• 2009-2010 Roussos Dimitrakopoulos
• 2008 Donald Myers
• 2007 Vera Pawlowsky-Glahn
  
• 2006 Larry W. Lake
  
• 2005 Larry Drew
  
• 2004 Frederick P. Agterberg
  
• 2002 John C. Davis
  
  

2009-2010 IAMG Distinguished Lecturer Series

Roussos Dimitrakopoulos, the IAMG Distinguished Lecturer for 2009, is professor and holds the Canada Research Chair (Tier I) in “Sustainable Mineral Resource Development and Optimization Under Uncertainty – BHP Billiton”, at the Department of Mining and Materials Engineering, McGill University in Montreal, Canada.   Roussos serves as the Editor-in-Chief of the journal of Mathematical Geosciences published by Springer and is also Director of McGill’s COSMO Laboratory.  Previously he was Professor and Director of the Bryan Research Centre, Univ. of Queensland, Australia. He holds a PhD from École Polytechnique, Montreal, and a MSc from the University of Alberta, Edmonton. He has been working in stochastic spatial simulation and optimization since 1983, and the last decade on risk-based optimization in mine planning and valuation. Roussos has been Senior Geostatistician with Newmont Mining Co., Denver, and Senior Consultant with Geostat Systems Int’l. He has taught and worked in North America, Australia, South America, Europe, the Middle East, South Africa and Japan.
URL: http://people.mcgill.ca/roussos.dimitrakopoulos/

Institutions interested in having Prof. Dimitrakopoulos visit should contact the DL Committee Chairman, Sean McKenna, at samcken@sandia.gov
 

Lecture 1

An Overview of Modern Stochastic Conditional Simulations:  Fast and efficient, point and block support, Gaussian and non-Gaussian including high-order, sequential simulations       

Modeling the spatial uncertainty of natural phenomena may require large size simulations (grid sizes up to 108) and a new ‘line’ of sequential approaches with low computational costs can be used.  After giving examples of the ‘size’ issue, this presentation provides a general overview of sequential decomposition of a pdf for simulating very large fields at point-support scale.  Subsequently, the approach is expanded to the direct simulation at the block-support scale. The differences in computational performance is documented in examples and further discussed for the case of efficient multivariable simulations.  The last part of the presentation considers an expansion of sequential approaches beyond the second-order methods currently employed, and shows how the sequential framework is developed to high-order, non-Gaussian, non-linear simulation.        

Lecture 2

An Introduction to Stochastic Simulation: Basic concepts made easy and examples     

Modeling the spatial uncertainty of natural phenomena using geostatistical or spatial stochastic simulations is commonly used.   This presentation aims to introduce the non specialist to:  (a) basic concepts presented in an intuitive way, through examples; (b) the type of problems addressed with respect to natural spatial or spatial-temporal phenomena; (c) introduce the concept of random number generation; (c) the generation of correlated numbers and conditional distributions; (d) the ‘intuitive’ sequential Monte Carlo sampling; and (e) using the above to solve different problems (environment, mining, reservoirs).       

Lecture 3

High-order Geostatistics:  Simulating complex, non-Gaussian geological and environmental phenomena

Geo-science and engineering related phenomena such as characteristics of mineral deposits and attributes of petroleum reservoirs, pollution levels, the earth’s surface temperature, and so on, represent complex natural systems distributed in space. Their spatial distributions are currently predicted from finite measurements and second-order spatial statistical models. The latter models are limiting, as geo-systems are commonly highly complex, non-Gaussian and exhibit non-linear patterns of spatial connectivity. Non-linear and non-Gaussian high order geostatistics is a new area of research based on higher-order spatial connectivity measures termed spatial cumulants.  

