2021 IAMG Distinguished Lecturer – Jaime Gómez-Hernández
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Jaime Gómez-Hernández, the 2021 Distinguished Lecturer still believes that he could travel to deliver his lecture in person during the last quarter of the year. Interested institutions should get in touch with him to organize his visit. The following three talks are offered, all of them oriented to the larger public.
My name is Filter, Kalman Filter
In this talk, Jaime will introduce the Kalman filter and how it has evolved into the ensemble Kalman filter and its variants. From the original work by Kalman to the Extended Kalman filter to the Ensemble Kalman filter (EnKF) and beyond. He will comment on the similarities with cokriging, and will demonstrate the application of the EnKF for parameter identification in hydrology.
Inverse modeling in hydrogeology
A review of methods and practices for inverse modeling in hydrogeology. Jaime will start introducing the problem and will present a journey of how inverse modeling has evolved in hydrogeology in the last forty years.
Who is to blame?
Contaminant source identification whether in an aquifer, a river or a water distribution system is a hard problem to solve. On many occasions, contamination is detected in the system but when, where and how much contaminant was introduced is unknown. Forensic methods have been used for the identification of the source and its parameters. Jaime will make a historical review contaminant source identification in hydrogeology and present some cases from hydrology, hydrogeology and water distribution systems.
2020 IAMG Distinguished Lecturer – Professor Peter M. Atkinson BSc, PhD, MBA, FLSW, FRSS, FRGS, FRSPS
Peter is Distinguished Professor of Spatial Data Science and Dean of the Faculty of Science and Technology at at Lancaster University. Peter is also Visiting Professor at the University of Southampton, Southampton, UK and the Chinese Academy of Sciences, Beijing, China. Peter is a Fellow of the Learned Society of Wales (FLSW) and is also the 2016 Laureat of the Peter Burrough Medal of the International Spatial Accuracy Research Association (ISARA), recipient of the Belle van Zuylen Chair at Utrecht University in 2015-16, and Visiting Fellow at Green-Templeton College, Oxford University in 2012-14.
Peter’s research focus is in spatial data science, including Bayesian spatial and spatio-temporal statistics and physical dynamic models applied to a range of environmental and epidemiological phenomena using remote sensing and other big data. Four most significant themes of research group are (i) spatial epidemiology of vector-borne disease transmission systems, including Trypanosomiasis and malaria, (ii) remote sensing of global changes in vegetation phenology and its climate drivers, (iii) spatial modelling of natural hazards and their impacts, including flood forecasting, landslide susceptibility mapping and near-Earth object impact simulation based on Newtonian orbital dynamics (iv) remote sensing image downscaling and image fusion, based on explicit models of the space-time sampling framework.
Peter has published over 300 peer-reviewed articles in international scientific journals, with a Thompson H-index of 73 in Google Scholar and H-index of 55 in WoS. In addition, he has published one authored book, edited eight books, written over 50 refereed book chapters and edited eight journal special issues. He has led multiple large grants and supervised 60 PhD students. Peter is Editor-in-Chief of the journal Science of Remote Sensing and Associate Editor of Environmetrics, and sits on the editorial boards of Geographical Analysis, Spatial Statistics, the International Journal of Applied Earth Observation and Geoinformation and Environmental Informatics.
Contact by email at firstname.lastname@example.org
12-16.07.2021 Distinguished Lecture (IAMG), Geostats 2020 Conference, Toronto, Canada (postponed) 20/20 Hindsight Talk (title to be confirmed)
16-18.06.2021 Distinguished Lecture (IAMG), GeoENV 2020, Parma, Italy, Implications of the PSF for Downscaling and Image Fusion in Remote Sensing
- 26.05.2021 Distinguished Lecture (IAMG), Indian Statistical Institute, India, The Importance of Representations for Spatial Data Science
- 15.04.2021 Distinguished Lecture (IAMG), GISRUK 2021 conference, Cardiff University, Wales, UK, A Novel Paradigm for Simultaneous Land Use and Land Cover Classification
- 26.11.2020 Distinguished Lecture (IAMG), International Geospatial Week, National Geographic Institute of Colombia, Columbia Trends in Geospatial Data Science and Remote Sensing
- 12.11.2020 Distinguished Lecture (IAMG), IAMG Student Chapter, University of Freiberg, Germany, Implications of the PSF for Downscaling and Image Fusion in Remote Sensing
Invitations that were postponed or cancelled.
