4.7. Samples, their pre-assigned groups and unknowns for the sixth model used in the discriminant analysis
v LIST OF APPENDICES
APPENDIX A
Table 1. Chemical composition of biotites of the Osmundsberg K-bentonite from its type section by electron microprobe analysis Table 2. Trace element abundances for the Osmundsberg K-bentonite accompanying beds
APPENDIX B
Figure 1. (a) Eu- Sm/Nd covariance plot of the Osmundsberg samples (b) Yb-Hf covariance plot of the Osmundsberg K-bentonite samples (c) Th-Eu covariance plot of the Osmundsberg K-bentonite samples (d) Yb-Sc covariance plot of the Osmundsberg K-bentonite samples (e) TiO2-MnO covariance plot of the Osmundsberg samples (f) Yb-Dy covariance plot of the Osmundsberg samples
Figure 2. (a) Yb-Th covariance plot of the Osmundsberg, and Nova Scotia K- bentonite samples (b) Eu-La/Lu covariance plot of the Osmundsberg, Nova Scotia and Podolia K-bentonite samples (c) Hf-V covariance plot of the Osmundsberg, and Podolia K-bentonite samples (d) Zr/Tb-Th/Yb covariance plot of the Osmundsberg, and Wales K- bentonite samples. (e) Eu-Ta/Yb covariance plot of the Osmundsberg and Nova Scotia K- bentonite samples f) Zr-Th/Yb covariance plot of the Osmundsberg, Wales and Nova Scotia K-bentonite samples.
APPENDIX C
Tables 1-6 Step-wise statistics for the first model Tables 7-12 Step-wise statistics for the second model Tables 13-18 Step-wise statistics for the third model Tables 19-24 Step-wise statistics for the fourth model Tables 25-30 Step-wise statistics for the fifth model Tables 31-36 Step-wise statistics for the sixth model
vi Chapter 1
INTRODUCTION
The Silurian marine successions in Baltoscandia and Northern Europe contain numerous widespread ash beds now altered to potassium rich clay beds or K-bentonites, which encompass a considerable time span of volcanic activity.
The term “ bentonite” was first used by Knight (1898) to describe a colloidal plastic clay in the Cretaceous Fort Benton Formation in Wyoming. Its name was derived from this formation where the first commercial deposits were mined. The use of the term
K-bentonite varies among geologists. Weaver (1953) introduced the term to the literature emphasizing the high interlayer potassium content compared with Na- and Ca- bentonites. Whereas, bentonite is a predominantly smectite-rich rock formed by the alteration of volcanic ash, K-fixation during diagenesis of bentonite transformed the original smectite into mixed layer illite-smectite with consequent loss of the characteristic swelling properties of younger beds.
Volcanic ash beds may be deposited hundreds of kilometers away from the source in highly explosive eruptions, but its preservation as pyroclastic units is dependent upon accumulation in a basin where background sedimentation rates are slow, and where reworking and erosion are minimal. These beds can provide evidence of their volcanic origin in spite of the alteration to bentonite during early diagenesis and the progressive illitization to form K-bentonite during late diagenesis.
1 K-bentonites have been recognized since the early 1900`s as useful stratigraphic tools. (Nelson ,1922). However, the potential offered by regional correlations of K- bentonite beds has only begun to be realized since 1960`s. Although several early studies attempted long-range correlations of individual beds (Kay, 1931), their use as regional stratigraphic markers was always limited by the difficulty in identifying individual beds and correlating them over long distances. Criteria such as lithology and clay mineralogy alone are not satisfactory. Studies shown that the K-bentonites are too similar in their clay mineralogy to be diagnostic feature for identifying individual beds (Weaver, 1953;
Huff, 1963).
Although mineralogy and petrology of K-bentonites have been studied in detail by early workers (Weaver, 1953; Huff, 1963), the use of chemical composition as a means to identify individual beds is a relatively new stratigraphic tool. Recent studies have demonstrated that the abundances of certain elements appear to be K-bentonite specific yielding a unique `fingerprint` for each bed (Huff 1981, 1983; Kolata et al. 1983,
1987; Huff and Lollis, 1983)
Because ash falls occurs in very brief intervals of time over wide areas, K- bentonites are essentially isochronous units that are potentially very useful as time lines.
If a single K-bentonite bed, or a series of beds, can be recognized in different stratigraphic sections, then the sections are considered to be contemporaneous.
Recently, a widespread ash bed of the Lower Silurian (Llandovery) age that occurs throughout Baltoscandia and parts of northern Europe has been traced over more than 2000 km from Estonia across Sweden to the British Isles by Bergström et al (1998)
(Fig 1.1). This series of K-bentonites is formally called the Osmundsberg K-bentonite,
2 named after a carbonate mound known as Osmundsberget in central Sweden. Previous studies suggest that the Osmundsberg K-bentonite represents the largest volcanic ash eruption during Silurian times (Bergström et al 1998). Numerous Silurian K-bentonite beds occur throughout northern Europe. Some of the beds only occur at local scales while others appear to be widespread on a regional scale.
The designation of Osmundsberg K-bentonite has been proposed for the thickest
Silurian K-bentonite bed (up to 115 cm) by Bergström et al (1998) and it has been traced from Estonia across Sweden to the British Isles using biostratigraphy. However, the occurrence of numerous K-bentonite beds in the investigated regions, similarity of the mineralogy, lack of continuity of individual beds and lack of good biostratigraphic control created some difficulties in correlating the Osmundsberg K-bentonite bed over long distances.
This study will focus on the use of chemical fingerprinting of K-bentonites to provide a high-resolution correlation of the Lower Silurian Osmundsberg K-bentonite in
Baltoscandia and Northern Europe using the stratigraphic relationships established by
Bergström et al. (1998) as a testable hypothesis.
3 Figure 1.1. Diagram showing the correlation of the Osmundsberg K-bentonite across Baltoscandia from Northern Ireland to Western Estonia proposed by Bergström et al. 1998. For location of sections, see inset map. Figures to the right of each column indicate ash bed thicknesses in centimeters (modified after Bergström et al 1998). 4 1.1. Purpose of the Study
The purpose of this study is to investigate the use of chemical fingerprinting, using trace elements, in the Lower Silurian Osmundsberg K-bentonite beds in
Baltoscandia and Northern Europe and to provide a high-resolution correlation. Further, this study will investigate how reliable the chemical fingerprinting is for large scale
Silurian correlations.
For this purpose, 15 Osmundsberg K-bentonite samples and 26 samples of accompanying beds from 12 different localities were analyzed for major and trace elements by instrumental neutron activation analysis. The resulting data were then statistically treated using discriminant function analysis to determine if the trace element data provides a unique chemical fingerprint for the Osmundsberg K-bentonite beds to verify the proposed correlation.
Another goal of this study is to investigate the use of the clay and non-clay mineralogy and geochemistry of the Osmundsberg K-bentonite as a stratigraphic tool in establishing the regional distribution pattern of the Osmundsberg K-bentonite.
The regional relationships of the Osmundsberg K-bentonite in Baltoscandia and
Northern Europe are of particular interest in studies of Silurian successions in these regions, since a time-stratigraphic marker is needed for solving problems of paleogeography during early Silurian time. The Lower Silurian (Llandoverian) paleogeography on the Iapetus region is somewhat controversial and not yet been well established. Beds of approximately the same age are known in the Central Appalachian
Mountains of North America, but it is unclear whether any of them can be correlated with the Osmundsberg (Bergström et al 1997). This research will seek to provide a valuable
5 and needed tool for further studies, and will attempt to provide an interpretation and an understanding of the Early Silurian tectonic and stratigraphic events that led to the formation of Baltoscandia and Northern Europe. Furthermore, the high-resolution correlation of the Osmundsberg ash bed will be a reference for a variety of regional sedimentological, environmental, paleoclimatological and biogeographical studies.
1.2. Previous Work on K-bentonites in Europe
The Silurian successions in Northwestern Europe contain numerous discrete K- bentonites produced by explosive volcanism. The significance of Baltoscandian K- bentonites remained unrecognized until the 1940`s, when Jassnusson and & Mannil
(1941) used the Ordovician K-bentonites in Baltoscandia as index horizons. This study was followed by the others. K-bentonites in Sweden, Norway, Denmark, and Estonia were investigated by some workers for example, Jurgenson (1958), Skoglund (1963),
Jaanusson (1964), Hagemann & Spjeldnaes (1955), Snall (1978), Huff et al. (1992).
Bergström et al. (1995) named and traced four K-bentonite beds (the Grefsen, Sinsen,
Kinnekulle, and Grimstorp K-bentonites) from Norway to Ingria in westernmost Russia.
Huff et al. (1992) and Bergström et al. (1995) have done numerous studies on the stratigraphic position, geographic distribution, geochemistry, and tectonomagmatic significance of Ordovician and Silurian K-bentonites in northwestern Europe and British
Isles. There at least 150 discrete Silurian K-bentonite beds in northwestern Europe. Of these, the best-documented ones occur in British Isles where more than 100 discrete exposures have been described (Huff & Morgan 1990; Romano & Spears 1991;
Batchelor & Clarkson, 1993). Substantial amount of work has been done to trace and
6 correlate Ordovician K-bentonites on a regional scale. Two prominent Ordovician ash beds, the Deicke and the Millbrig K-bentonites have been traced over more than 1000 km2 in eastern North America (Huff & Kolata, 1990; Huff et al. 1996). However, relatively little information was available on Silurian K-bentonites.
In recent years, more information has been gathered on Silurian K-bentonites.
Bergström et al. 1998 traced the Osmundsberg K-bentonite over a distance of 2000 km from Estonia to the British Isles using graptolite and conodont biostratigraphy, which is uncertain. This study aims to provide an additional tool for regional correlation by using chemical fingerprinting.
7 Chapter 2
GENERAL GEOLOGY
2.1. Paleogeography of the Study Area
Paleogeographic reconstructions are based primarily on biogeography and the distribution of faunal provinces, paleomagnetic data, latitude-sensitive sediments and facies distributions (McKerrow et al. 2000; Torsvik et al. 1996). Recently constructed
Lower Paleozoic paleogeographic maps show the positions of several plates in the
Iapetus region: Laurentia on the west, Baltica on the east, Avalonia and Gondwana on the south. These pieces were in the process of coming together during Early Silurian time.
Until late Ordovician time, northern and southern Britain had been separated by the wide
Iapetus Ocean. Scotland together with the north and west of Ireland, were part of the southeastern margin of the major continent Laurentia, which straddled the equator through much of the Early Paleozoic. England and Wales, together with south and east
Ireland, lay on the smaller continent of Avalonia, which also included parts of mainland
Europe to the east and fragments of the maritime states of North America to the west.
Avalonia had originated on the northern margin of Gondwana, the major continent straddling the early Paleozoic south pole. It rifted from Gondwana early in Ordovician time and the two plates moved northwards together from Late Ordovician time onwards.
During early Silurian (Llandovery) time, both Baltica and Avalonia began to impinge on the Laurentian continent, closing the Tornquist Sea between them. Continental crust started to choke the northward dipping subduction zone beneath Laurentia. The collision
8 of Baltica and Laurentia has been considered to have started in the late Llandovery causing the Scandian Orogeny (Torsvik et al. 1996). By late Silurian (Ludlow) time, the last remnant of the Iapetus Ocean had been destroyed and the Laurentian margin was being overthrust onto the British segment of Avalonia. This thrusting and associated crustal compression rapidly obliterated the marine Silurian basins and began to uplift parts of them above sea-level. Consequently marine and non-marine facies dominate the stratigraphical record from late Silurian (Pridoli) time into the Devonian (McKerrow et al. 1991).
Figure 2.1. Sketch-map of paleogeography of the northern Iapetus region in Llandovery time showing the thicknesses (in cm) of the Osmundsberg K-bentonite at 15 selected localities (black diamonds) in northwestern Europe. Black dots denote other areas with Llandovery K-bentonites (Bergström et al. 1998).
9 In addition to this generally accepted interpretation, there are several alternative models proposed for Silurian paleogeography (Torsvik et al. 1996). Lack of good biostratigraphical control and the presence of rapid facies changes are the major problems in reconstructions of Silurian paleogeography. The areal distribution of ancient volcanic ash deposits can provide important paleogeographical information. Event stratigraphy can provide a framework of a regional geologic history. Stratigraphic frequency and lateral distribution of K-bentonites can be used to assess recent models of the early Paleozoic evolution of the Iapetus Ocean (Huff et al. 1996).
Figure 2.1 shows the thicknesses of the Osmundsberg K-bentonite ash beds in
Baltoscandia and the British Isles. There is a general increase in thickness in the northwestern direction across Baltoscandia. The presence of the Osmundsberg K- bentonite in sections of southern Scotland and Northern Ireland is of special interest because in the most recent reconstruction of the Llandovery paleogeography (see
McKerrow et al. 1991; Torsvik et al. 1996), these parts of the British Isles are placed on the Laurentian site of the Iapetus Ocean. The question of the location of the volcanoes, which produced the Osmundsberg ash beds, is somewhat problematic. Bergström et al.
(1998) suggested a source area in the northern Iapetus Ocean based on a consideration of the general distribution pattern of the Lower Silurian K-bentonites in northwestern
Europe and eastern North America. However, some early workers (Bjerreskov &
Jorgenson, 1976) proposed an entirely different source region for Baltic Silurian K- bentonites, namely volcanoes in the Rheic Ocean south of the Baltic Platform.
10 Distribution patterns of widespread ash beds like the Osmundsberg K-bentonite have the potential to provide valuable information for the detailed paleogeographic reconstruction of early Silurian time, which has not been very well established yet.
2.2. Stratigraphy of the Study Area
Osmundsberg is a small village on the flanks of a large Middle and Upper
Ordovician carbonate mound known as Osmundsberget at Boda in the Siljan impact structure in the Province of Dalarna in central Sweden (Fig. 2.2) Osmundsberget is important in the history of the study of Silurian K-bentonites because such beds were described (as Fuller`s Earth) from this locality more than 250 years ago. This is probably the first K-bentonite record from the Silurian, not only in Sweden, but also in the entire world. The Osmundsberg K-bentonite was first discovered in central Sweden (Bergström et al. 1993) and additional information about its occurrence in Scandinavia and the
British Isles was gathered in 1993 based on its stratigraphic position and unusual thickness. Figure 2.1 illustrates the geographical location of investigated Telychian K- bentonite sections in Baltoscandia and the British Isles.
The interval between 3.3 and 8 m in the Osmundsberget North section (Fig. 2.3) contains graptolites of the sedgwickii Zone. Overlying, now partly covered, strata are referable in the turriculatus Zone. No detailed graptolite biostratigraphy has yet been published. Lack of good biostratigraphical control is the major problem in establishing the regional distribution pattern of the Osmundsberg ash beds.
11 There are nine K-bentonite beds present in the type section of the Osmundsberg
(Figs. 2.4-2.5). Six of them are less than 3 cm thick and the three others are 11, 32 and
115cm. The most prominent and thickest K-bentonite bed (115 cm) occurs in the lower portion of the turriculatus Graptolite Zone and about 5 m above the base of the D. staurognathoides Conodont Zone (Fig 2.6). This K-bentonite is quite widespread and was informally named as the Osmundsberg K-bentonite by Bergström et al. (1993).
According to Bergström et al. (1998) this ash bed is the thickest Silurian K- bentonite recorded anywhere in the world. Another occurrence of the Osmundsberg K- bentonite in the Siljan region is about 30 km northwest of Osmundsberget at the Kallholn quarry. There are ten K-bentonite sections of 15 cm or more. The most prominent one has a thickness of 25 cm, which has a sample number of SWE 132. This ash bed was interpreted as the Osmundsberg K-bentonite by Bergström et al. (1998).
12 Figure 2.2. Sketch map showing geographic location of Telychian K- bentonite sections in Baltoscandia and the British Isles. Red dots denote the locality of investigated the K-bentonites. 13 Several outcrops of fossiliferous Llandovery rocks are known from the Storsjon region in the Province of Jamtland in north-central Sweden. Two of these localities, namely Ange and Berge, have ash beds identified as the Osmundsberg K-bentonite by
Bergström et al. (1998). Four K-bentonites were observed in the outcrop at Ange, one of which has a thickness of 35 cm and it is in the lower turriculatus Zone. These ash beds
(SWE 96A, SWE 96 B) were interpreted as the Osmundsberg by Bergström et al. (1998).