In this presentation, definitions of high-order statistics are first given, then, the inference with spatial templates and interpretation of anisotropic cumulants are introduced. Several examples are presented to elucidate the concepts stressing the physical interpretation of cumulant maps.  Subsequently, new research results on ‘high-order’ conditional simulations are shown.  A new simulation method is outlined and is founded upon spatial cumulants in the high-order space of Legendre polynomials. The method does not require any data pre-processing or transformations, it is shown in the examples presented to reproduce complex spatial geometries, bimodal data distributions, and the high-order cumulants of the data used.  The presentation concludes with the ‘down stream’ effects from the use of simulation approaches to engineering problem solving.   
 
Lecture 4

An Extended View of Mining Geostatistics:  Integrating short- and long- term mine production forecasting under uncertainty and application in a major gold mine

Do our models work?  If they do, what could they encompass?  How do our predictive models compare to reality?  What type of problems surface in the world of engineering?  These are the types of questions addressed here, through a specific example from the world of mining and metal production.  The presentation explores stochastic optimization for mine production scheduling as a space and time problem, integrated with stochastic simulations of orebodies with data updating capabilities, and simulation of non-available “future data”.  A large gold mine and tests conducted demonstrate that problems exist, how stochastic solutions perform, and how this adds value to the operation. 

Lecture 5

Mining Geostatistics Revisited: Limits of the current paradigm, non-linearity of the chain of mining, extended stochastic solutions, applications and monetary value  

Conventional approaches to estimating reserves and optimization for mine planning and production forecasting result in single, often biased forecasts. This is largely due to the non-linear propagation of errors in understanding orebody attributes from a limited finite number of drilling data., throughout the chain of mine planning and mining  A ‘redefinition’ of mining geostatistics is considered to include two interacting and potentially fusing elements: stochastic simulation and stochastic optimisation. These two elements provide an expanded mathematical framework that allows modelling of orebody uncertainty and its direct integration to mine design, planning and valuation of mining projects and operations. The pertinent mathematical models and multiple examples show the key characteristics and value of this redefined geostatistical modelling framework. 

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Prof. DONALD E. MYERS: 2008 IAMG DISTINGUISHED LECTURER


Donald Myers is Emeritus Professor of Mathematics and Hydrology at the University of Arizona. Don is one of “giants” within the IAMG and the broader communities of mathematical geology, spatial statistics, and environometrics. He has devoted almost his entire career to the applications of mathematics and statistics in the earth and environmental sciences and has a distinguished record of scholarship in this arena. He has numerous publications in IAMG journals, as well as many others in scientific journals of related interest to most IAMG members, and he is well-known within the IAMG community. Don is a good speaker, is enthusiastic about mathematical geology, and is well-traveled around the globe, having given at least 30 presentations outside the United States over the past 10 years. 

Don Myers is available to meet informally for discussion with small groups. He is prepared to present the following one hour lectures, each can be tailored somewhat to specific audiences. There will be a strong emphasis on the use of software and actual data in each of the lectures.

This e-mail address is being protected from spam bots, you need JavaScript enabled to view it Institutions that may wish to host lectures by Dr. Myers are invited to contact him directly at myers[at]math.arizona.edu or to contact Sean McKenna, chair of the DL Committee, at samcken@sandia.gov

I. For a general audience with little prior knowledge of geostatistics:
HISTORY OF GEOSTATISTICS - PAST, PRESENT AND FUTURE Geostatistics as we know it now is only about 45 years old although clearly it is based on earlier ideas. Initially and even yet to a considerable extent it has developed outside of the statistical community, its development being heavily influenced by applications. While similar ideas were being put forward by Gandin in the USSR and Matérn at about the same time, it was the work of G. Matheron and his students at the Centre de Geostatistiques that prompted the spread of geostatistics in mining, hydrology, petroleum in the early years. Geostatistics might also be viewed as a special case of spatial statistics which also is a relatively recent development. Geostatistics and more generally spatial statistics have been greatly influenced by the development of fast, inexpensive computing. The development and availability of software for geostatistics has also been a critical factor.