- 2020 Distinguished Lecture (IAMG), Spatial Accuracy conference, Buffalo, USA (invited, cancelled)
2019 IAMG Distinguished Lecturer – Philippe Renard
Philippe Renard is Associate Professor of Hydrogeology at the University of Neuchatel Switzerland where he leads the stochastic hydrogeology group. He graduated from the Nancy School of Geology in Nancy, France and obtained his PhD from École des Mines de Paris in 1996. He was water supply engineer in Kankan, Guinea from 1992 to 1993 and lecturer in hydrogeology at the Swiss Federal Institute of Technology Zurich (ETHZ) from 1997 to 2001.
His research focuses on stochastic hydrogeology and aquifer characterization. This includes various aspects such as groundwater hydraulics in porous, fractured or karstic rocks and the development of fast upscaling techniques for hydraulic conductivity. In the field of geostatistics, he has mainly been involved during the last 10 years in the development of multiple-point statistics methods and their application to a wide range of problems from 3D geological modeling to the simulation of climate variables. Since Neuchâtel is located at the foot of the Jura mountain, a special emphasis was also devoted to the study and modeling of karstic systems. Philippe and his team developed novel pseudo genetic methods allowing to simulate cave network structures in a probabilistic manner. He has also led studies related to the analysis and modeling of seawater intrusion processes in coastal aquifers in Cyprus, Tunisia, and Yucatan. Philippe was editor of Hydrogeology Journal from 2005 to 2011, president of the geoENVia association promoting the application of geostatistics for environmental sciences from 2006 to 2010 and organizer of several conferences such as the geoENV and Eurokarst biannual events. He is the author of more than 115 scientific articles ( http://members.unine.ch/philippe.renard/biblio.html ) in international journals. He has been the initiator of the world-wide hydrogeological parameter data base http://wwhypda.org , an open source project aiming at facilitating the access of well referenced data allowing to construct ex-situ prior distributions. He initiated and manages the Hydrogeologist Time Capsule project https://timecapsule.iah.org devoted to interviewing leading hydrogeologists.
Contact by email at Philippe.Renard@unine.ch
Université de Lorraine, Georesources Laboratory, IAMG student chapter. Nancy, France. 11 March 2019.
Université de Montpellier, Hydrosciences Laboratory, Montpellier, France. 19 March 2019
Southern University of Science and Technology, School of Environmental Science and Engineering, Shenzhen, China. 26 April 2019.
The University of Hong Kong, Department of Earth Sciences. Hong Kong. 29 April 2019.
Venice International University. 1-5 July 2019. Teaching geostatistics and aquifer characterization within the PhD school :
Title: Hydrogeophysical inversion and data assimilation for the characterization and monitoring of coastal aquifers.
IAMG 2019 conference. Penn’ State University. 10-16 August 2019.
2018 IAMG Distinguished Lecturer – Grégoire Mariéthoz
Gregoire Mariethoz’s interests reside in ways of characterizing the spatial and temporal variability inherent to most hydrological systems. This involves developing and using a variety of methods including geostatistics (in particular approaches based on training images), image analysis and inverse problems. Such methods are then applied to complex datasets like remote sensing images, hydrological time series or complex aquifers.
He obtained his PhD in 2009 at the University of Neuchâtel, Switzerland. After that, he worked as a researcher at Stanford University, USA. He then moved to the University of New South Wales, Australia, where he was for four years senior lecturer in hydrogeology and chief investigator in the National Center for Groundwater Research and Training. Since 2014, he is professor assistant at the University of Lausanne in Switzerland.
His record includes 80 peer-reviewed journal papers and over 10 major research grants. He developed the Direct Sampling multiple-point simulation method and co-authored the first reference textbook on the topic of multiple-point geostatistics. Since 2016 he is co-editor-in-chief of the journal Computers & Geosciences.