In Berge, a 55 cm thick K-bentonite bed (SWE 93, SWE 94), which is in the same stratigraphic position as the prominent volcanic ash bed in Ange, occurs in the lower turriculatus Graptolite Zone. This bed was interpreted as the Osmundsberg K-bentonite by Bergström et al. (1998).
One of the biostratigraphically best known Llandovery sections in Sweden is that at Kullatorp, in the Province of Vastergotland. The outcrop in this region has seven K- bentonite beds, one of which is 6 cm thick and occurs in the turriculatus Zone. This bed here was identified as the Osmundsberg K-bentonite (Bergström et al. 1998). A similar
K-bentonite bed succession is present in the coeval stratigraphic interval in the Kallholn
Formation at Motala, in the Province of Ostergotland. This 8 cm thick ash bed (SWE
34A) was correlated with the Osmundsberg as well.
14 Figure 2.3. K-bentonite succession in the Osmundsberget section, the type section of the Osmundsberg K-bentonite. SWE 123,124 etc. are K-bentonite sample numbers. SE.-series; ST.-stage; FM.-formation; G.Z.-graptolite zone (Bergström et al. 1998).
15 Figure 2.4. The type section of the Osmundsberg K-bentonite along the northern entrance road to the Osmundsberget quarry in the Siljan District, Dalarna. White sample bags mark K-bentonite beds in the Llandovery Kallholn Shale. The Osmundsberg K-bentonite is exposed in the trench in central part of the photograph. The ruler is 2m long (Bergström et al. 1988). 16 Figure 2.5. The Lower parts of the K-bentonite succession, the type section of the Osmundsberg K-bentonite. Among nine beds present in this section, six are less than 3 cm thick and two others are 11 cm and 3 cm respectively.
Figure 2.6. 115 cm thick Osmundsberg K-bentonite in its type section in the Siljan district, Dalarna (Bergström et al. 1998).
17 One of the biostratigraphically best-controlled Telychian successions in
Baltoscandia occurs in Norway in the Lake Mjosa region. There are four bentonites in the turriculatus Zone. One of these beds (NOR 31) is much thicker than any of the others (20 cm), and was interpreted as the Osmundsberg K-bentonite (Bergström et al. 1998).
There are more than 20 K-bentonite beds in the Llandovery and Wenlock successions on the Island of Bornholm, easternmost Denmark. The best exposed
Telychian strata occur in Olea where much of the turriculatus Zone is covered in the sections. The only K-bentonite recorded in this zone is 1 cm thick (DEN 8) and it occurs in the Bjerreskov succession. Bergström et al. (1998) stated that the positive identification was not possible without chemical fingerprinting although it is in the same stratigraphic position as the Osmundsberg K-bentonite.
Compared to the sections in Sweden, K-bentonites beds are less common in north-central Europe. But a few Silurian ash beds have been recorded from Estonia
(Bergström et al 1992) and from Podolia, southern Ukraine (Huff et al. 2000). There is a prominent and widespread ash bed in Estonia, known as the `O` bed, which is used locally as a stratigraphic guide horizon in Rumba Formation. This ash bed lies in the lower Telychian and has a thickness of 6 cm (EST 124). This ash bed was correlated with the Osmundsberg K-bentonite by Bergström et al (1998).
K-bentonites of Llandovery age are known from a substantial number of localities in the British Isles (Fortey et al. 1996). The turriculatus Zone in the Buttington
Brickworks section in Wales contains several K-bentonite beds one of which was thought to be a correlative of the Osmundsberg K-bentonite (Huff pers. comm.). This ash bed has a thickness of 18 cm (WAL 7).
18 The well-known exposure at Dob`s Linn 16 km northeast of Moffat in southern
Scotland has one of the most extensive Lower Silurian K-bentonite bed successions known in northwestern Europe. Bergström et al. (1998) reported 49 individual K- bentonites in the Linn Branch outcrop at Dob`s Linn. Among the 49 beds, two beds (DL-
6B, DL-3) are distinctly thicker than the others. Each individual bed has a thickness of 17 cm. Bergström et al. (1998) stated that chemical fingerprinting is required to be able to correlate these ash beds with the Osmundsberg K-bentonite.
Another occurrence of Llandovery K-bentonites in southern Scotland is 8 km northeast of Dob`s Linn at Thirlestane Score. Among 12 K-bentonite beds observed in this locality, one (TS-2) is several times thicker than the others. Bergström et al. (1998) identified this ash bed as the Osmundsberg K-bentonite as well.
There are 11 K-bentonite beds in the Ballytrustan Shale Formation in County
Down about 40 km southeast of Belfast in Northern Ireland. Among these 11 ash beds, one of them has an unusual thickness (18 cm) and lies in the turriculatus Zone.
Bergström et al (1998) identified this ash bed (BT-6) as the Osmundsberg K-bentonite based on its stratigraphic position.
This study will focus on the chemical fingerprints of these K-bentonite samples to accept or reject the hypothesis that all of them are the Osmundsberg K-bentonite.
19 Chapter 3
METHODS OF INVESTIGATION
3.1. Electron Microprobe Analysis
Electron microprobe analysis (EMPA) is a technique for chemically analyzing small selected areas of polished solid samples, in which X-rays are excited by a focused electron beam. The resulting X-rays are diffracted by crystals, detected, and their intensities measured. The composition of the unknown is determined by comparison with
X-ray intensities from materials with known compositions (standards). The lowest detection limit of the microprobe is around 50 ppm ( Reed, 1996).
The chemical compositions of biotite grains from three Osmundsberg samples from its type locality were analyzed with a four-channel, Cameca SX-50 Electron microprobe at the University of Indiana at Bloomington microprobe facilities. An accelerating voltage of 15 kV and a beam current ranging between 15 and 30x 10-9 amp were the analytical parameters used for the measurements. An average beam diameter of
1 µm was used for analysis. The beam was continuously moved during analysis of Na2O to reduce the effects of alkali loss from the grains. Analysis of single biotite phenocrysts was carried out in multiple locations to check for variable compositions within single grains.
The energy dispersive spectrum (EDS) was used to determine the element compositions of each grain. All elements were detected by wavelength-dispersive
20 spectrometry (WDS). Analyses of biotite phenocrysts were carried out on portions of the grains where the alteration is minimal. Results are listed in Table 1, Appendix A.
Sample Preparation
Heavy mineral grains of the Osmundsberg were separated from light fraction using bromoform, with a specific gravity of 2.84 g/ml. Biotite grains were in both fractions due to variations in specific gravity as a result of alteration. The heavy mineral fraction was further separated using a Frantz magnetic separator at 0.5 and 1.0 amps. The
1.0-map fraction contained the most biotite grains and this fraction was used for microprobe analysis. Thin sections of biotites of three Osmundsberg samples from its type section, were prepared for the electron microprobe analysis.
The preparation procedure was the same as the for ordinary thin sections at the first stage. The thin sections were carefully hand polished using 0.05 and 1.0 µ alpha alumina. Then, the specimens were cleaned by ultra-sound to avoid excess abrasive powder, which accumulates in the pores and cracks of the specimen. These slides then were photocopied using a Minolta RP 605Z Microfiche photocopier to obtain larger images of biotite and pyroxene phenocrysts that were of possible interest to microprobe.
These phenocrysts were marked on the corresponding Microfiche photocopies, which served as location maps when the phenocrysts were analyzed.
Non-conductive samples were given an electrically conductive coating before placing them in the instrument. Since the biotites have low thermal conductivity, the thermal effect of the electron beam can be adjusted by using a good conductor as a surface coating. Carbon is the preferred coating because of its minimal effect on the X-
21 ray spectrum. The samples were placed in a vacuum chamber with a carbon evaporation source, which consists of carbon rods in contact under light pressure.
A current of 100 amp was passed through the carbon rods for a few seconds, causing carbon to evaporate from the hottest region where the rodes are in contact. After the samples were coated, they were placed in the sample chamber and the analysis was conducted.
3.2. Instrumental Neutron Activation Analysis
Instrumental neutron activation analysis (INAA) is a highly sensitive analytical technique useful for both qualitative and quantitative analyses of major, minor, and trace elements in samples from a broad range of scientific fields. Since INAA utilizes nuclear reactions, element analyses are not affected by chemical composition or crystal structure of the specimens.
INAA analysis of 41 whole rock Osmundsberg samples from Baltoscandia and the British Isles was carried out at Ford Nuclear Reactor facilities at the University of
Michigan. The data generated by INAA resulted from two separate irradiations (in-core and pneumatic tube), each followed by two separate counts of gamma activity. The data for elements with intermediate and long half-life isotopes (including As, Ba, La, Lu, Na,
Sm, U, Yb, Ce, Co, Cs, Eu, Fe, Hf, Nd, Rb, Sc, Tb, Th, Zn, Zr ) result from a 10 H core- face irradiation with an average thermal neutron flux of 4.2 x 1012 n/cm2/s. Following irradiation, two separate counts of gamma activity were done: a 5000-second count (live time) after a period of 5 weeks decay. The data for short half-live isotopes (Al, Ca, Ti, V,
K, Mn, Na ) result from a 1-minute core-face irradiation delivered via pneumatic tube to
22 a location with an average thermal flux of 2.13 x 1012 n/cm2/s. Again, two separate counts were made, one after a 13-minute decay (PT) and a second count after a 1 hour and 56 minute decay (1H); both were for 500 seconds.
The concentrations of most elements were determined based on the comparison with three replicates of the standard reference material, which is coal fly ash. The determination of Ca content was based on basalt. The elemental abundances are listed in
Table 2, Appendix A.
Sample Preparation
Due to the relatively high water content in bentonitic materials, samples were dehydrated by heating at 60°C for approximately 12 hours. The dry samples were then powdered using a tungsten carbide ball-mill. Roughly 2 grams of powder from each sample were placed in a clean crucible and further dried at 93.3°C in a desiccating oven for 48 hours. After heating, 200-250 milligrams of each sample weighed into 250 ml polyethylene v-vial. As a final step, each vial was labeled and sealed before sending them for elemental analysis. Samples were analyzed in the Phoenix Memorial Lab/Ford
Nuclear Reactor located at the University of Michigan, Ann Arbor, as part of the
Department of Energy Reactor Sharing Program.
3.3. Powder X-Ray Diffraction Analysis
Powder X-ray diffraction (XRD) is a reliable and relatively quick method for mineral identification. All of 41 samples were analyzed with a Siemens D-500 X-ray powder diffractometer using Cu Ka radiation. Ethylene-glycolated, oriented slides were run at a step-size 0.05 seconds and a count time per step 1 second from 2 degrees through
23 32 degrees 2q. After the initial run as air-dried mounts, samples were placed in an ethylene glycol vapor bath in a sealed glass container at 60 0C for 24 hours and heated to
350 0C as separate treatments. In some cases, samples were heated up to 500 0C for kaolinite analysis (Chen, 1977). The mixed layer phases in the clay fraction were determined by the computer program NEWMOD (Reynolds, 1985) using the diffractogram patterns of ethylene glycolated samples.
Sample Preparation
Proper sample preparation is one of the most important requirements in the analysis of samples by powder X- ray diffraction. This statement is especially true for clays that contain fine colloids, which are poor reflectors of x-rays. For this study, 41 samples were analyzed by XRD to determine their clay mineralogy. However, representative samples are presented here. Samples were air-dried before they were ground. 10-15 g of each sample was lightly ground by hand using a mortar and pestle. The pulverized samples were dispersed using a kitchen type rotary mixer and than suspended in 500 ml of deionized water for the separation of clay by gravity settling to obtain the < 2 mm fraction and then the sample was left undisturbed for 3 hours and 45 minutes, time required for non-clay size particles (>2mm) to settle at least five centimeter according to Stoke`s Law.
The upper five centimeter of the suspension was then poured into centrifuge tubes and centrifuged at 10, 000 rpm for five minutes to concentrate clay fraction. The clay was then collected with a spatula and mounted onto a petrographic slide, preferably one that had a well etched on surface using the `Smear Mount Method ` described by Moore and
Reynolds (1977). The smeared clay should have a smooth surface and be thick enough to
24 prevent transmission of X-rays. After the slides were made, they were labeled and set aside to dry.
In some cases, the samples contained considerable amount of carbonate material, which promotes the flocculation of the clay. It was removed by heating it gently in1 N
HCl acid in a beaker. After effervescence stopped, the acid was drained off using a funnel and the sample was washed thoroughly using acetone followed by deionized water to remove the acid and any remaining ion, which might promote flocculation.
After excessive organic content was removed, the procedure described earlier was followed and samples were placed into the XRD instrument to be analyzed.
3.4. Scanning Electron Microscope Analysis
The scanning electron microscope (SEM) is a close relative of the electron microprobe but its primarily for imaging rather than analysis. It consists essentially of the following; a source of electrons, means for focusing them to a fine beam, facilities for sweeping the beam in a raster, arrangements for detecting electrons emitted by the specimen, and an image display system. Scanning the beam in a television-like raster and displaying the signal from an electron detector on the screen produce images. The primary electron beam hits the specimen, which causes the specimen to emit secondary electrons. The electrons are attached to the positively charged detector electrode. As they travel through the gaseous environment, collisions occur between an electron and a gas particle results in emission of more electrons and ionization of the gas molecules. The positively charged gas ions are attracted to the negatively biased specimen and offset charging effects. If a large number of electrons are emitted from a position on the
25 specimen during a scan, there is a high signal. If only a small amount of electrons are emitted the signal is less intense. The difference in signal intensity from different locations on the specimen allows an image to be formed.
A number of zircon, apatite, and biotite phenocrysts from the Osmundsberg K- bentonite were chemically analyzed and photographed with were analyzed with Hiatachi
S-4000 Field Emission Scanning Electron Microscope at the University of Cincinnati facilities. The results of chemical analysis and the SEM images of phenocrysts are illustrated in Figures 4.4 through 4.7.
Sample Preparation
Sample preparation is an important aspect of microanalysis. At the first stage, samples were cleaned and dried to remove any water by placing them in a low temperature oven and then the heavy mineral separation was carried out as described in section 4.1.2 to collect the phenocrysts. Then, a piece of double-sided carbon tape was attached to the SEM stub where the samples were placed. Once the samples was attached to the stubs and they were given a gold coating to make the samples electrically conductive by using a device called sputter coater. The sputter coater uses argon gas and a small electric field. The samples were placed in a small chamber, which was at vacuum.
Argon gas was then introduced. The Ar ions are attracted to a negatively charged piece of gold foil. The Ar ions knock the gold atoms from the surface of the foil, and gold atoms settle onto the surface of the sample, producing a gold coating. This conductive coating prevents the charging under electron bombardment. As a final step, the samples were labeled and placed into the SEM to be analyzed.
26 Chapter 4
RESULTS AND DISCUSSIONS
4.1. MINERALOGY
The mineralogy of K-bentonites must be investigated carefully in order to distinguish between components of the terrigenous and volcanic origin. The
Osmundsberg and the associated beds consist mainly of clay minerals accompanied by a variety of primary phenocrysts, which are good indicators of a volcanic origin, and secondary sulfides, sulfates, oxides, carbonates and silicates. The dominant non-clay mineral composition of the Osmundsberg consists of biotite, quartz, apatite and lesser amounts of zircon, sanidine and calcite, gypsum, ilmenite and pyrite as secondary mineral phases.
4.1.1. Clay Mineralogy
Clay minerals are sensitive to the thermal conditions and geochemical environments that have characterized their post-emplacement history. Paleozoic K- bentonites are typically characterized by mixed-layer illite-smectite (I/S) assemblages with the illite as the dominant phase, in contrast to Mesozoic and Cenozoic K-bentonites are usually dominated by smectites. Previous studies have concluded that I/S in K- bentonites as well as in shales is a diagenetic product of smectite alteration (Altaner et al.