II. CONNECTIONS - GEOSTATISTICS, RADIAL BASIS FUNCTIONS AND OBJECTIVE ANALYSIS
Objective Analysis was the name given to the work of Gandin and it was primarily known in the atmospheric sciences. It has largely been absorbed and merged with the results and ideas of geostatistics. In contrast the work of R. Hardy in the early 1970's on interpolation of gravity data was and is best known in the numerical analysis literature. The equivalence between the RBF interpolating function and the kriging estimator as well as between the equations determining the coefficients requires only basic linear algebra. However the thrust in terms of applications has remained quite different. Moreover the emphasis is almost entirely on radial, i.e., isotropic basis functions in the Radial Basis Function literature. The direct derivations for Radial Basis functions appear to depend on deterministic assumptions rather than statistical assumptions but this is more a difference in interpretation.

III. NON-GEOMETRIC ANISOTROPIES AND SPACE - TIME MODELING
Continuity is a basic function property in analysis but it is deterministic and generally is taken to be non-directional. The variogram and (auto) covariance function are statistical measures of the degree of continuity when it is not deterministic and they might be directionally dependent. The practical problem is constructing valid variograms or covariance functions incorporating directional dependence in the right way. Models where only the range of dependence is directionally dependent can be obtained by a stretching and a rotation on the underlying space. More complicated models are necessary if the sill or other parameters change with direction. Space-time models are a special case of this latter problem and various authors have used different constructions. The work of Cressie- Huang, De Cesare-Myers-De Iaco and Posa, Ma, Fuentes, Gneiting, Stein and others will be reviewed.

IV. MULTIVARIATE SPATIAL STATISTICS
Some authors have used the term “multivariate statistics” to mean spatial problems in higher dimensional space. But more commonly it means that there are several variables of interest in which case the key question is whether there is some form of dependence between the variables. The dependence may be deterministic, e.g., the differential equation linking head and hydraulic conductivity, or it may be statistical. The difference between variables may be one of the scale of observation, e.g., ground based observation vs satellite mounted sensor observations or core assays vs “block” assays. Sometimes the relationship is assumed to be one of “cause and effect” but does not give rise to an analytic expression. Linear models (including Linear Mixed models and Generalized Linear Mixed Models) is one method for obtaining empirical relationships. Cokriging in its various forms is a generalization of kriging from the univariate form. Various problems arise in applying each of these techniques and they overlap to some extent. Cokriging is often used to utilize the redundancy in multivariate data to compensate for a lack of data for some variables by using spatial cross-correlations between pairs of variables as well as the spatial correlations for each variable separately. There are both practical and theoretical problems with applying these techniques. Their development have been strongly driven by applications.

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Department of Computer Science and Applied Mathematics
University of Girona, Spain
e-mail: vera.pawlowsky@udg.es

Vera Pawlowsky-Glahn is a professor of the Department of Computer Science and Applied Mathematics at the University of Girona. She studied Mathematics at the University of Barcelona in Spain and obtained her PhD (doctor rerum naturam) from the Free University of Berlin in Germany. Before going to Girona, she was professor at the School of Civil Engineering at the Technical University of Catalonia (UPC) in Barcelona. Her main research topic since 1982 has been the statistical analysis of compositional data. The results obtained over the years have been published in multiple articles, proceedings and a book in the Oxford University Press series Studies in Mathematical Geology. She has been guest editor for a special issue on this topic for Mathematical Geology in 2005 and has acted, together with A. Buccianti and G. Mateu-Figueras, as editor of a book on compositional data analysis published by the Geological Society, London, as special publication 264. She is the leader of a research group on this topic involving professors from different Spanish universities located in Girona, Barcelona, Murcia and Cáceres. The group organises every two years a workshop on compositional data analysis, known as CoDaWork, and their research has received regularly financial support from the Spanish Ministry for Education and Science and from the University Department of the Catalan Government. Vera Pawlowsky-Glahn has been vice-chancellor at UPC from 1990 to 1994, head of the Department of Computer Science and Applied Mathematics at the University of Girona in 2004-05, and dean of the Graduate School of the University of Girona in 2005-06. She received in 2006 the William Christian Krumbein Medal of IAMG.

Dr. Pawlowsky-Glahn has prepared lectures and a short course on the following topics:

1. Hypothesis underlying statistical data analysis
Hypothesis underlying standard mathematical models for the statistical analysis of real-life data relay on the Euclidean geometry of real space. They are universally accepted _exception made of some particular cases, like directional data_ despite the fact that they not always comply with intuition. The aim of this talk is to show _based on her research in the field of compositional data analysis_ how she learned that it is possible to obtain models where both common sense and hypothesis agree. Examples using real geological data are used for illustration.

2. The Aitchison geometry of the simplex and the statistical analysis of compositional data
Since John Aitchison introduced in 1982 the log-ratio approach for compositional data analysis, much work has been done to analyse the algebraic-geometric structure of their sample space, the D-part simplex. In this talk, the real Euclidean space structure of the simplex is presented, and the implications for the statistical analysis of compositional data are illustrated developing case studies in the field of the geosciences.

3. Geostatistical analysis of compositional data
Like compositional data in general, spatially dependent compositional data present problems, like spurious spatial correlation. In this talk, compositional co-kriging is presented, which is based on the Aitchison geometry of the simplex, the sample space of compositional data. Also, simplicial indicator kriging (IK) is discussed as a particular case of compositional co-kriging. This approach avoids by construction all the standard drawbacks of IK, like estimates outside the (0,1) interval or order-relation problems. The potential is illustrated with real case studies.

4. The statistical analysis on coordinates in constrained sample spaces
Phenomena with a constrained sample space and relative measure of difference are frequent in practice: rain fallen within a certain period in meteorology is always positive; relative humidity in a soil sample lies in the (0,1) interval; (sand,silt,clay) composition of sediments lies in the 3-part simplex. In this talk it is shown how these facts can be taken into account to perform a proper statistical analysis which produces meaningful results using easy-to-apply techniques.

Short course (12 hours, 2-3 days): The statistical analysis of compositional data
1. Hypothesis underlying statistical data analysis
2. The Aitchison geometry of the simplex
3. Exploratory analysis (biplot, balances-dendrogram)
4. Distributions on the simplex
5. Parameter estimation and hypothesis testing (optional)
6. Linear models
7. Geostatistical analysis of compositional data (optional)
8. Discussion of case studies

Vera Pawlowsky-Glahn has agreed to give lectures in Neuchatel (Swiss Confederation) in December 2006; in Firenze (Italy) in January 2007; in Toronto and Ottawa (Canada) in February 2007; and in Bogotá (Colombia) in March 2007. Further plans include a second visit to Canada, a European Tour, and a visit to China. IAMG provides for travelling expenses within a reasonable amount. Inviting institutions are expected to provide for local expenses.

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Distinguished Lecturer 2006: Prof. Larry W. Lake

Larry W. Lake is a professor of the Department of Petroleum and Geosystems Engineering at The University of Texas at Austin and director of the Enhanced Oil Recovery Research program. He holds B.S.E and Ph.D. degrees in Chemical Engineering from Arizona State University and Rice University. Dr. Lake has published widely and frequently conducts industrial and professional society short courses in enhanced oil recovery and reservoir characterization. He is the author or co-author of more than 100 technical papers, three textbooks and the editor of three bound volumes. He has been teaching at UT for 24 years prior to which he worked for Shell Development Company in Houston, Texas. He was chairman of the department from 1989 to 1997 and formerly held the Shell Distinguished Chair and the W.A. (Tex) Moncrief, Jr. Centennial Endowed Chair in Petroleum Engineering. He currently holds the W.A. (Monty) Moncrief Centennial Chair in Petroleum Engineering. He has served on the Board of Directors for the Society of Petroleum Engineers (SPE) as well as on several of its committees; he has been an SPE distinguished lecturer. Dr. Lake is a member of the National Academy of Engineers and won the 1996 Anthony F. Lucas Gold Medal of the SPE. He also has won the 1999-2000 Billy and Claude R. Hocott Distinguished Research Award and The University of Texas and the SPE/DOE Symposium IOR Pioneer Award in 2000. In 2000 he was also awarded the SPE Distinguished Service Award, and in 2001, was chosen as a member of the Texas Society of Professional Engineers Dream Team.