1984) and further alteration to C/S occurs under low grade metamorphic conditions
(Bergström et al. 1997).
27 Samples of the < 2 mm clay fraction of the Osmundsberg K-bentonite bed from the type locality and other associated beds were prepared as oriented specimens on glass petrographic slides. The clay mineral compositions were determined by powder X-ray diffraction (XRD) analysis of the glycol-saturated specimens, and the data for the representative K-bentonite beds are shown in Figure 4.1.
The Osmundsberg and the associated beds contain abundant mixed layer illite- smectite, as illustrated by the glycolated XRD patterns. The samples from the type locality of the Osmundsberg have a single prominent peak at 16.8 A0, which is interpreted as randomly stratified (R0) illite-smectite (Moore and Reynolds, 1989).
Modeling of the diffraction tracings using NEWMOD (Reynolds, 1985) showed that samples contain 19 % illite and 81 % smectite (Fig. 4.2). This is consistent with previous reports of Silurian K-bentonite composition (Bergström et al. 1997). The presence of 7.1 and 3. 57 A0 reflections are interpreted as Kaolinite and there is some minor amount of quartz accompanied by some carbonates and feldspars. One sample was characterized by the presence of chlorite/smectite (SWE 132). Although all Osmundsberg samples are characterized by mixed layer illite-smectite phases (Fig 4.3), associated illite percentages changes from 10 % to 90 % through short range (R1) to long-range interstratifications
(R3). This might be explained by the different diagenetic conditions under which the beds are formed. Different intensities of secondary burial an/or uplift must be responsible for the varying amounts of illite percentages. Therefore, clay mineralogy is not a particularly diagnostic feature of the Osmundsberg K-bentonites. However, clay mineralogy provides a confirmation that these beds are actually K-bentonites derived from a volcanic source.
28 I/S
I+Q
I/S I
I+I/S K F K Q I/S WAL-7
EST-124
SWE34A
DEN-8
TS-2
BT-6
C/S NOR-31
SWE-132
OSM
5 10 15 20 25 30 °2Q CuKa Figure 4.1. Representative powder X-ray diffraction tracings of ethylene glycolated, < 2mm fraction of the Osmundsberg K-bentonite beds. OSM is the sample from the type locality of the Osmundsberg K-bentonite. I/S is mixed-layer illite/smectite, I is illite, Q is quartz, K is kaolinite, C/S is mixed-layer chlorite/smectite, F is feldspar.
29 Figure 4.2. Mixed layer illite-smectite X-ray diffraction tracings of the Osmundsberg K- bentonite from its type locality by using computer program NEWMOD.
30 Figure 4.3. Mixed layer illite-smectite powder X-ray diffraction tracings of the some representative Osmundsberg K-bentonite samples using NEWMOD.
31 4.1.2. Non-Clay Mineralogy
Mineral isolation of the samples was achieved by several methods. K-bentonite samples were disaggregated by soaking in water and then agitating in a blender. Samples were then wet-sieved using 200 mesh polyester sieve. Heavy grains were further separated from light ones using bromoform, with a specific gravity of 2.84 g/ml. Biotite grains were found in both fractions due to the variations in specific gravity as a result of alteration of some grains. The heavy mineral fraction was further separated using a Frantz magnetic separator set at 0.5 and 1.0 amps. The 1.0-amp fraction contained the most biotites and this fraction was used for microprobe analysis. The non-magnetic fraction was separated by methylene iodide with a specific gravity of 3.1 g/ml. Zircon crystals were in the heavy fraction, while apatite crystals are in the light fraction. Isolated grains were identified using petrographic and binocular microscope in addition to the scanning electron microscope with the energy-dispersive x-ray capabilities.
Biotite is a common primary magmatic phase and accounts for 15 to 40 % of the phenocrysts in the Osmundsberg samples. It changes from disseminated flakes to large euhedral flakes, which range from very dark brown to colorless and transparent depending on their degree of alteration (Fig. 4.4a). Altered biotite flakes occur mainly in the basal portion of the Osmundsberg K-bentonite. Individual flakes range from 85 to
280mm in diameter, substantially larger than accompanying phenocrysts1 as a function its platy shape (Fig 4.5a-b). The general energy-dispersive x-ray spectra of biotite grains is given in figure 4.6. Zircons occur as pale pink and yellowish red euhedral elongate prismatic crystals (Fig. 4.4b). The size of the crystals ranges from 80 to 150mm and the quantity compared to the other primary phenocrysts is very small.
32 a
b
33 c
Figure 4.4. Characteristic phenocrysts from the Osmundsberg K-bentonite bed (a) photomicrograph of biotite flakes (b) photomicrograph of euhedral and zoned zircon crystals (c) photomicrograph of euhedral and etched apatite crystals.
Zircon crystals have very sharp edges and show complex zoning patterns in polarized light. Figures 4.5e and 4.5f illustrate the tubular cavities in zircon crystals which may be the remnants of older zircon from pre-existing basement rocks with the precipitation of additional zircon by the Osmundsberg magma (Bergström et al. 1997).
Some crystals contain melt inclusions. The general x-ray spectra of zircon crystals was obtained by using scanning electron microscope with EDAX capabilities, which is illustrated in figure 4.7. Apatite occurs as milky white to colorless euhedral prismatic crystals (Fig. 4.4c). Some of them are broken or rounded. They are slightly larger than zircon crystals (90-200mm) and more abundant (Figs 4.5c-d). The general energy- dispersive x-ray spectra of apatite crystals is given in figure 4.8.
34 a b
a a
a
a
c d
a a
a
a
e f
a a
a a
a a
Figure 4.5. SEM images of primary volcanogenic phenocrysts1 from the Osmundsberg K- bentonite. (a) and (b) SEM images of flakes, (c) and (d) SEM images of apatite phenocrysts, (e) and (f) SEM images of zircon phenocrysts.
1 Phenocryst is here used to describe juvenile crystal pyroclasts, which are products of parental magma crystallization and have been emplaced as discrete fallout particles (see Huff et al. 1996).
35 Figure 4.6. General chemical profile of fresh biotite phenocrysts the Osmundsberg K-bentonite.
36 Figure 4.7. General chemical profile of zircon crystals the Osmundsberg K-bentonite.
37 Figure 4.8. General chemical profile of apatite crystals the Osmundsberg K-bentonite.
38 4.2. GEOCHEMISTRY AND TECTONIC SETTING
Immobile trace elements and rare earth elements (REE) have been used by numerous workers to provide information on the magmatic composition of K-bentonites and tectonic setting of the source volcanoes (Merriman and Roberts; 1990 Huff &
Morgan 1990). Geochemical information about the tectonomagmatic origin of the
Osmundsberg K-bentonites is provided by the use of several widely referenced discrimination plots. For K-bentonites, which are altered remnants of volcanic ash, immobile trace elements provide information about original magma chemistry (Huff &
Morgan 1990). High field strength elements e.g. Ta, and rare earth elements are commonly considered to be immobile under most upper crustal conditions are thus useful indicators of petrogenetic processes (Teale & Spears 1986; Huff & Morgan 1990). In
Figure 4.8, a plot of Ta against Yb shows that the majority of the Osmundsberg samples lie in the field of volcanic arc granites and some samples are in the field of syn-collision granites as defined by Pearce, Harris & Tindle (1984). This distribution pattern suggests that Osmundsberg magma can be product of mixing of two or more magmas in various proportions (Huff et al.1997).
Trace element ratios have also been found to be useful in classifying magmas. Figure
4.9 is such a diagram showing the variation of Th/Yb versus Ta/Yb of the Osmundsberg samples (Pearce et al. 1984). Yb is the denominator in both of these ratios, and this has the effect of largely eliminating variations due to partial melting and fractional crystallization processes, allowing attention to be focused on source composition.
39 100 Osmundsberg SWE 132 DEN 8 BT 6 TS 2 WAL 7
10
ORGb Ta Syn-COLG
1
ORG
VAG
0.1 0.1 1 10 100
Yb
Figure 4.9. Plot of fifteen Osmundsberg samples on a granite discrimination diagram after the method of Pearce, Harris & Tindle (1984). All samples indicate affinities with a volcanic to syn-collision tectonomagmatic setting. WPG-within- plate granites; ORG-ocean ridge granites; VAG-volcanic arc granites; Syn- COLG- syn-collision granites.
40 Mid-oceanic ridge basalts (MORB) and uncontaminated intra-plate basalts plot within a well-defined band with a slope of unity, as mantle enrichment events appear to concentrate Ta and Th equally. In contrast, island-arc and active continental margin basalts are displaced to higher Th/Yb ratios. Five Osmundsberg samples (SWE 132, TS
2, DEN 8, BT 6 and WAL 7), which have slightly different chemistry, plot away from the region defined by the majority of the Osmundsberg samples (Fig.2.2)
The Th-Hf/3-Ta tectonic discrimination diagram of Wood (1980) shows that all of the Osmundsberg K-bentonite samples fall in the category of Th-enriched, calc-alkaline, destructive plate margin volcanics (Fig. 4.10). Huff et al. (1997) showed that the parent magma composition of Osmundsberg K-bentonite were trachyandesite to rhyodacitic in composition. All of the information from discrimination plots suggests that the
Osmundsberg K-bentonite beds were produced by volcanoes situated along a collision margin setting related to subduction. A similar conclusion was reached by Huff et al.
(1993,1997) for late Ordovician and early Silurian K-bentonites in Baltoscandia.
41 100 Osmundsberg SWE 132 DEN 8 BT 6 TS 2 10 WAL 7
WPG
CA 1 Th/Yb
OA s c w
0.1 f
N-MORB
0.01 0.01 0.1 1 10
Ta/Yb
Figure 4.10. Th/Yb versus Ta/Yb plot of the Osmundsberg K-bentonite samples. Overall Th enrichment suggests some mixing of crustal material, but essentially the data define a trend parallel to the fractional crystallization trend of upper mantle rocks (after Pearce 1984). CA- continental arc; OA- oceanic arc; MORB-mid-oceanic ridge basalt; WPB; within plate basalt
42 Figure 4.11. Th-Hf-Ta tectonic discrimination diagram of Wood (1980) showing that the Osmundsberg K-bentonite samples fall in the category of Th- enriched, calc-alkaline, destructive margin volcanics related to the subduction.
43 Rare earth element data of the Osmundsberg K-bentonite samples are normalized to C1 chondrite, which represent the best estimate of primitive solar nebula abundances and have general acceptance (Taylor & Mclennan, 1988). Normalization smoothes out the zigzag natural pattern of REE concentrations, which is caused by the domination of even-numbered lanthanides. For this study, normalizing factors are taken from Evensen et al (1978). Figure 4.12 illustrates the chondrite normalized plots of 15 Osmundsberg K- bentonite samples. REE Plots show an overall enrichment in LREE approximately 20-
200 times chondritic .The lack of pronounced negative Eu anomaly suggests an incomplete crystallization of plagioclase. Eu/Eu* >1 corresponds to a positive anomaly and Eu/Eu* <1 corresponds to a negative anomaly and indicates depletion Eu. The average Eu/Eu* value for the Osmundsberg samples is 0.84 using geometric mean method with a standard deviation of 0.20. These features are characteristic of calc- alkaline magmas erupted in subduction-related volcanic arc environments (Taylor &
McLennan, 1988) and resemble previously reported Ordovician and Silurian K-bentonite compositions (Merriman & Roberts, 1990; Huff et al.1997)
.
44 1000
Osmundsberg BT6 100 DEN8 EST124 NOR31 SWE132 SWE34A SWE93 Rock/Chondorite SWE94 10 SWE96A SWE96B TS2 WAL7
1 La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
Figure 4.12.Chondrite normalized REE patterns for fifteen Osmundsberg K-bentonite samples. Filled area represents the samples from the type section of the Osmundsberg. All are enriched in LREE. Normalizing values are from Evensen et al (1978). 45 4.2.1.Biotite Geochemistry
Thirty-two unaltered biotite grains from the type section of the Osmundsberg K- bentonite bed were analyzed for SiO2, Al2O3, MgO, Na2O, NiO, FeO*, MnO, K2O, CaO,
TiO2, V2O3 and Cr2O3, by electron microprobe. 20 of the biotite grains were isolated from the top of the Osmundsberg and 2 from the middle part and the 10 grains were from the bottom of the Osmundsberg in order to assess the vertical variation in biotite chemistry.
Magnesium and iron values in biotite grains are only slightly higher than the coexisting melt, (Hess, 1989) so the biotite composition is a reflection of the melt composition at the time of biotite crystallization. The data were used to calculate the magnesium number, which is a widely applied fractionation index, assuming a mantle- derived primary magma. The magnesium number is calculated with the equation Mg+2 /
(Mg+2+Fe+2)*100. (Tatsumi & Eggins, 1995). Primary magma generated from the mantle has a magnesium number of 90. In early stages of fractional crystallization, magnesium- rich minerals such as olivine and pyroxene crystallize out, so differentiated magmas have a lower magnesium number under low pressure conditions. Typical subduction zone magmas have a number around 60-70. Samples from the Osmundsberg have magnesium numbers between 54.3 and 58.7 indicating moderately fractionated magma. Figure 4.13 compares the magnesium number of the Osmundsberg samples with MORB values. It can be seen from the figure that the Osmundsberg samples do not come from a highly evolved magma, which is consistent with the previous findings (Bergström et al. 1998).
46 30 SWE 129 SWE 130 25 SWE 131 MORB
20
15 MgO
10
5
0 35 40 45 50 55 60 65 70
Mg +2 / (Mg +2 +Fe +2 )*100 Figure 4.13. Plot of Mg # versus MgO showing the moderately evolved nature of the source magma for the biotites of the Osmundsberg K-bentonite. 47 The total oxide weight percent of biotite samples varies between 97.71 and 100.82
%. Missing weights are due to the volatiles such as water and fluorine in biotite crystals.
The K2O concentration ranges between 7.3 and 9.2 % in unaltered biotites, and goes as low as 1 % for the altered biotites.
Biotite data were plotted on a FeO*-MgO discrimination diagram constructed by
Abdel-Rahman (1994). All biotite samples from the Osmundsberg fall within the calc- alkaline magma source region (Fig. 4.14). Then the data were plotted on a ternary plot in
MgO, Al2O3 and FeO* space. This plot is based on data of known volcanic compositions from 26 separate volcanoes and 329 biotite samples representing alkaline, calc-alkaline and peraluminous magma sources (Abdel-Rahman, 1994) (Fig. 4.15). Alkaline magmas are iron-rich, due to crystal fractionation and the fact that iron oxides and iron-titanium oxides form late in the fractionation sequence (Abdel-Rahman, 1994). Calc-alkaline magmas are relatively magnesium-rich as a result of increased water content that allows iron oxides and iron rich amphiboles to crystallize early, removing iron from the system.
Peraluminous magmas are enriched in aluminum due to partial melting of the continental crust, with abundant aluminum-rich minerals.
All the biotite samples of the Osmundsberg plot within calc-alkaline magma source region consistently.
48 20 SWE 129 SWE 130 SWE 131 15 C
P 10 A MgO (wt %)
5
0 5 15 25 35 45 FeO* (wt %)
Figure 4.14. FeO*- MgO biotite discrimination diagram. A= Alkaline orogenic suites, C= Calc-alkaline orogenic suits, P= Peraluminous orogenic suites. FeO* = total Fe as FeO. 49 Figure 4.15. Ternary plot of the biotites of the Osmundsberg after the method of Abdel- Rahman (1994). A=Alkaline orogenic suites, C=Calc-alkaline orogenic suits,P= Peraluminous orogenic suites. FeO* = total Fe as FeO.
50 4.3. BINARY DISCRIMINATION DIAGRAMS
Binary discrimination diagrams provide a simple way to show the distribution of samples as points on two-dimensional graphs using variables which define the vertical and horizontal axes. The variables in this case are elements, element ratios or oxide concentrations. Such diagrams have been widely used by many workers to show compositional differences between K-bentonite horizons. To test the stratigraphic usefulness of fingerprinting and to demonstrate the degree of discriminating power of the elements relative to each other, chemical data were plotted on a series of binary diagrams using several of the best discriminating elements and elemental ratios. Figures 4.16a through 4.16d and figures 1a through 1f in Appendix B show the chemical grouping of the Osmundsberg (Llandovery) K-bentonite samples from Baltoscandia and British Isles using various pairs of elements that are thought to be relatively immobile, as discriminating variables. The following table lists the samples and the localities used to construct the binary diagrams.
Group # Locality Samples used in analysis 1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7
2 Nova Scotia, Canada 92B33-1, 92B33-2, 92B33-3, 92B33-4, 92B33-5, 92B34-1, 92B35-1 3 Podolia, Ukraine c31 , c32, c6, m7
4 British Isles (Wales) WDH-26, WDH-28, WDH-29, WDH-30, WDH-31, WDH-47
Table 4.1 K-bentonite groups and their associated samples used to construct the binary diagrams.
51 4.3.1. Plots of Osmundsberg K-bentonite group:
More than 300 binary discrimination diagrams were constructed using various combinations of elements and element ratios, but the elements used to construct the following diagrams were found to be the ones that show clearest discrimination between the Osmundsberg K-bentonites. The following are the binary discrimination diagrams constructed to analyze the chemical similarities and differences between 15 Osmundsberg
K-bentonite samples, all of which are considered to be Osmundsberg equivalents;
Ti/1000 –V (Fig. 4.16a), Hf – Dy (Fig. 4.16b), Hf/Ta–Yb (Fig. 4.16c), Sc- Eu (Fig.
4.16d), Eu - Sm/Nd (Fig. 1a, Appendix B), Yb –Hf (Fig. 1b, Appendix B), Eu –Th (Fig.
1c, Appendix B), Yb - Sc (Fig. 1d, Appendix B), TiO2 – MnO (Fig. 1e, Appendix B),
Yb -Dy (Fig. 1f, Appendix B).
Figures 4.16 a through d and figures 1a through 1 f (Appendix B) show how the
Osmundsberg samples are separated using different pairs of elements and element ratios as discriminators. There are five samples, described by Bergström et al (1998) as probable Osmundsberg that do not plot within the territory defined by the Osmundsberg.
These samples are the ones from Sweden, Scotland, Denmark, Northern Ireland and
Wales (SWE 132, TS 2, DEN 8, BT 6 and WAL 7) that have anomalous chemistry.
Therefore it is unlikely that they are Osmundsberg equivalents.
4.3.2. Comparative Plots of Osmundsberg and other K-bentonite groups:
Figures 4.17 a-b and figures 2a through 2f (Appendix B) show the chemical grouping of the Osmundsberg K-bentonite samples together with the samples from Nova
52 1000 ( a ) Osmundsberg SWE 132 TS 2 DEN 8 BT 6 WAL 7
V 100
10 1 10
Ti/1000
100 Osmundsberg ( b ) SWE 132 TS 2 DEN 8 BT 6 WAL 7
Hf 10
1 0.1 1 10 100
Dy
53 ( c ) Osmundsberg SWE 132 10 TS 2 DEN 8 BT 6 WAL 7 Yb
1
0.1 1 10 100
Hf/Ta
Osmundsberg ( d ) SWE 132 TS 2 DEN 8 BT 6 WAL 7
10 Sc
1 0.1 1 10
Eu
54 Figure 4.16. (a) Ti/1000-V covariance plot of the Osmundsberg K-bentonite samples. (b) Hf-Dy covariance plot of the Osmundsberg K-bentonite samples. (c) Yb-Hf/Ta covariance plot of the Osmundsberg K-bentonite samples. (d) Sc-Eu covariance plot of the Osmundsberg K-bentonite samples.
Scotia, Podolia, and Wales using various pairs of Eu, Zr, La, Lu, Dy, Hf, Sc, Yb, Tb, Th,
V as discriminating variables. These K-bentonite groups are chosen because two of them
(Wales, Nova Scotia) are of same age as the Osmundsberg K-bentonite and the other group (Podolia) is slightly different in age than the Osmundsberg K-bentonite. These groups will be analyzed to see the chemical similarities and differences between these K- bentonite groups as a function of their age and geographic location. Numerous binary discrimination diagrams were constructed using various combinations of elements and element ratios, but the elements used to construct the diagrams illustrated here were found to be the ones, that has the greatest discrimination between these K-bentonite beds from various locations. The following is the binary discrimination diagrams constructed to analyze the chemical differences between the Osmundsberg and other three K- bentonite groups; plots of Eu- Th/Yb (Fig. 4.17a), Zr-Eu (Fig. 4.17b), Yb- Th (Fig. 2a,
Appendix B), Eu- La/Lu (Fig 2b, Appendix B), V-Hf (Fig. 2c, Appendix B), Zr/Tb-
Th/Yb (Fig. 2d, Appendix B), Eu-Ta/Yb (Fig. 2e, Appendix B), Zr-Th/Yb (Fig. 2f,
Appendix B)
Figures show the separation of the groups when trace elements and elemental ratios are used as discriminators. The Osmundsberg samples plot away from the other three groups and show good clustering. Although, there is some overlap between groups, the groups have acceptable amount of separation between them. As with the previous binary diagrams, five members of the Osmundsberg K-bentonite group (SWE 132, TS 2,
DEN 8, BT 6 and WAL 7) do not cluster with the other Osmundsberg group members.
55 10 ( a )
Eu 1
Osmundsberg Nova Scotia Podolia SWE 132 TS 2 DEN 8 BT 6 WAL 7 0.1 1 10 100
Th/Yb
1000 Osmundsberg ( b ) Wales Podolia SWE132 TS 2 DEN 8 BT 6 WAL 7 Zr
100 0.1 1 10
Eu
56 Figure 4.17. (a) Eu-Th/Yb covariance plot of the Osmundsberg, Nova Scotia (Llandovery) and Podolia (Ludlow) K-bentonite samples. (b) Zr-Eu covariance plot of the Osmundsberg, Wales (Llandovery) and Podolia (Ludlow) K-bentonite samples.
4.3.3. Discussion of the Binary Discrimination diagrams
Figures 4.16 a-d and figures 1a through 1 f (Appendix B) show that the members of the Osmundsberg K-bentonite group are very well clustered together with some consistent exceptions. There are five samples, SWE 132, TS 2, DEN 8, BT 6 and WAL 7, which have slightly different chemistries and consistently plot away from the territories defined by the Osmundsberg samples. The same observation can be made when two or more chemically different K-bentonite horizons are plotted on binary diagrams (Figures
4.17 a-b). Some incompatible elements, which are not considered to be highly mobile
(Huff, 1983), such as Yb, Sc, Eu, Zr, Hf, V, Ta, Ti, Th, Dy, and the ratios of some elements, La/Lu, Sm/Nd, Hf/Ta, Th/Yb and some oxides, TiO2 and MnO, were found to be effective discriminators. Eu and Zr were the most effective elements to distinguish between different K-bentonite horizons.
This straightforward form of sample classification indicates that an identifiable chemical signature does exist for each K-bentonite horizons, but it does not provide a quantitative expression of that signature nor does it permit criteria for optimal classification of unknown samples. Therefore, to achieve further separation of the K- bentonite groups, discriminant function analysis was applied to the chemical data, which is discussed in detail in the following chapter.
57 4.4. DISCRIMINANT FUNCTION ANALYSIS
Discriminant function analysis is a powerful multivariate statistical technique that seeks to statistically distinguish between two or more groups of samples using a set of variables that are thought to differ between the groups (Klecka, 1975). The discriminant function is especially useful in detecting subtle combinations of variables that, when considered together, result in a larger group difference than any of the variables considered alone. If that difference is significant, then the computed discriminant functions can be used to assign unknown samples to one of the groups.
In this study, discriminant analysis was used to test the null hypothesis that samples from the same K-bentonite bed are chemically identical, and they can be discriminated from other beds. If this hypothesis can be proved, then it can be said that the chemical variation is not random and each bed has a unique chemistry. If this is true, then the samples from the same K-bentonite bed would be chemically similar to each other and would be successfully discriminated from the other K-bentonite beds using discriminant analysis. Huff (1981, 1983) and Kolata et al. (1983) and others demonstrated that this assumption is only valid for minor element (trace, rare earth elements etc.) chemistry for K-bentonites. Since the major element chemistry of bentonites is not a powerful discriminator due to being so similar to each other and also subject to gain or loss during devitrification and diagenesis. However, it should be remembered that a K-bentonite stratigraphic model supported by discriminant analysis does not necessarily prove the validity of this model, but only indicates the considerable
58 amount of chemical similarity of the K-bentonite samples grouped or ungrouped using the proposed model.
Discriminant analysis generates a discriminant function or a set of functions for more than two groups based on observed linear combinations of the variables, or chemical elements in this case that provide the best discrimination between the groups.
The functions are generated from a sample of cases, k-bentonite beds, for which group membership is known. The functions can then be applied to new cases with measurements for the variables but unknown group membership.
The discriminant functions can be considered as coordinate axes in geometric space. The groups are represented in that space, which are defined by the functions, as clusters of points far apart from each other. The distance between groups is controlled by the functions. Figure 4.18 from Davis (1973) demonstrates a graphical representation of the distribution of two groups, A and B, using two variables X1 and X2. In this example, two discriminant functions are calculated to provide linear scales along which the separation of groups is maximized. Along the linear scales, a centroid is computed and plotted for group and each variable. The centroids for each variable are compared with the group centroids, and the variables are assigned to the groups whose centroids are closest to their own. The cases, which are assigned to the groups different from the ones to which they were pre-assigned indicate errors in the original model or overlap between groups.
59 Figure 4.18. Graphical representation of the linear discriminant functions. Two groups, A and B overlap with respect to the distribution of variables X1 and X2 in two- dimensional space. The discriminant function, however, provides complete separation of groups A and B in n-dimensional multivariate space where maximum separation of groups occurs (after Davis, 1973).
60 Figure 4.18 also illustrates the relationship between the number of discriminant functions that can be derived and the number of variables being used in the analysis. The number of functions computed is one less than the number of variables or groups whichever is smaller. In this case, the number of groups (K-bentonite beds) is always less than the number of variables (elements). Since the discriminant functions are derived in the order of their relative strength to discriminate between groups, only the number of functions which account for the desired amount of variance need to be computed.
The relative contribution of each variable to a function is represented by the discriminant function coefficients, which are multiplied and summed to arrive at a discriminant score for each sample. The discriminant scores for all samples from the same group are averaged to a mean score called the group centroid, which is the average value of the samples of a particular group.
In this study, step-wise discriminant analysis was used to establish the order or the ability of all variables to distinguish between groups. In this method, variables are chosen to enter the analysis in the order of their ability to improve the discrimination between groups in combination with other variables. The variable with the greatest discriminating power is selected and compared with all other variables to find the next best variables, which improves the discrimination between groups. Subsequent variables are selected in the same manner until further addition of variables no longer improves the discrimination to a significant degree. This procedure reveals the relative strengths of each variable
(element) to achieve the grouping at an acceptable level of confidence.
Detailed theoretical information and discussion of the mathematical basis of this analysis can be obtained from Davis (1973).
61 4.4.1. Results of Discriminant Function Analysis
The objective of this statistical analysis is to identify the most effective group of elements for discriminating the Osmundsberg K-bentonite bed in Northwestern Europe and to provide a basis for comprehensive high resolution chemostratigraphic correlation using these set of elements. The purpose is to test the model provided by Bergström et al.
(1998) for its validity. It is also assumed that between-bed chemical variation is greater than the within-bed variation (Hildreth & Mahood, 1985; Cronin et al. 1996).
For this purpose, thirty-three samples were analyzed for 26 trace elements each by
Instrumental Neutron Activation Analysis (INAA) and the data statistically treated using the Statistical Package for Social Sciences (SPSS). These samples represent 16
Osmundsberg K-bentonite beds and 17 accompanying K-bentonite beds of similar age from Baltoscandia, Northern Europe and British Isles whose stratigraphic relations were established by Bergström et al. (1998) (Fig 1.1).
A summary of the K-bentonite samples involved and their whole-rock trace element data used in the discriminant function analysis is given in Tables 37 through 42
(See Appendix C).
For this analysis, the DISCRIMINANT subprogram of SPSS was used which calculates and prints discriminant coefficients, and it enters the variables in the order of their discriminating power and classifies all samples.
The Osmundsberg K-bentonite beds were initially grouped on the basis their occurrence in the turriculatus Graptolite zone and discriminant analysis was used to test the hypothesis that such groups also have chemical characteristics. There are six distinct models tested for this study, each model is composed of three K-bentonite groups whose
62 stratigraphic relationships are already established. Two distinct Silurian K-bentonite horizons that are known to be slightly different in age from the Osmundsberg and three
K-bentonite groups of the same age with the Osmundsberg were selected to provide a basis for chemical comparison. These included a series of four Ludlow samples from
Podolia, three Wenlock samples from Estonia, and 46 Llandovery samples from
Baltoscandia, British Isles and Nova Scotia.
These samples are treated by discriminant analysis to provide an effective way of differentiating the Osmundsberg K-bentonite beds and their equivalents in Baltoscandia and the British Isles by comparing them with the combinations of these five K-bentonite groups on the basis of their chemical signatures. It allows us to compare the chemical similarities and differences between these K-bentonite groups as a function of age and geographic location. Samples were assigned to the groups according to their geographical locations, ages and known stratigraphic positions.
63 4.4.1.1. MODEL 1
For this model, 3 control groups of Silurian age were chosen from different localities to test the chemical similarities and differences between them. The first group is the Osmundsberg K-bentonite bed of Llandovery age from Baltoscandia and British Isles as defined by Bergström et al. (1998), the second group includes the K-bentonite samples from Arisaig, Nova Scotia of Llandovery age ( Bergström et al. 1997) and the third group is from Podolia, Ukraine of Ludlow age ( Huff et al. 2000). The following table lists the samples and the control groups used in the discriminant analysis.
Group # Locality Samples used in analysis
1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7
2 Nova Scotia, Canada 92B33-1, 92B33-2, 92B33-3, 92B33-4, 92B33-5, 92B34- 1, 92B35-1
3 Podolia, Ukraine c31 , c32, c6, m7
Table 4.2. Samples and their pre-assigned groups of the first model used in the discriminant analysis based on relationships in Bergström et al (1998).
The results of the step-wise discriminant analysis are shown in Appendix C. The best group of elements selected during analysis is listed in Table 1, Appendix C. These elements were selected by a step-wise method, in which the within-bed variance of all elements were compared and the element with the greatest discriminating power, Zr, was selected to begin the analysis. Subsequent elements were individually entered into the
64 analysis on the basis of their ability to increase the discrimination between the beds when added to the previously selected elements. If the addition of an element reduces the discriminating power of elements already selected, that element is returned to the group of unused elements for reconsideration. The Wilk`s Lambda associated with each step is the amount of within-bed variance, relative to the between-bed variance, that remain unaccounted for after each element is entered into the analysis. The smaller Wilk`s
Lambda, the less information remaining within the beds and the greater the discrimination between beds. Therefore, as the variability within each bed is reduced with the addition of a new element, Wilk`s Lambda becomes smaller and the variability between beds is greater. For this model, the most successful element is Zr, followed in order by Sc, V, Eu, Th, Hf and so forth. After Zr, Sc and V are entered into the analysis, a
Wilk`s Lambda of 0.51 means that only 5.1 % of the variance within K-bentonite beds remains unaccounted for. After Zr, Sc, V, Eu, Th and Hf are entered into the analysis, almost 99 % of the variance within beds compared to the variance between beds can be accounted for by these 6 elements (Table 2, Appendix C). It can be seen that relatively few elements are needed to obtain satisfactory discrimination between groups. The addition of other elements added little or no further information to the model and, therefore, was not used.
The next step in the analysis was to compute the discriminant functions for the discriminant model. Two discriminant functions were derived for three K-bentonite groups. The discriminant functions are defined by the discriminant scores that were calculated for each sample by multiplying elemental concentrations by their respective coefficients and then summed to arrive at a discriminant score for each function. The
65 effectiveness of each function is shown by eigenvalues in Table 3 (Appendix C), which measure the amount of variance within the elements that each function can account for.