Since he was selected as IAMG Distinguished Lecturer for 2006 Larry Lake has given about a dozen lectures so far. Total audience has been about 350-400 people with a nearly even mix of students and professionals. Among other locations he has spoken at Sandia (about 70 people), and he did a great job of publicizing IAMG. He has also done a tour through Canada in late May including, among others, a visit at the University of British Columbia, Memorial University of Newfoundland, and a talk at the Geological Survey with the title: “The Oil Business; A Personal Assessment of Uncertainty”.

A tour of Europe is in the planning stages, in connection with attending the annual meeting in Liège, Belgium.

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Dr. Lawrence J. Drew of the United States Geological Survey is the IAMG 2005 Distinguished Lecturer. Larry Drew attended the University of New Hampshire (B.Sc. Degree in Geology and Chemistry), The Pennsylvania State University (M.Sc. and Ph.D. degrees in Mineralogy and Petrology and Statistics and a Post-Doctoral Fellow), and Virginia Polytechnic Institute (M.A. in Economics). He was employed by Geotech Inc. (1967-1969), Cities Service Oil Company (1969-1972), and the U.S. Geological Survey (1972-present). During his career, he has specialized in oil and gas and mineral-resource assessment, structural geology and tectonics as related to the emplacement of mineral deposits, environmental issues, and, more recently, the assessment of natural aggregate, ground water in fractured reservoirs, and regional geochemistry.
Dr. Drew has published over 200 scientific papers and abstracts, written two books, conducted workshops throughout the world, and been the keynote speaker at numerous national and international meetings and conferences.

In recognition of his research, Dr. Drew has been awarded the Meritorious Service Award by the U.S. Department of the Interior and the Griffith Teaching Award by the International Association for Mathematical Geology (IAMG).

Institutions that may wish to host lectures by Dr. Drew are invited to contact him directly at (ldrew@usgs.gov) or to contact Sean McKenna, chair of the DL Committee, at (samcken@sandia.gov)
Available Lectures

1. Regional Geochemistry-Baselines for Complex Geological Terranes
The application of GIS and Statistical/Graphical methods to establish baseline regional geochemical signatures for complex geological terranes. The State of South Carolina comprises multiple geological terranes that range from high-rank metamorphic and igneous rocks to volcanic rock with ore bodies to Tertiary sediments. These terranes occur in many geomorphic land-forms-upland, fall zones, incised sedimentary sections, and the coastal plain. The goal is to unravel a complex puzzle.

2. Hydrologic Significance of the Association Between Well-Yield Variography and Structures in Fractured Bedrock Aquifers
A surprising result has been recently obtained -the structural characteristics of fractured bedrock aquifers are directly associated with patterns in variogram maps and directional variograms. Variogram mapping on nets of initial yields of water wells decodes complex, underlying tectonic information in the bedrock.

3. Oil and Gas Discovery Process Modeling
Based on research published in several books and many papers, a summary of the importance of discovery process model to forecasting undiscovered oil and gas is presented. What is a field- size distribution?

4. Mineral Deposits-Grades to Tonnages to Economic Filters
Why do we use such terms as "mineral deposit" and "mineral occurrence"? The answer lies somewhere in the nexus among mineral deposit models, grade and tonnage models, and the metric for the probabilities for mineral-deposit occurrence.

5. Ecocentrism and Anthropocentrism-Are They End Members in Environmentalism or Not?
This lecture is based on over 30 columns and papers written on the environmentalism associated with the production of raw materials with some microeconomics thrown in.

6. From Bayan Obo to Muruntau to Porphyry Copper Deposits
It began with two super-giant mineral deposits, one in China and the other in Uzbekistan, and then continued with tectonics and structural geology. The author will tell the tale of his interlude into economic geology beginning with these two super-giant mineral deposits and then on to research in the occurrence of ore bodies through the eye of a tectonicist and structural geologist.