Therefore, it is a measure of the ability of the function to discriminate among the beds.
The greater the amount of variance, the greater the separation between beds. For example, when expressed in cumulative percentage, the first function accounts for 82.1 % of the variance by itself, and the two functions together account for 100 % of the variance existing within the variables. The second function accounts for a relatively small amount of variance compared with the first discriminant functions, which has most power to discriminate among the K-bentonite beds.
Table 3 (Appendix C) also lists the canonical correlation for each function, which measures the effectiveness with which coefficients computed for each element to separate
K-bentonite beds. There is a very good correlation between the two discriminant functions and their set of variables.
The other way of evaluating the ability of the functions to discriminate between
K-bentonite beds is to look at the Wilk`s Lambda which is the inverse measure of variance remaining within the variables, after each function has been derived. Table 4
(Appendix C) lists the Wilk`s Lambda for each function and their corresponding Chi- square values. It can be seen that a relatively small amount of information remains in the variables after the two discriminant functions has been derived.
Table 5 (Appendix C) shows the unstandardized discriminant function coefficients for the two functions computed for this model. Discriminant scores were calculated for each sample by multiplying elemental concentrations by their respective coefficients. Ideally the samples from the same bed will have a fairly similar discriminant
66 scores. Scores for the samples whose membership is not known can be calculated using the coefficients in the table and classified by their similarity of scores to one of the three
K-bentonite bed groups. The average discriminant score for a group of samples is called the group centroid. These scores can be plotted on a territorial map (Fig 4.19) to illustrate the group separation the location of unknowns defined by the two derived discriminant functions. Each sample is plotted on the map on the basis of its discriminant score for each function. The Euclidian distance between group centroids defines the boundary lines between groups. All samples plot within areas of their respective group centroids. To determine the likelihood that any given unknown sample is chemically similar to one of the K-bentonite groups, the analyzed value for a particular element is multiplied by the unstandardized discriminant coefficients and the result is plotted on the territorial map.
The absence of an overlap between groups reflects a good separation among them using only 6 variables.
67 6
3 4
2
Label 2
2 0 Group Centroids n o
i 1 t c
n Podolia, u F
Ludlow t
n -2 a n
i Nova Scotia, m i
r Llandovery c s i
D -4 Osmundsberg -6 -4 -2 0 2 4 6 8 10
Discriminant Function 1
Figure 4.19. Territorial map constructed from the two discriminant functions calculated for 19 elements. Numbers 1 through 3 refers to K-bentonite groups shown in tables 37,38, 41 (Appendix C)
68 The ability of the functions to discriminate between beds can also be tested by the percentage of the samples that are correctly classified using these two discriminant functions to see how well the functions can predict the group membership when compared with the actual membership of the samples. All 29 samples used for the discriminant model were correctly identified and grouped appropriately (Table 6,
Appendix C) by using two discriminant functions.
4.4.1.2. MODEL 2
For the second model, 3 control groups of Silurian age were chosen keeping the first two groups the same as in model 1 and changing one of the groups of K-bentonite beds to see if there is any significant change in grouping and distribution of the samples.
The first two groups are the same as the first model and the third group is from Estonia of
Wenlock age (Huff pers. comm.). The following table lists the samples and their pre- assigned groups used in the discriminant analysis.
Group # Locality Samples used in analysis
1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7
2 Nova Scotia, Canada 92B33-1, 92B33-2, 92B33-3, 92B33-4, 92B33-5, 92B34- 1, 92B35-1
3 Estonia EST-125, EST-129, EST-130
Table 4.3. Samples and their pre-assigned groups of the second model used in the discriminant analysis.
69 The results of the step-wise discriminant analysis are shown in Appendix C. The best group of elements selected during analysis is listed in Table 7 (Appendix C). Zr has the most discriminating power followed in order by Eu, Sm, Sc, Th, V, and Hf. The set of elements used in this model is almost same with the previous model except the addition of Sm, which has been defined as a good discriminator. After Zr, Eu, Sm and Sc are entered into the analysis, a Wilk`s Lambda of 0.05 (Table 8, Appendix C) means that only 5.0 % of the variance within K-bentonite beds remains unaccounted for. After Zr,
Eu, Sm, Sc, Th, V and Hf are entered into the analysis, almost 99 % of the variance within beds compared to the variance between beds can be accounted for by these 7 elements.
There are two functions derived for this model. When expressed in cumulative percentage, the first function accounts for 75.8 % of the variance, and the two functions together account for 100 % of the variance existing within the variables (Table 9,
Appendix C). There is an excellent correlation between the two discriminant functions and their set of variables indicated by high correlation values, 0.966 for the first function and 0.905 for the second function computed. Wilks ` Lambda, which is the inverse measure of variance remaining in the variables, is almost the same with the previous model. (Table 10, Appendix C). Unstandardized discriminant function coefficients, which are used to construct the territorial map (Fig. 4.20), for each variable (element) for each of two functions are listed in Table 11 (Appendix C). There is no overlap between the areas of distribution of groups of K-bentonite beds as aimed. According to the step- wise statistics, 100 % of the samples are classified and grouped correctly (Table 12,
Appendix C). Each sample is assigned to their actual groups of beds.
70 8
6 3
4
2 Label 2 Group Centroids n 2 o
i 0 t c
n 1 Estonia, u F
Wenlock t n a -2 n
i Nova Scotia, m i
r Llandovery c s i
D -4 Osmundsberg -6 -4 -2 0 2 4 6 8
Discriminant Function 1
Figure 4.20. Territorial map constructed from the two discriminant functions calculated for the second model using 19 elements and 28 samples. Numbers 1 through 3 refers to K-bentonite groups shown in tables 37,38 and 42 (Appendix C).
71 4.4.1.3. MODEL 3
For the third model, three K-bentonite bed groups of Llandovery age samples were chosen from different localities. The first two groups of beds are the same as the first two models and the third group is made up of K-bentonite beds of the same general age as the Osmundsberg K-bentonite. The third group is chosen because of its chemical similarity and its potential to be correlated with the Osmundsberg. This group has 15 samples and each of them has an equal probability to be correlated with the
Osmundsberg. The following table lists the samples and pre-assigned groups used in the discriminant analysis.
Group # Locality Samples used in analysis
1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7
2 Nova Scotia, Canada 92B33-1, 92B33-2, 92B33-3, 92B33-4, 92B33-5, 92B34-1, 92B35-1
3 Baltoscandia DEN10, DEN7, DEN9, DL3, DL6B, NOR35, NOR36, NOR37, NOR38, SWE110, SWE111, SWE123, SWE124, SWE125, SWE126, SWE127, SWE128
Table 4.4. Samples and their pre-assigned groups of the third model used in the discriminant analysis.
The results of the step-wise discriminant analysis are shown in Appendix C. The best group of elements selected during analysis is listed in Table 13 (Appendix C). Zr has the most discriminating power followed by Th, Dy, V, Lu, Hf, Ta, Sm, Sc, and Yb in the order of discriminating power. However, Dy was removed at step nine to improve the
72 discrimination. The number of elements used in this model is greater than the number of elements used in the first two models, which results in a better discrimination of the K- bentonite groups.
After Zr, Th, Dy, V, Lu, Hf, and Ta are entered into the analysis, a Wilk`s
Lambda of 0.02 (Table 14, Appendix C) means that only 2.0 % of the variance within K- bentonite beds remains unaccounted for. After all the elements are entered into the analysis, the variance remains unaccounted for decreases down to 0.009 which indicates an excellent discrimination among the groups.
The first function derived for this model accounts for 82.5 % of the variance and the two functions together account for 100 % of the variance existing within the variables
(Table 15, Appendix C). There is a good correlation between the two discriminant functions and their set of variables indicated by high correlation values, 0.980 for the first function and 0.915 for the second discriminant function computed. Wilks ` Lambda values for the two functions are very small indicating an effective discrimination (Table
16, Appendix C).
Unstandardized discriminant function coefficients and the classification results are listed in tables 17 and 18 (Appendix C) respectively. All of the 38 samples correlated and classified correctly. The territorial map showing the separation of three groups are illustrated in Figure 4.21. The excellent separation of Llandovery samples indicates that each K-bentonite group has their unique chemical fingerprints.
73 6
4
3 2
2 Label
2 0 Group Centroids n o i t c
n Baltoscandia, u F
Llandovery t
n -2
a 1 n
i Nova Scotia, m i
r Llandovery c s i
D -4 Osmundsberg -10 0 10 20
Discriminant Function 1
Figure 4.21. Territorial map constructed from the two discriminant functions calculated for the third model using 19 elements and 38 samples. Numbers 1 through 3 refers to K- bentonite groups shown in tables 37, 38, and 40 (Appendix C).
74 4.4.1.4. MODEL 4
For the this model, three K-bentonite bed groups of the Llandovery age samples, the same samples used in the previous model, were chosen to test an unknown. This unknown sample from Denmark (DEN 8) is thought to be the correlative of the
Osmundsberg (Bergström et al. 1998) and its membership is actually known, however, the chemical profile of this sample is not consistent with the samples in the Osmundsberg group and it may belong to the other control group called Baltoscandia whose general chemical profile fits with it. Therefore, it is treated as an unknown to test its group membership using discriminant analysis. Appendix B lists the results of the step-wise discriminant analysis. The group of elements, that have the most discriminating power, is listed in Table 19 (Appendix C). Th was to be selected first followed by Zr, Hf, V, Ta,
Sc, and Ce in the order of discriminating power.
Group # Locality Samples used in analysis
1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7 2 Nova Scotia, Canada 92B33-1, 92B33-2, 92B33-3, 92B33-4, 92B33-5, 92B34-1, 92B35-1 3 Baltoscandia DEN10, DEN7, DEN9, DL3, DL6B, NOR35, NOR36, NOR37, NOR38, SWE110, SWE111, SWE123, SWE124, SWE125, SWE126, SWE127, SWE128
Unknown Denmark DEN 8
Table 4.5. Samples, their pre-assigned groups and unknowns of the fourth model used in the discriminant analysis.
75 6
4
3 2 DEN 8 Label
2 Group Centroids
2 0 Unknowns n o i t c Baltoscandia, n u F
Llandovery t
n -2 1 a n
i Nova Scotia, m i
r Llandovery c s i
D -4 Osmundsberg -6 -4 -2 0 2 4 6 8 10 12
Discriminant Function 1
Figure 4.22. Territorial map constructed from the two discriminant functions calculated for the fourth model using 19 elements and 38 samples. Numbers 1 through 3 refers to K- bentonite groups shown in table 37,38 and 40 (Appendix C).
76 The set of elements used in this model is similar with the set of elements used in previous models except the addition of Ce, which has relatively small discriminating power and was the last variables to enter the analysis. When Th, Zr, Hf, V, and Ta are entered into the analysis, a Wilk`s Lambda of 0.16 (Table 20, Appendix C) means that only 1.6 % of the variance within K-bentonite beds remains unaccounted for. After Sc and Eu are entered into the analysis, 99.2 % of the variance within beds compared to the variance between beds can be accounted for by these 7 elements.
Two discriminant functions, derived for this model, are listed in Table 21
(Appendix C). The first function accounts for 83.6 % of the variance, and the two functions together account for 100 % of the variance existing within the variables.
Canonical correlation between the two discriminant functions and their set of variables are indicated by high correlation values, 0.979 for the first function and 0.904 for the second function computed. Wilks` Lambda, measure of variance remaining in the variables, has a value of 0.008 indicating a good separation between the groups (Table
22, Appendix C). A territorial map (Fig. 4.22) is constructed using the set unstandardized discriminant function coefficients listed in Table 23 (Appendix C). The sample that was treated as unknown, plots within area of the samples from Baltoscandia with a high probability. The affinity of this sample to plot with samples from Baltoscandia raises a question about the membership of this sample, which has a peculiar chemical profile.
According to the step-wise statistics, 100 % of the samples are classified and grouped appropriately (Table 24, Appendix C). The unknown sample is classified as a member of the third group, which is composed of the accompanying beds of the
Osmundsberg from Baltoscandia.
77 4.4.1.5. MODEL 5
Three control groups of Silurian age, which were used in the third and fourth models were chosen to test the membership of two other unknowns, which have the potential to be a correlative of the Osmundsberg. The unknowns are the samples from
Scotland (DL 3 and DL 6B) and have a similar chemical fingerprints with the
Osmundsberg. The following table lists the samples, their pre-assigned groups and the unknowns used in the discriminant analysis.
Group # Locality Samples used in analysis
1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7
2 Nova Scotia, Canada 92B33-1, 92B33-2, 92B33-3, 92B33-4, 92B33-5, 92B34- 1, 92B35-1
3 Baltoscandia DEN10, DEN7, DEN9, DL3, DL6B, NOR35, NOR36, NOR37, NOR38, SWE110, SWE111, SWE123, SWE124, SWE125, SWE126, SWE127, SWE128
Unknown Scotland DL3, DL6B
Table 4.6. Samples, their pre-assigned groups and unknowns of the fifth model used in the discriminant analysis.
78 6
4
3 2 Label
2 DL 6B Group Centroids DL 6B
2 0 Unknowns n o
i DL 3 t c DL 6B Baltoscandia, n u F
Llandovery t
n -2 1 a n
i Nova Scotia, m i
r Llandovery c s i
D -4 Osmundsberg -6 -4 -2 0 2 4 6 8 10 12
Discriminant Function 1
Figure 4.23. Territorial map constructed from the two discriminant functions calculated for the fifth model using 19 elements and 39 samples. Numbers 1 through 3 refers to K- bentonite groups shown in tables 37,38 and 40 (Appendix C).
79 The results of the step-wise discriminant analysis are shown in Appendix C. The elements used in the analysis are listed in Table 25 (Appendix C). Th has the most discriminating power followed by Zr, Hf, V, Ta, Sc, and Ce. The set of elements used in this model is exactly the same with the previous model, All the step-wise statistics including the set variables used in the analysis, Wilks` Lambda, eigenvalues, discriminant function coefficients are the same with the results of the previous model, since the same set of samples and elements used in this analysis (Tables 26-29, Appendix C).
According to the step-wise statistics, 100 % of the samples are classified and grouped correctly (Table 30, Appendix C). One of the unknowns, DL 3 is classified as a member of the Osmundsberg group, and DL 6B falls in the Baltoscandian group (Fig.
4.23).
4.4.1.6. MODEL 6
This model is the last discriminant analysis performed using the same group of samples, which were used in the preceding models, to test the membership of the 5 samples whose memberships are actually known. All of these samples, treated as unknowns, were thought be equivalent of the Osmundsberg, (Fig. 1.1) however trace element chemistry of these samples do not match with the general chemistry profile of the Osmundsberg due to their high Cs, Eu, Hf, Sc, Tb, V and Zr content. The following is the list of samples and unknowns used in this analysis.
80 Group # Locality Samples used in analysis
1 Osmundsberg, Sweden BT6, DEN8, EST124, NOR31, NOR31B, SWE129, SWE130, SWE131, SWE132, SWE34A, SWE93, SWE94, SWE96A, SWE96B, TS2, WAL7
2 British Isles WDH-26, WDH-28, WDH-29, WDH-30, WDH-31, WDH-47
3 Baltoscandia DEN10, DEN7, DEN9, DL3, DL6B, NOR35, NOR36, NOR37, NOR38, SWE110, SWE111, SWE123, SWE124, SWE125, SWE126, SWE127, SWE128
Unknown Baltoscandia, British Isles SWE132, BT6, TS2, WAL7
Table 4.7. Samples, their pre-assigned groups and unknowns for the sixth model used in the discriminant analysis.
Appendix C lists the results of the step-wise discriminant analysis. There are 8 elements found to be good discriminators. Zr was to be selected first followed by Hf, V,
Ta, Tb, Th, Yb and U in the order of discriminating power. (Table 31, Appendix C)
When Zr, Hf, V, and Ta are entered into the analysis, a Wilk`s Lambda of 0.22
(Table 32, Appendix C) means that 2.2 % of the variance within K-bentonite beds remains unaccounted for. After Tb, Th, Yb and U are entered into the analysis, 99.6 % of the variance within beds compared to the variance between beds can be accounted for by these eight elements.
The first function accounts for 81.3 % of the variance, and the two functions together account for 100 % of the variance existing within the variables (Table 33,
Appendix C). Canonical correlation values between the two discriminant functions and their set of variables are 0.979 for the first function and 0.904 for the second discriminant function computed. After each element is entered into the analysis, variance remaining in
81 the variables, Wilks ` Lambda, reduces to 0.004, which indicates a good discrimination between groups with a high confidence.
When the discriminant functions are projected into the two-dimensional plane,
(Fig. 4.24) it can be seen that, three of the five unknowns do not plot within areas of their respective group centroids. According to this profile, these samples, which are thought to be correlative of the Osmundsberg, are not a part of the Osmundsberg group.
Table 36 (Appendix C) lists the classification results of this analysis. 100 % of the samples are grouped correctly. Two of the unknowns are grouped with the
Osmundsberg while three of them are classified as a part of the Baltoscandian group, which is the group made up of the accompanying K-bentonite beds of the Osmundsberg.
82 6
4 DEN 8 2
2 Label
Group Centroids TS 2 WAL7
2 0 SWE 132 Unknowns n
o 3 i t BT 6 c British Isles, n u F
Llandovery t
n -2 a n
i Baltoscandia, 1 m i
r Llandovery c s i
D -4 Osmundsberg -10 0 10 20
Discriminant Function 1
Figure 4.24. Territorial map constructed from the two discriminant functions calculated for the sixth model using 19 elements and 36 samples. Numbers 1 through 3 refers to K- bentonite groups shown in tables 37, 39 and 40 (Appendix C).
83 4.4.2. Discussion of The Results of the Discriminant Function Analysis
The use of discriminant analysis to identify or classify K-bentonite beds is a relatively new and effective technique and it has been successfully used for example by
Huff (1983, 1991), Bergström (1995), and Fortey et al. (1996). In discriminant analysis, elements are grouped on the basis of their combined, rather than individual, ability to separate K-bentonite groups and the result can be more effective separation than otherwise with single variables. Discriminant analysis is also used for ranking the elements in the order of their discriminating power and classifying and grouping the unknown samples using this set of elements. Discriminant analysis can operate with many variables at a time, which is an advantage over traditional bivariate discrimination diagrams. In addition, grouping and separation of K-bentonite beds can be maximized using the discriminant functions. The most important outcome of the discriminant analysis is the confidence with which an unknown sample can be classified and quantified by the discriminant functions.
Selection of discriminating variables was restricted to elements considered relatively immobile under conditions of diagenesis and low-grade metamorphism.
Although, a total of 19 elements were entered into the analysis, the number of elements ranging from 7 to 11 was selected on the basis of their discriminating power to form the discriminant models described earlier.
The variance between the samples, that remains unaccounted for, changes between 0.5% and 1.2 %, which indicates a very good separation between K-bentonite groups of these models. Different elements are important in each model. The approximate
84 order of the highest ranking elements are Zr, Th, Hf, Sm, V, Sc, and, Eu accompanied by
Ce, Ta, Dy, U, Tb, Lu, Yb. This study has shown that these elements are the most useful for regional correlation of K-bentonites in Baltoscandia and the British Isles and it should be noted that they are approximately the same as those that have been found to be the best discriminators of K-bentonites in recent studies (Huff 1983, Kolata et al 1983).
The results of the discriminant analysis of these models generally support the correlation of the Osmundsberg K-bentonite bed in Baltoscandia as shown by Bergström et al. (1998). However, three K-bentonite beds from Sweden (SWE132), Denmark (DEN
8), and Scotland (TS 2) that were correlated with the Osmundsberg do not plot within the territory defined for the Osmundsberg, which raises a question mark about their proposed correlation with the Osmundsberg. Except this, the models were able to separate 100 % of the group members as identified by their biostratigraphic position. Once the criteria for membership was established by the discriminant functions, test of the two suspected
Osmundsberg equivalents from Scotland was carried out and the results are illustrated by their position on the discrimination plot in Figure 4.6. One of these samples, DL 3, was correlated with the Osmundsberg on the basis of its chemical composition with a high degree of confidence.
In present study, all the samples were classified correctly using the discriminant functions into their respective groups. The excellent separation of the K-bentonite groups indicate that chemical variance between beds is much greater than the variance within bed variance. If the achieved separation of groups were insufficient to define individual territories, control groups would overlap one another. Thus, it can be concluded that each
K-bentonite group used in this study has its unique chemical signature.
85 Identification and classification of K-bentonites using discriminant analysis is a useful stratigraphic tool which can be most helpful where conventional methods are inadequate such as the absence of a good biostratigraphic control as in this case.
However, the maximum advantage can be gained when the statistical analysis is coupled with other stratigraphic methods.
86 Chapter 5
DISCUSSION AND CONCLUSIONS
In order to provide a high-resolution chemostratigraphic correlation of the
Osmundsberg K-bentonite, and to test the stratigraphic usefulness of fingerprinting in regional correlations of Silurian K-bentonites in Baltoscandia, chemical data were plotted on a series of binary diagrams using several of the most effective discriminating elements and elemental ratios and discriminant function analysis was performed using data for twenty trace elements in thirty-three samples of the Osmundsberg K-bentonite.
The information from the binary discrimination diagrams show that the members of the Osmundsberg K-bentonite group are very well clustered together with one exception. There are five samples, SWE 132 (central Sweden), TS 2 (southern Scotland) ,
DEN 8 (Denmark), BT 6 (Northern Ireland) and WAL 7 (Wales), which have slightly different chemistry and consistently plot away from the territories defined by the
Osmundsberg samples.
Osmundsberg K-bentonite beds were biostratigraphically grouped and discriminant analysis was used to test the hypothesis that such groups also have unique chemical characteristics. There are six distinct models tested for this study, each model is composed of three K-bentonite groups whose stratigraphic relationships are already established. Two distinct Silurian K-bentonite horizons that are known to be slightly different in age from the Osmundsberg and three K-bentonite groups of the same age with the Osmundsberg were selected to provide a basis for chemical comparison. These samples were treated by discriminant analysis to provide an effective way of
87 differentiating the Osmundsberg K-bentonite beds and their equivalents in Baltoscandia and British Isles by comparing them with the combinations of these five K-bentonite groups. The results generally agree with the K-bentonite correlation model proposed by
Bergström et al. (1998) with some exceptions. Three K-bentonite beds from Sweden
(SWE 132), Denmark (DEN 8), and southern Scotland (TS 2) that were initially correlated by Bergström et al. (1998) with the Osmundsberg do not plot within the territory defined for the Osmundsberg, which is interpreted to mean they are not
Osmundsberg. There are two samples, WAL 7 (Wales), BT 6 (northern Ireland) whose memberships are ambiguous. These samples are correlated with the Osmundsberg with a question mark. Except these, the models were able to separate 100 % of the group members as identified by their biostratigraphic position. Moreover, a test of the two suspected Osmundsberg equivalents from Scotland was carried out. One of these samples, DL 3 (southern Scotland), was correlated with the Osmundsberg on the basis of its chemical composition with a high degree of confidence. This study has shown Zr, Th,
Hf, Sm, V, Sc, and, Eu accompanied by Ce, Ta, Dy, U, Tb, Lu, Yb, in approximate order of the relative strength to discriminate among the K-bentonite beds, are the most useful for regional correlation of K-bentonites in Baltoscandia and the British Isles.
The use of clay and non-clay mineralogy for stratigraphic correlation was not possible due to the absence of systematic variation in the clay mineralogy of different samples of the Osmundsberg K-bentonite and high similarity of heavy minerals found in the Osmundsberg K-bentonite samples. The Osmundsberg and the associated beds contain abundant mixed layer illite-smectite, accompanied by discrete kaolinite and illite.
88 Although all Osmundsberg samples are characterized by mixed layer illite-smectite phases associated illite percentages changes from 10 % to 90 % through short range (R1) to long-range interstratifications (R3). This might be explained by the different diagenetic conditions under which the beds are formed. Therefore, clay mineralogy was not a particularly diagnostic feature of the Osmundsberg K-bentonites. However, clay mineralogy provided a confirmation that these beds were actually K-bentonites derived from a volcanic source.
Whole rock trace element data of 15 Osmundsberg K-bentonite samples and 26 samples of accompanying beds from 12 different localities were analyzed for major and trace elements and thirty-two fresh biotite grains from the type section of the
Osmundsberg K-bentonite bed were analyzed for major oxides by electron microprobe.
Results were plotted on several widely referenced discrimination plots. A majority of the
Osmundsberg samples plot within the calc-alkaline magma source region in a collision margin setting related with subduction, consistent with the previous reports. The source volcano(es) that was responsible for the Osmundsberg K-bentonite is thought to have been located along the western margin of Iapetus Ocean (Bergström et al. 1997).
89 Figure 5.1. Diagram showing the revised correlation of the Osmundsberg K-bentonite across Baltoscandia from Northern Ireland to Western Estonia. For location of sections, see inset map. 90 When all the results are combined, a new model is proposed for the correlation of
the Osmundsberg K-bentonite across Baltoscandia from Northern Ireland to Estonia.
(Fig. 5.1). Three samples (SWE 132, DEN 8, TS 2) which were thought to be the
correlatives of the Osmundsberg K-bentonite has been taken out from the proposed
model and 1 sample (DL 3) has been added based on chemical fingerprints of these
samples. In addition to these, two samples (BT 6, WAL 7) are correlated with the
Osmundsberg K-bentonite with a question mark due to ambiguous chemistry of these
samples. The major results of the present investigation is summarized as follows:
i . The size, regional distribution pattern and the geochemistry of the Osmundsberg
K-bentonite indicates presence of a large volume, subduction related felsic ash
explosively erupted in Early Silurian (Llandovery) times
ii . Optimal correlation of the Osmundsberg K-bentonite beds from Baltoscandia and
the British Isles is achieved by the combined use of biostratigraphic,
mineralogical and chemical information based on statistical data.
iii . Discriminant function analysis of whole rock trace element data of the
Osmundsberg K-bentonite samples and other Silurian K-bentonite groups show
systematic differences between Llandovery, Wenlock and Ludlow K-bentonites in
Baltoscandia and Europe as well as differences between geographical locations of
these K-bentonite beds.
91 iv . The excellent separation of the K-bentonite groups indicate that chemical variance
between beds is much greater than the variance within bed variance. Thus, it can
be concluded that each K-bentonite group used in this study has its unique
chemical signature.
v . Identification and classification of K-bentonites using discriminant analysis is a
useful stratigraphic tool which can be most helpful where conventional methods
are inadequate such as the absence of a good biostratigraphic control as in this
case. However, sample population plays an important role in discriminating the
groups of sample from each other.
Further studies should concentrate on the enhancement of the sample numbers from each locality and from different localities across Baltoscandia and the British Isles to eliminate the limitations of the proposed chemostratigraphic correlation of the
Osmundsberg K-bentonite.
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99 APPENDIX A