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Dr. Frederik P. Agterberg is the IAMG's Distinguished Lecturer for 2004.

Frederik Pieter (Frits) Agterberg was born in 1936 in Utrecht, the Netherlands. He studied geology and geophysics at Utrecht University obtaining BSc (1957), MSc (1959) and PhD (1961). These three degrees were obtained "cum laude" (with distinction). After a one-year Wisconsin Alumni Research Foundation postdoctorate fellowship at the University of Wisconsin, he joined the Geological Survey of Canada in 1962, initially as petrological statistician working on the Canadian contribution to the International Upper Mantle Project.

Later he formed and headed the Geomathematics Section of the Geological Survey of Canada in Ottawa (1971-1996). The primary objectives of this group were (1) to develop and apply computer-based geo-scientific data integration techniques for mineral potential mapping; and (2) to provide mathematical and statistical consulting services to other scientists within the Geological Survey of Canada.

Agterberg has authored or co-authored over 200 scientific publications including the textbook "Geomathematics: Mathematical Background and Geo-science Applications" published in 1974 by Elsevier with approximately 10,000 copies sold word-wide, and the monograph "Automated Stratigraphic Correlation" (1990). He has edited or co-edited seven other books and special issues in scientific journals.

In 1978 he became the third W.C. Krumbein medallist of the International Association for Mathematical Geology. He won Best Paper Awards for articles in the international scientific journal "Computers & Geosciences" for 1978, 1979 and 1982. Other honors include his appointment as correspondent of the Royal Dutch Academy of Sciences in 1981, and as Honorary Professor of the China University of Geo-sciences in 1987. A newly discovered fossil was named after him Adercotrima agterbergi to recognize his contributions to quantitative stratigraphy.

Since 1968 he is associated with the University of Ottawa where he has taught an undergraduate course on "Statistics in Geology" for 25 years, and directed the research of four undergraduate and nine graduate students (7 PhD and 2 MSc). Several of his former students now occupy prominent positions in the mining industry, universities and government organizations in Canada and abroad. Other academic positions included being Distinguished Visiting Research Scientist at the Kansas Geological Survey of the University of Kansas (1969-1970), Adjunct Professor at Syracuse University (1977-1981), Esso Distinguished Lecturer for the Australian Mineral Resource Foundation, University of Sydney (August - November 1980), and Adjunct Research Professor, Department of Mathematics, Carleton University, Ottawa (1986-1994).

Agterberg has lectured in more than 40 short courses worldwide. From 1979 to 1985 he was Leader of the International Geological Correlation Programme's Project on "Quantitative Stratigraphic Correlation Techniques". He has served on numerous committees, editorial boards, and councils of national and international organizations. This included being an associate editor of both the Canadian Journal of Earth Sciences and the Bulletin of the Canadian Institute of Mining and Metallurgy. Recently (1996-2000), he chaired the Publications Committee of the International Association for Mathematical Geology and the Quantitative Stratigraphy Committee of the International Stratigraphic Commission that is part of the International Union of Geological Sciences.

In 1996, Frits Agterberg commenced a phased retirement from the Geological Survey of Canada to work as part-time independent geomathematical consultant for industry. He continues to teach and supervise graduate students at the University of Ottawa.

Dr. Agterberg has prepared lectures on the following topics:

Past and Future of Mathematical Geology
Applications of mathematics in geology commenced slowly during the 19th and the first half of the 20th century. With the advent of computers, numerical modeling in the geosciences became increasingly accelerated. This includes new methods of 3D geological map-making.

Probabilistic of Mineral Resource Potential Mapping
The processing of geoscientific information for the purpose of estimating probabilities of occurrence for various types of mineral deposits was made easier when Geographic Information Systems became available. Weights of evidence modeling and logistic regression are examples of techniques to be discussed.

Lognormal Distributions and Pareto Tails in Geochemistry and Resource Appraisal
The lognormal frequency distribution model has seen many successful geoscientific applications. This includes the modeling of trace element concentration values in rock samples and the sizes of ore deposits and oil pools. Multifractal modeling provides clues on how lognormal distributions can have Pareto tails.

Statistical Methods used for Construction of the 2004 Geological Time Scale
A newly constructed geological time scale uses statistical techniques for integrating age determinations with stratigraphic information. Maximum likelihood chronograms and smoothing splines are used to provide estimates of the ages of chronostratigraphic boundaries and unit durations.

Automated Stratigraphic Correlation
Earliest and latest occurrences of fossils can be sequenced and scaled for the construction of regional stratigraphic zonation columns with incorporation of lithostratigraphic and seismic information. Land-based sections or exploratory wells can be correlated with one another using the scaled optimum sequence.

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Dr. John C. Davis (Kansas Geological Survey) is the IAMG's 2002 Distinguished Lecturer.

He will be familiar to many as the author of the classic text "Statistics and Data Analysis in Geology", recently released in its 3rd. edition. Institutions interested in hosting a lecture by Dr. Davis are invited to submit a proposal to Alexandre Desbarats, chair of the IAMG Distinguished Lecturer Committee (desbarat@NRCan.gc.ca) or directly to Dr. Davis (jdavis@kgs.ku.edu). The IAMG will fund the speaker's travel expenses to the extent allowed by the DL series budget; However, host institutions will be expected to contribute toward the speaker's meals and accommodation as their resources permit. Dr. Davis has prepared a selection of talks suitable for a variety of earth science audiences and technical levels

1. Computing Risk for Oil Prospects : Even a little operator can use big tools!
This presentation is on the quantitative evaluation of petroleum prospects. It is based on research conducted by Dr. Davis at the KGS since 1973, and which has resulted in two books, two industry training programs, an academic course, and numerous publications. Most of the examples in the presentation use data on oil exploration in Kansas, although additional material is drawn from his cooperative research on regionalization conducted with Prof. Jan Harff of the Institute for Baltic Research in Germany. This presentation would be of interest to those concerned with improving the the state-of-the-practice in prospect evaluation and resource estimation.

2. Geological Hazard Prediction : Landslides--Not tornados--In Kansas??
This presentation draws on recent research conducted by Dr. Davis in cooperation with Dr. Greg Olmacher on risk assessment applied to landslides. This research project in northeastern Kansas is still underway and a presentation of the mathematical theory behind the risk assessment procedure was given at the 12th Annual Conference of the IAMG in Berlin. The presentation includes additional recent work on environmental hazards done by Gunther Hausberger in Austria.

3. Geochemical Data and How to Map It : Looking for minerals--Finding the environment
The topic of this presentation is the analysis of multiple geological properties. It is based on material from several sources, but mostly on the work done by Dr. Davis during his tenure as a Fulbright scholar in Austria. This material consists of geochemical data produced for the Geochemical Atlas of the Austrian Republic, for which the KGS provided mapping software solutions and advice on statistical analyses. Additional examples are drawn from grain-size data from the Baltic Sea provided by the Institute for Baltic Research. These data are used to illustrate discussions on the issue of closure and the application of multivariate statistical methods such as canonical analysis.

4. Classical Statistics for Geological Problems : Regulation, monitoring, and other nasty tasks
The role of classical statistics in the analysis of geologic data is the subject of this presentation which is based on KGS experience in quality control and analysis of variance applied to water level measurements in the High Plains Aquifer of western Kansas. The presentation also describes applications of regression and time-series analysis to climate data.

5. Alternatives for an Unpopular Business : Decision-making in the mining and mineral industry
This presentation describes the use of probabilistic modeling in the minerals industry. It addresses the possible costs of societal decisions that may adversely affect mining, and how financial models incorporating alternative actions can be used as management decision tools. Although these risk-based methodologies are not widely known in the mining industry, they are commonly used in petroleum exploration and are discussed in the book, Computing Risk for Oil Prospects, co-authored by Dr. Davis.

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last update 2011-01-08