ÿ Microprobe data for the Osmundsberg K-bentonite samples
ÿ Trace element data for the Osmundsberg K-bentonite samples Sample ID SiO2 Al2O3 MgO Na2O NiO FeO* MnO K2O CaO TiO2 V2O3 Cr2O3 H2O Total 129-1 37.18 14.48 13.12 0.15 0.03 18.09 0.11 9.18 0.00 3.80 0.23 0.03 4.00 100.40 129-2 35.23 15.65 12.73 0.08 0.05 18.63 0.14 7.62 0.04 3.44 0.18 0.00 3.90 97.71 129-3 36.87 14.88 13.03 0.11 0.05 18.00 0.26 8.88 0.06 3.81 0.23 0.12 4.00 100.31 129-4 37.07 14.71 13.14 0.14 0.08 18.14 0.34 8.93 0.06 3.80 0.20 0.03 4.01 100.66 129-5 36.49 15.88 12.36 0.26 0.00 17.21 0.25 8.20 0.07 3.54 0.15 0.01 3.96 98.37 129-6 36.94 15.57 13.42 0.15 0.04 17.87 0.08 8.56 0.00 3.35 0.17 0.03 4.02 100.18 129-7 37.36 15.19 13.18 0.08 0.00 17.63 0.20 8.89 0.08 3.36 0.15 0.03 4.01 100.17 129-8 37.63 15.67 11.34 0.12 0.01 17.00 0.21 9.04 0.01 3.46 0.17 0.03 3.97 98.65 129-9 36.41 17.68 13.10 0.04 0.00 17.02 0.19 8.11 0.05 3.53 0.22 0.06 4.06 100.46 129-10 36.42 15.71 13.17 0.14 0.03 17.63 0.29 8.87 0.05 3.85 0.18 0.05 4.01 100.40 129-11 36.00 13.87 12.78 0.16 0.01 18.62 0.21 9.17 0.01 3.36 0.15 0.01 3.89 98.22 129-12 36.91 16.46 12.47 0.26 0.04 17.76 0.28 8.25 0.05 3.79 0.18 0.04 4.04 100.50 129-13 37.18 14.46 13.12 0.11 0.02 17.90 0.17 9.44 0.00 3.76 0.22 0.04 4.00 100.39 129-14 36.47 15.76 12.41 0.07 0.06 18.56 0.10 8.35 0.01 3.34 0.16 0.02 3.97 99.28 129-15 36.96 14.73 13.25 0.13 0.08 18.07 0.14 9.06 0.00 3.63 0.16 0.00 4.00 100.18 129-16 35.76 14.02 12.61 0.08 0.01 18.41 0.24 9.24 0.05 3.74 0.20 0.09 3.89 98.34 129-17 36.96 14.33 13.24 0.10 0.03 18.04 0.19 9.24 0.00 3.83 0.18 0.06 3.99 100.18 129-18 37.17 16.16 12.41 0.06 0.07 17.33 0.10 8.42 0.06 3.32 0.19 0.06 4.00 99.34 129-19 36.53 15.27 13.48 0.10 0.00 17.25 0.16 8.62 0.02 3.64 0.15 0.05 3.98 99.24 129-20 36.75 14.67 13.44 0.11 0.04 18.10 0.36 8.96 0.06 3.60 0.15 0.05 3.99 100.29 130-1 35.61 15.88 12.67 0.26 0.04 17.48 0.31 8.46 0.04 3.86 0.21 0.01 3.95 98.76 130-2 35.53 15.33 13.92 0.24 0.03 19.15 0.31 7.31 0.02 3.44 0.19 0.04 3.97 99.49 131-1 37.02 14.44 13.40 0.30 0.04 17.57 0.29 8.76 0.06 3.68 0.16 0.05 3.99 99.75 131-2 36.85 14.31 13.47 0.26 0.05 17.52 0.27 8.64 0.02 3.86 0.21 0.03 3.98 99.45 131-3 37.43 14.81 13.52 0.24 0.04 16.93 0.23 8.64 0.06 3.87 0.17 0.03 4.02 99.98 131-4 36.58 15.03 13.23 0.32 0.02 17.79 0.36 8.57 0.08 3.74 0.18 0.03 3.99 99.92 131-5 37.23 14.99 13.31 0.24 0.04 17.67 0.31 8.49 0.11 3.68 0.15 0.00 4.02 100.23 131-6 36.40 14.52 13.59 0.25 0.04 17.76 0.29 8.40 0.08 3.81 0.18 0.02 3.97 99.31 131-7 35.66 16.70 12.52 0.36 0.00 16.90 0.32 8.38 0.08 3.51 0.17 0.07 3.96 98.62 131-8 37.32 16.03 12.27 0.24 0.01 16.90 0.31 8.25 0.04 3.61 0.18 0.04 4.00 99.19 131-9 36.10 16.41 13.32 0.23 0.00 17.55 0.28 8.07 0.10 3.76 0.20 0.03 4.02 100.07 131-10 36.68 15.61 13.91 0.24 0.07 18.05 0.28 7.86 0.07 3.78 0.19 0.04 4.04 100.82
Table 1. Chemical composition of biotites of the Osmundsberg K-bentonite from its type section by electron microprobe analyses. Samples 129-1 through 129-20 are from bottom part of the bed, samples130-1, 130-2 are from the middle part and samples 131-1 through 131-10 are from the top of the Osmundsberg K-bentonite bed. FeO*= total Fe as FeO. Sample ID Al As Ba Ca Ce Co Cr Cs Dy Eu Fe Hf K La Lu Mn BT6 147157 1.5 1677.6 -1532 64.54 15.01 5.11 5.98 5.7 1.53 27037 10.78 49107 29.5 0.443 1009.0 BT7 145373 3.7 943.1 -1000 290.06 4.38 5.16 8.80 19.8 4.49 12170 23.39 62612 153.1 1.174 48.9 DEN10 105421 59.3 468.9 3228 75.16 67.89 62.48 7.76 7.2 1.56 73662 10.37 32729 34.4 0.891 1486.5 DEN7 124155 31.5 445.4 2788 20.27 3.15 8.50 8.53 27.6 2.17 74126 28.68 44694 6.5 2.356 123.7 DEN8 101400 133.4 420.8 5216 46.97 49.33 28.05 7.07 18.8 2.98 111083 13.17 32591 18.0 1.701 392.7 DEN9 118035 17.7 435.6 8332 48.96 31.26 33.16 16.47 13.8 4.04 64476 7.69 34286 17.2 0.622 426.6 DL3 125501 1.7 1085.1 10550 124.18 21.97 4.60 22.16 5.0 2.32 24191 11.73 45161 57.5 0.540 1815.1 DL4 135254 5.0 1232.7 5890 159.72 22.93 7.99 19.93 7.7 2.66 19070 13.13 46913 76.4 0.556 1456.3 DL6B 122974 2.1 1069.9 8871 119.64 32.75 24.52 17.71 7.3 2.70 25636 10.08 37736 55.0 0.583 1927.2 EST124 93370 2.5 514.0 30907 21.39 11.96 9.51 1.92 0.8 0.40 14642 5.63 88934 8.9 0.083 328.4 NOR31 108001 1.0 355.4 46741 88.21 10.71 5.99 11.75 3.5 1.54 30566 6.27 45548 36.1 0.193 1664.8 NOR31B 115589 0.9 377.9 14329 50.04 15.32 5.11 10.16 2.0 0.81 37556 8.35 46515 19.3 0.152 328.3 NOR32 79481 23.1 406.8 139474 90.72 25.11 55.17 6.52 5.2 1.85 26836 5.36 29317 37.7 0.359 667.7 NOR33 105634 4.8 645.7 53199 128.05 26.72 35.46 11.53 11.5 3.32 42124 10.69 41189 39.4 0.991 2439.8 NOR34 101088 4.3 369.5 67932 30.56 23.98 17.38 13.51 5.4 1.39 43457 5.26 39046 11.4 0.286 2670.6 NOR35 116446 9.6 682.5 14298 145.24 15.74 61.38 12.72 10.6 3.04 30110 15.68 48701 44.5 0.893 1133.6 NOR36 109733 19.8 552.5 83102 95.50 16.24 32.60 13.42 8.37 1.96 25685 7.86 41009 37.5 0.477 4698.47 NOR37 127881 8.3 684.6 4713 318.13 16.13 35.86 14.45 16.5 4.40 33730 23.78 53341 91.4 1.175 709.8 NOR38 132646 18.8 631.4 6339 322.54 15.18 31.24 15.45 18.1 4.34 31892 24.86 56476 90.9 1.212 1040.2 SWE110 80403 12.2 162.2 147559 46.57 12.17 12.44 80.48 6.6 1.10 42313 14.90 22480 21.0 0.685 3201.8
Table 2. Trace element abundances for the Osmundsberg K-bentonite accompanying beds. All values are in ppm. Shaded areas represent the Osmundsberg samples, others are accompanying beds in alphabetical order. Sample ID Na Nd Ni Rb Sb Sc Sm Sr Ta Tb Th Ti U V Yb Zn Zr BT6 2852 47.6 27.3 165.27 0.65 11.80 6.20 -132.4 1.13 0.98 21.91 8683 1.89 60.1 2.87 26.4 217.3 BT7 3303 160.6 -25.6 200.55 0.72 11.95 23.35 -132.7 2.62 3.08 32.58 5054 11.05 32.0 8.31 14.5 604.6 DEN10 2672 47.0 73.9 138.67 7.11 15.94 7.33 366.0 1.73 1.15 20.18 5328 12.61 150.4 5.69 384.4 245.1 DEN7 1295 32.7 -32.7 141.55 0.85 16.06 6.66 76.3 3.25 3.42 39.18 5866 9.79 42.4 16.80 34.5 603.4 DEN8 1785 43.2 196.5 104.66 9.73 15.21 12.88 689.5 1.69 2.82 27.64 8744 15.07 111.8 11.09 699.8 349.6 DEN9 2228 52.7 79.4 149.97 2.65 15.22 17.35 244.4 1.66 2.62 24.49 5584 3.14 97.3 4.30 112.5 144.1 DL3 6657 65.4 -26.1 157.01 0.77 8.81 10.73 -136.1 1.42 1.66 20.53 5398 3.61 45.3 3.13 54.3 296.3 DL4 9283 88.4 20.2 149.93 0.98 10.88 12.90 85.4 1.61 1.51 23.56 8047 8.09 61.3 3.97 57.7 348.3 DL6B 9007 75.6 35.1 129.92 1.05 12.99 11.97 -144.8 1.17 1.38 15.39 6220 7.40 120.2 4.02 53.6 268.9 EST124 1878 -8.2 -21.1 82.34 0.44 5.47 1.79 71.3 0.87 0.15 25.12 2193 1.40 36.4 0.67 18.7 114.5 NOR31 4428 51.4 18.3 174.10 3.18 6.25 8.03 100.1 0.98 0.74 29.72 2177 0.49 33.4 1.27 37.5 91.3 NOR31B 10987 31.1 -25.4 167.33 2.94 7.88 3.94 166.5 1.13 0.37 26.53 3674 0.51 55.5 0.96 58.1 165.6 NOR32 4141 54.5 59.8 110.64 1.16 11.53 8.94 -147.1 0.78 0.97 11.18 5055 3.26 107.3 2.56 79.4 152.0 NOR33 2505 89.7 66.6 157.34 5.08 12.10 18.31 -156.4 2.16 2.84 41.84 2896 4.84 73.4 6.50 62.1 225.6 NOR34 2018 29.5 -27.2 152.30 4.23 7.66 6.10 119.2 1.49 0.99 25.64 2034 1.93 58.6 1.88 53.7 122.4 NOR35 4051 87.0 45.6 202.65 4.91 14.39 16.09 58.3 1.98 2.35 21.82 6845 6.70 100.1 5.82 59.8 361.3 NOR36 1957 68.5 48.5 170.59 4.48 10.57 13.05 145.4 2.28 1.93 24.69 1697 4.18 86.0 3.27 50.8 189.5 NOR37 2291 180.7 45.6 192.87 4.39 14.14 26.72 -144.8 3.24 2.80 41.49 6154 12.37 78.2 7.86 68.1 570.9 NOR38 2074 170.1 43.9 200.79 5.56 14.09 26.10 133.7 3.25 2.87 43.09 5818 14.20 80.2 7.98 39.7 585.0 SWE110 623 18.2 52.9 101.90 2.00 7.24 4.78 384.5 3.19 1.24 9.11 3289 3.79 25.0 5.13 37.1 288.7
Table 2 (cont.) Trace element abundances for the Osmundsberg K-bentonite accompanying beds. Shaded areas represent the Osmundsberg samples, others are accompanying beds in alphabetical order. Sample ID Al As Ba Ca Ce Co Cr Cs Dy Eu Fe Hf K La Lu Mn SWE111 96984 29.7 283.2 24260 43.57 10.26 31.24 38.74 5.7 1.28 34843 7.93 27740 18.4 0.503 772.1 SWE112 104832 5.56 654.41 52834 175.69 8.70 52.34 15.60 15.77 6.28 22765 7.79 37523 65.41 0.98 722.41 SWE123 141957 43.02 231.72 38719 19.88 2.18 7.64 6.13 8.32 0.78 22607 14.63 14259 4.61 1.18 29.59 SWE124 96132 152.30 286.15 62889 34.03 10.02 36.04 7.23 5.87 0.77 67688 8.41 14136 13.20 0.98 920.38 SWE125 123775 1.98 289.23 10422 46.86 10.00 71.34 8.51 5.93 0.99 20767 8.94 27414 23.65 0.39 158.56 SWE126 87633 52.28 429.41 35094 214.94 29.89 94.07 7.22 8.75 3.05 62265 6.24 27933 141.35 0.55 805.21 SWE127 119325 30.34 341.36 27375 32.15 5.43 32.33 7.17 4.81 0.74 30861 7.92 32117 14.74 0.55 72.91 SWE128 87231 19.41 466.16 25387 43.85 8.51 80.93 8.23 3.49 0.65 52293 5.34 26618 22.92 0.40 171.32 SWE129 108589 2.86 653.01 29691 50.83 7.20 3.45 4.02 2.32 0.73 19408 8.99 12462 23.92 0.30 385.94 SWE130 97710 0.48 474.08 27461 54.49 4.92 -2.87 3.75 1.66 0.64 15005 6.26 10475 22.98 0.31 229.79 SWE131 95707 0.39 481.63 19509 59.94 5.27 -2.93 3.19 1.33 0.63 16060 5.92 11093 26.92 0.20 205.11 SWE132 92564 9.24 460.56 76804 77.16 12.09 33.66 18.54 5.42 1.44 25571 5.63 25292 39.21 0.34 1494.19 SWE34A 143146 4.37 557.99 7530 31.55 7.28 5.05 26.59 2.77 0.66 17953 8.54 40111 12.47 0.30 179.96 SWE93 116553 0.92 865.33 2995 92.16 7.75 -3.74 7.93 1.67 0.70 16422 8.09 52959 43.76 0.02 116.24 SWE94 117942 4.53 503.18 3594 95.85 6.81 2.91 9.60 2.16 0.93 18587 8.69 54653 47.83 0.17 138.43 SWE96A 110392 1.34 486.00 8716 66.63 11.06 -3.12 9.68 2.29 0.90 20408 6.52 48660 30.73 0.38 145.84 SWE96B 110859 0.72 680.86 -2095 63.92 12.54 -3.18 8.81 2.80 0.68 14376 5.63 46533 29.32 0.27 84.80 SWE97 108364 2.10 515.92 5685 159.57 6.81 3.50 13.92 7.81 1.81 20588 8.69 48772 79.24 0.60 185.20 TS2 139283 3.60 1154.56 3272 179.98 24.12 18.85 6.57 8.68 3.57 25531 10.30 52251 75.31 0.52 1211.30 WAL7 157775 0.71 539.96 -2547 84.11 5.38 -3.53 7.42 9.76 1.74 22696 13.26 53642 34.50 0.70 89.20
Table 2 (cont.) Trace element abundances for the Osmundsberg K-bentonite accompanying beds. Shaded areas represent the Osmundsberg samples, others are accompanying beds in alphabetical order. Sample ID Na Nd Ni Rb Sb Sc Sm Sr Ta Tb Th Ti U V Yb Zn Zr SWE111 1121 40.0 89.2 109.41 9.02 9.32 5.64 138.6 0.94 1.40 12.79 5702 10.92 336.1 3.01 124.6 215.3 SWE112 2334 128.18 -38.51 104.90 0.95 11.52 27.21 357.0 0.94 3.19 16.78 6386 8.51 134.90 5.86 44.96 215.1 SWE123 339 -23.28 -32.03 25.25 81.08 8.53 2.89 16.5 1.50 1.13 16.32 7770 29.91 406.19 5.62 64.78 335.8 SWE124 292 -26.00 66.59 55.00 39.18 9.36 3.03 39.4 1.29 0.65 12.95 5048 18.98 223.32 4.92 20.77 190.4 SWE125 683 -32.99 52.70 93.06 1.24 13.02 4.71 102.4 1.28 0.90 21.51 5158 1.97 167.71 3.78 57.88 156.8 SWE126 750 69.15 58.54 96.73 13.76 18.71 13.69 649.7 1.08 1.70 19.85 8744 18.44 238.07 3.31 2120.18 173.8 SWE127 625 -23.44 -35.37 73.47 6.67 9.40 2.94 168.6 0.95 0.55 10.12 8006 6.76 198.44 3.21 29.12 129.5 SWE128 745 -24.08 -39.57 95.47 3.85 12.43 3.21 28.6 0.90 0.56 8.81 5042 3.66 156.58 2.43 50.63 102.2 SWE129 1305 13.76 -31.54 47.64 -0.28 7.72 3.64 -213.9 1.06 0.33 22.93 3701 8.65 46.23 0.95 49.53 162.3 SWE130 792 14.03 -26.40 33.03 -0.26 4.96 2.79 -179.1 0.93 0.23 27.14 2461 12.58 21.04 0.63 38.28 136.6 SWE131 1070 8.39 -27.21 40.69 0.31 5.49 2.97 90.4 0.92 -0.18 25.73 1870 10.10 30.32 0.54 36.59 137.1 SWE132 3067 29.30 -34.95 96.68 1.51 9.36 6.91 147.2 1.07 0.94 20.68 2317 2.78 89.46 2.78 74.18 143.0 SWE34A 994 -24.77 -31.15 118.11 1.10 8.50 2.96 59.4 1.46 0.37 38.66 2788 4.56 43.36 1.85 73.71 155.8 SWE93 10815 -29.57 -33.31 205.35 -0.28 6.11 4.71 7.4 1.03 0.38 28.95 2541 6.13 21.57 1.56 32.89 151.5 SWE94 6558 25.10 -32.32 210.45 -0.26 7.33 5.32 -215.4 1.09 0.33 29.64 2604 6.55 42.82 0.78 32.24 172.2 SWE96A 9494 98.06 -29.44 182.51 0.50 5.89 4.71 -196.6 1.62 0.56 26.73 2012 11.40 25.59 0.66 61.22 174.5 SWE96B 15685 20.28 -29.11 186.13 -0.25 5.55 4.68 20.4 1.57 0.52 27.15 1983 12.83 17.01 1.48 33.22 125.0 SWE97 3910 132.94 -31.84 202.00 0.36 6.97 12.19 -217.5 1.56 1.37 27.47 1896 9.25 10.06 3.57 21.11 172.1 TS2 2510 138.97 -36.32 208.40 0.90 8.36 15.24 -231.2 1.17 1.56 23.25 5610 4.95 96.91 3.72 37.79 262.8 WAL7 3726 47.83 -33.08 166.46 -0.24 8.84 7.80 -219.3 1.96 1.50 37.25 3041 0.62 47.85 5.35 34.85 225.7
Table 2 (cont.) Trace element abundances for the Osmundsberg K-bentonite accompanying beds. Shaded areas represent the Osmundsberg samples, others are accompanying beds in alphabetical order. APPENDIX B
ÿ Bivariate diagrams of the Osmundsberg K-bentonite. Figure 1. (a) Eu- Sm/Nd covariance plot of the Osmundsberg K-bentonite samples.
(b) Yb-Hf covariance plot of the Osmundsberg K-bentonite samples.
(c) Th-Eu covariance plot of the Osmundsberg K-bentonite samples.
(d) Yb-Sc covariance plot of the Osmundsberg K-bentonite samples.
(e) TiO2-MnO covariance plot of the Osmundsberg K-bentonite samples.
(f) Yb-Dy covariance plot of the Osmundsberg K-bentonite samples. ( a ) 10 Osmundsberg SWE 132 TS 2 DEN 8 BT 6 WAL 7
Eu 1
0.1 0.01 0.1 1
Sm/Nd
100 Osmundsberg ( b ) SWE 132 TS 2 DEN 8 BT 6 WAL 7
10 Yb
1
0.1 1 10 100
Hf 100 Osmundsberg ( c ) SWE 132 TS 2 DEN 8 BT 6 WAL 7 Th
10 0.1 1 10
Eu
Osmundsberg ( d ) SWE 132 TS 2 10 DEN 8 BT 6 WAL 7 Yb 1
0.1 1 10 100
Sc 10 Osmundsberg ( e ) SWE 312 TS 2 DEN 8 BT 6
1 TiO2
0.1 0.001 0.01 0.1 1
MnO
100 ( f ) Osmundsberg SWE 132 TS 2 DEN 8 BT 6 WAL 7
10 Yb
1
0.1 0.1 1 10 100
Dy Figure 2. (a) Yb-Th covariance plot of the Osmundsberg, and Nova Scotia (Llandovery) K-bentonite samples.
(b) Eu-La/Lu covariance plot of the Osmundsberg, Nova Scotia (Llandovery) and Podolia (Ludlow) K-bentonite samples.
(c) Hf-V covariance plot of the Osmundsberg, and Podolia (Ludlow) K-bentonite samples.
(d) Zr/Tb-Th/Yb covariance plot of the Osmundsberg, and Wales (Llandovery) K- bentonite samples.
(e) Eu-Ta/Yb covariance plot of the Osmundsberg and Nova Scotia (Llandovery) K-bentonite samples.
f) Zr-Th/Yb covariance plot of the Osmundsberg, Wales (Llandovery) and Nova Scotia (Llandovery) K-bentonite samples. Osmundsberg ( a ) WAL 7 TS 2 10 SWE 132 DEN 8 BT 6 Nova Scotia Yb
1
0.1 10 100 1000
Th
Osmundsberg Nova Scotia 10 Podolia SWE 132 ( b ) TS 2 DEN 8 BT 6 WAL 7
Eu 1
0.1 1 10 100 1000
La/Lu 1000 Osmundsberg (c ) Podolia
100 V
10
1 1 10 100
Hf
1000 (d )
100 Zr/Tb
Osmundsberg Wales SWE 132 TS 2 DEN 8 BT 6 WAL 7 10 1 10 100
Th/Yb 10 Osmundsberg ( e ) Nova Scotia SWE 132 TS 2 DEN 8 BT 6 WAL 7
Eu 1
0.1 0.1 1 10
Ta/Yb
Osmundsberg ( f ) Wales Nova Scotia SWE 132 TS 2 DEN 8 BT 6 WAL 7 1000 Zr
100 1 10 100
Th/Yb APPENDIX C
ÿ Step-wise statistics of Discriminant Function analysis
ÿ Trace element data used in the statistical analysis Stepwise Statistics
Model 1
Table 1. Variables in the Analysis
Step F to Remove Wilks' Lambda 1 ZR 24.539 2 ZR 53.874 .773 SC 17.253 .346 3 ZR 48.031 .256 SC 30.289 .181 V 22.076 .145 4 ZR 33.909 .126 SC 21.563 .092 V 22.406 .094 EU 6.999 .051 5 ZR 23.127 .057 SC 19.784 .052 V 23.338 .058 EU 12.223 .039 TH 7.920 .032 6 ZR 14.579 .028 SC 20.354 .035 V 23.311 .038 EU 14.670 .029 TH 10.698 .024 HF 5.825 .019 nvb
Table 2. Wilks' Lambda
Number of Step Variables Lambda 1 1 .346 2 2 .145 3 3 .051 4 4 .032 5 5 .019 6 6 .012
Table 3. Eigenvalues
% of Canonical Function Eigenvalue Variance Cumulative % Correlation 1 16.937a 82.1 82.1 .972 2 3.681a 17.9 100.0 .887 a. First 2 canonical discriminant functions were used in the analysis.
Table 4. Wilks' Lambda
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .012 104.113 12 .000 2 .214 36.271 5 .000 Table 5. Discriminant Function Coefficients
Function 1 2 EU 1.549 .381 HF -.460 -.490 SC -.151 .675 TH .066 .005 V -.039 -.069 ZR .020 .006 (Constant) -2.995 -.649 Unstandardized coefficients
Table 6. Classification Results a
Predicted Group Membership LABEL 1 2 3 Total Original Count 1 16 0 0 16 2 0 9 0 9 3 0 0 4 4 % 1 100.0 .0 .0 100.0 2 .0 100.0 .0 100.0 3 .0 .0 100.0 100.0 a. 100.0% of original grouped cases correctly classified. Stepwise Statistics
Model 2
Table 7. Variables in the Analysis
Wilks' Step F to Remove Lambda 1 ZR 20.422 2 ZR 12.469 .499 EU 6.600 .380 3 ZR 17.501 .307 EU 16.842 .300 SM 11.673 .245 4 ZR 52.365 .286 EU 25.058 .163 SM 22.913 .153 SC 15.932 .122 5 ZR 43.016 .151 EU 23.853 .097 SM 21.865 .091 SC 16.821 .077 TH 7.145 .050 6 ZR 40.941 .098 EU 27.587 .072 SM 21.173 .060 SC 10.269 .039 TH 6.503 .032 V 5.407 .030 7 ZR 12.842 .028 EU 29.997 .050 SM 21.494 .039 SC 10.951 .026 TH 9.604 .024 V 6.822 .020 HF 5.781 .019 Table 8. Wilks' Lambda
Number of Step Variables Lambda 1 1 .380 2 2 .245 3 3 .122 4 4 .050 5 5 .030 6 6 .019 7 7 .012
Table 9. Eigenvalues
% of Canonical Function Eigenvalue Variance Cumulative % Correlation 1 14.164a 75.8 75.8 .966 2 4.531a 24.2 100.0 .905 a. First 2 canonical discriminant functions were used in the analysis.
Table 10. Wilks' Lambda
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .012 97.444 14 .000 2 .181 37.628 6 .000 Table 11. Discriminant Function Coefficients
Function 1 2 EU 1.299 -4.365 HF -.583 .307 SC .044 -.642 SM .036 .986 TH .063 -.011 V -.044 .032 ZR .019 .006 (Constant) -2.747 -1.227 Unstandardized coefficients
Table 12. Classification Results a
Predicted Group Membership LABEL 1 2 3 Total Original Count 1 16 0 0 16 2 0 9 0 9 3 0 0 3 3 % 1 100.0 .0 .0 100.0 2 .0 100.0 .0 100.0 3 .0 .0 100.0 100.0 a. 100.0% of original grouped cases correctly classified. Stepwise Statistics
Model 3
Table 13. Variables in the Analysis
Wilks' Step F to Remove Lambda
1 ZR 38.488 2 ZR 14.061 .313 TH 13.993 .313 3 ZR 18.031 .192 TH 19.404 .200 DY 14.267 .171 4 ZR 17.772 .114 TH 15.299 .106 DY 15.658 .107 V 11.194 .092 5 ZR 23.151 .102 TH 23.523 .103 DY 18.890 .091 V 9.505 .066 LU 5.054 .054 6 ZR 54.349 .092 TH 29.357 .059 DY 4.151 .025 V 8.819 .032 LU 24.618 .053 HF 15.784 .041 7 ZR 51.538 .066 TH 19.943 .034 DY 2.769 .017 V 12.675 .027 LU 23.844 .038 HF 15.177 .030 TA 5.393 .020 8 ZR 55.113 .055 TH 24.912 .031 DY 1.177 .012 V 13.026 .022 LU 26.759 .033 HF 18.352 .026 TA 5.093 .015 SM 4.129 .014 9 ZR 57.020 .060 TH 25.457 .033 V 15.568 .025 LU 37.514 .044 HF 28.072 .036 TA 6.097 .017 SM 6.128 .017 10 ZR 54.231 .046 TH 21.491 .024 V 13.292 .018 LU 40.582 .037 HF 30.061 .030 TA 5.559 .013 SM 3.364 .012 SC 3.950 .012 11 ZR 52.956 .032 TH 19.775 .016 V 14.655 .013 LU 35.747 .024 HF 29.576 .021 TA 13.090 .013 SM 3.899 .008 SC 7.517 .010 YB 6.303 .009 Table 14. Wilks' Lambda
Number of Step Variables Lambda 1 1 .313 2 2 .171 3 3 .092 4 4 .054 5 5 .041 6 6 .020 7 7 .014 8 8 .011 9 7 .012 10 8 .009 11 9 .006
Table 15. Eigenvalues
% of Canonical Function Eigenvalue Variance Cumulative % Correlation 1 24.232a 82.5 82.5 .980 2 5.145a 17.5 100.0 .915 a. First 2 canonical discriminant functions were used in the analys is.
Table 16. Wilks' Lambda
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .006 156.358 18 .000 2 .163 56.286 8 .000 Table 17. Discriminant Function Coefficients
Function 1 2 HF -.678 .019 LU .127 -.007 SC -.039 .321 SM -.117 -.017 TA .528 2.142 TH .109 -.040 V -.002 .015 YB -.003 -.009 ZR .025 -.004 (Constant) -2.819 -6.120 Unstandardized coefficients
Table 18. Classification Resultsa
Predicted Group Membership LABE L 1 2 3 Total Original Count 1 16 0 0 16 2 0 7 0 7 3 0 0 15 15 % 1 100.0 .0 .0 100.0 2 .0 100.0 .0 100.0 3 .0 .0 100.0 100.0 a. 100.0% of original grouped cases correctly classified. Stepwise Statistics
Model 4
Table 19. Variables in the Analysis
Wilks' Step F to Remove Lambda 1 TH 41.935 2 TH 17.677 .320 ZR 14.293 .288 3 TH 37.951 .156 ZR 59.100 .218 HF 37.350 .155 4 TH 28.133 .078 ZR 56.388 .128 HF 38.869 .097 V 10.577 .046 5 TH 15.937 .033 ZR 53.947 .073 HF 28.949 .047 V 16.523 .034 TA 10.906 .028 6 TH 15.230 .021 ZR 55.197 .049 HF 28.266 .030 V 15.762 .021 TA 13.766 .020 SC 8.266 .016 7 TH 18.895 .018 ZR 51.496 .036 HF 28.464 .023 V 15.255 .016 TA 14.823 .016 SC 7.624 .012 CE 4.434 .010 Table 20. Wilks' Lambda
Number of Step Variables Lambda 1 1 .288 2 2 .155 3 3 .046 4 4 .028 5 5 .016 6 6 .010 7 7 .008
Table 21. Eigenvalues
% of Canonical Function Eigenvalue Variance Cumulative % Correlation 1 22.732a 83.6 83.6 .979 2 4.460a 16.4 100.0 .904 a. First 2 canonical discriminant functions were used in the analysis.
Table 22. Wilks' Lambda
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .008 150.794 14 .000 2 .183 52.623 6 .000 gggg
Table 23. Discriminant Function Coefficients
Function 1 2 CE -.009 -.002 HF -.634 .029 SC -.020 .292 TA .663 2.133 TH .103 -.042 V .000 .015 ZR .023 -.005 (Constant) -3.374 -5.814 Unstandardized coefficients
Table 24. Classification Res ults a
Predicted Group Membership LABEL 1 2 3 Total Original Count 1 15 0 0 15 2 0 7 0 7 3 0 0 15 15 Ungrouped 0 0 1 1 cases % 1 100.0 .0 .0 100.0 2 .0 100.0 .0 100.0 3 .0 .0 100.0 100.0 Ungrouped .0 .0 100.0 100.0 cases a. 100.0% of original grouped cases correctly classified.
Stepwise Statistics Model 5
Table 25. Variables in the Analysis
Wilks' Step F to Remove Lambda 1 TH 41.935 2 TH 17.677 .320 ZR 14.293 .288 3 TH 37.951 .156 ZR 59.100 .218 HF 37.350 .155 4 TH 28.133 .078 ZR 56.388 .128 HF 38.869 .097 V 10.577 .046 5 TH 15.937 .033 ZR 53.947 .073 HF 28.949 .047 V 16.523 .034 TA 10.906 .028 6 TH 15.230 .021 ZR 55.197 .049 HF 28.266 .030 V 15.762 .021 TA 13.766 .020 SC 8.266 .016 7 TH 18.895 .018 ZR 51.496 .036 HF 28.464 .023 V 15.255 .016 TA 14.823 .016 SC 7.624 .012 CE 4.434 .010 Table 26. Wilks' Lambda
Number of Step Variables Lambda 1 1 .288 2 2 .155 3 3 .046 4 4 .028 5 5 .016 6 6 .010 7 7 .008
Table 27. Eigenvalues
% of Canonical Function Eigenvalue Variance Cumulative % Correlation 1 22.732a 83.6 83.6 .979 2 4.460a 16.4 100.0 .904 a. First 2 canonical discriminant functions were used in the analys is.
Table 28. Wilks' Lambda
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .008 150.794 14 .000 2 .183 52.623 6 .000 Table 29. Discriminant Function Coefficients
Function 1 2 CE -.009 -.002 HF -.634 .029 SC -.020 .292 TA .663 2.133 TH .103 -.042 V .000 .015 ZR .023 -.005 (Constant) -3.374 -5.814 Unstandardized coefficients
Table 30. Classification Res ultsa
Predicted Group Membership LABEL 1 2 3 Total Original Count 1 15 0 0 15 2 0 7 0 7 3 0 0 15 15 Ungrouped 1 0 1 2 cases % 1 100.0 .0 .0 100.0 2 .0 100.0 .0 100.0 3 .0 .0 100.0 100.0 Ungrouped 50.0 .0 50.0 100.0 cases a. 100.0% of original grouped cases correctly classified. Stepwise Statistics
Model 6
Table 31. Variables in the Analysis
Wilks' Step F to Remove Lambda 1 ZR 25.810 2 ZR 88.967 .701 HF 37.937 .352 3 ZR 93.733 .365 HF 45.728 .201 V 13.985 .092 4 ZR 116.295 .224 HF 70.012 .143 V 16.018 .049 TA 13.117 .044 5 ZR 122.369 .125 HF 54.639 .062 V 20.321 .030 TA 12.077 .022 TB 11.284 .022 6 ZR 116.905 .085 HF 50.174 .041 V 13.959 .017 TA 14.092 .017 TB 15.331 .018 TH 5.445 .011 7 ZR 133.462 .071 HF 57.132 .034 V 13.799 .012 TA 17.861 .014 TB 11.069 .011 TH 4.481 .008 YB 4.384 .008 8 ZR 177.221 .063 HF 65.548 .025 V 12.849 .008 TA 21.646 .011 TB 12.096 .008 TH 4.217 .005 YB 6.551 .006 U 5.766 .005 Table 32. Wilks' Lambda
Number of Step Variables Lambda 1 1 .352 2 2 .092 3 3 .044 4 4 .022 5 5 .011 6 6 .008 7 7 .005 8 8 .004
Table 33. Eigenvalues
% of Canonical Function Eigenvalue Variance Cumulative % Correlation 1 32.549a 81.3 81.3 .985 2 7.510a 18.7 100.0 .939 a. First 2 canonical discriminant functions were used in the analys is.
Table 34. Wilks' Lambda
Wilks' Test of Function(s) Lambda Chi-square df Sig. 1 through 2 .004 138.529 16 .000 2 .118 52.460 7 .000
ff Table 35. Discriminant Function Coefficients
Function 1 2 HF -1.097 .148 TA 2.131 1.037 TB -1.243 2.005 TH .014 -.090 U -.152 -.010 V .010 .015 YB .558 .138 ZR .037 -.011 (Constant) -3.419 -2.460 Unstandardized coefficients
Table 36. Class ification Resultsa
Predicted Group Members hip LABEL 1 2 3 Total Original Count 1 11 0 0 11 2 0 14 0 14 3 0 0 6 6 Ungrouped 2 3 0 5 cases % 1 100.0 .0 .0 100.0 2 .0 100.0 .0 100.0 3 .0 .0 100.0 100.0 Ungrouped 40.0 60.0 .0 100.0 cases a. 100.0% of original grouped cases correctly classified. Table 37. Trace element data of the Osmundsberg K- bentonite Baltoscandia and British Isles
Table 38. Trace element data for K- bentonites from Arisaig, Nova Scotia (Bergström et al. 1997) Table 39. Trace element data for K- bentonites from British Isles (Huff et al., 1996) Table 40. Trace element data for K- bentonites from Baltoscandia and British Isles. Trace element data for K- bentonites from Estonia (Huff pers. com.) . Table 42 Table 41. Trace element data for K- bentonites from Podolia, Ukraine (Huff et al. 2000)