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University Pierre et Marie Curie, Paris, France, 23 February 2018
Title: Data expansion: using analogues to improve remote sensing and climate datasets
Shanxi University, School of Computer and Information Technology, China, 2-5 February 2018
Title: New statistical models for representing spatial and temporal variability
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China, 6 February 2018
Title: Multiple-point statistics: how they work, when to use them, or not to use them
Indian Statistical Institute, Bangalore, India, 1 June 2018
Title: Data expansion: using analogues to improve remote sensing and climate datasets
Kyoto University, Japan, 7 June 2018
Title: Enhancing Earth observation datasets using multiple-point geostatistics
geoENV 2018 conference in Belfast, UK, keynote speech, 4-5 July 2018
Title: Multiple-point geostatistics applications for Earth Observation
IAMG 2018 conference in Olomouc, Czech Republic, keynote speech, 2-8 September 2018
Title: Multiple-point geostatistics: when do they work, when do they not work
2017 IAMG Distinguished Lecturer – Clayton V. Deutsch
Dr. Deutsch is a Professor in the School of Mining and Petroleum Engineering at the University of Alberta. He teaches and conducts research into better ways to model heterogeneity and uncertainty in petroleum reservoirs and mineral deposits. Prior to joining the University of Alberta, Dr. Deutsch was an Associate Professor (Research) in the Department of Petroleum Engineering at Stanford University, a researcher at Exxon Production Research Company and a geostatistician with Placer Dome Inc. He has published eight books and over 250 research papers. Dr. Deutsch holds the Alberta Chamber of Resources Industry Chair in Mining Engineering and the Canada Research Chair in Natural Resources Uncertainty Characterization.
Contact by email at firstname.lastname@example.org
All Realizations All the Time
Sparse sampling combined with geological heterogeneity at all scales leads to inevitable uncertainty. The quantification of joint uncertainty in high dimensional spatial problems requires multiple realizations. Managing all realizations through decision making is problematic. Theory, implementation details and the practice of creating and managing realizations will be shown. Active uncertainty management versus passive observation of uncertainty will be emphasized.
Geometallurgy from a Geostatistical Perspective
High-resolution spatial numerical models of metallurgical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are reviewed and illustrated for geometallurgical modeling. Important considerations include the data scale, non-linear averaging, incomplete and censored sampling, and the appropriate modeling workflow. Theory, implementation and glimpses of case studies will be shown.
Millimeter to Kilometer Scale Reservoir Modeling
Geostatistical models of reservoir rock are constructed at many scales for different purposes. Millimeter scale models are used with high resolution image logs for improved permeability prediction. Kilometer scale models are created for regional exploration. There are many models in between. The techniques and practice of constructing different models for different purposes will be reviewed with examples.
Developments in Multivariate Geostatistics
Simultaneous modeling of 10s to 100s of related properties is required in many geostatistical studies. Novel developments in understanding multivariate dependencies and spatial prediction of many dependent variables will be presented. A taxonomy of multivariate techniques and workflows are presented to show the breadth and variety required for modern geostatistical modeling. Examples will be shown.
Dr. Deutsch has taught nearly 200 short courses internationally. Popular courses include:
Fundamentals of Geostatistics (2 to 4 days)
Mineral Resource Estimation (3 to 5 days)
Geostatistical Reservoir Modeling (3 to 5 days)
Advanced Multivariate Geostatistics (3 to 5 days)
2016 IAMG Distinguished Lecturer – Sean A. McKenna
IBM Research – Ireland
McKenna is Senior Research Manager at the IBM Smarter Cities Technology Centre (SCTC) in Dublin, Ireland. Sean’s particular research interests are centered on inverse parameter estimation of heterogeneous spatial processes, estimation and anomaly detection in spatio-temporal data sets and information extraction from networked sensors. These interests are being applied to improving the efficiency and sustainability in areas of natural resources management and urban infrastructure systems.
Prior to joining IBM Research in 2012, Sean was a Senior Scientist in the Geoscience Research and Applications Group, Sandia National Laboratories where he worked on fluid flow and solute transport in heterogeneous porous media and fractured rock systems, and also led development of the open-source water quality event detection software, CANARY. He also spent two years as Sandia Representative in Singapore.
Sean was Chair of the IAMG Distinguished Lecturer Committee from 2005 to 2012 and is currently on the Editorial Board of Mathematical Geosciences.
1• Simulation of Ground Water Flow in Heterogeneous Media
Hydraulic conductivity in heterogeneous and fractured media can range over many orders of magnitude. Creating numerical representations of this highly variable material property using combinations of discrete and continuous geostatistical simulations along with object-based techniques allows for a rich set of capabilities for creating alternative conceptual models. Forward modeling of ground water flow through these representations in a Monte Carlo framework provides characterization of uncertainty in downstream performance measures. Inverse parameter estimation is used to condition these hydraulic conductivity models to observations of state variables (pressures, concentrations, travel times). This presentation will describe development and applications of these techniques drawn from probabilistic risk assessment, effective property calculation, long-term geologic disposal of nuclear waste and geo-thermal resource development.
2• Hidden Markov Models in Environmental and Geoscience Applications.
Observations of geologic and environmental variables are often indirect measures that reflect an unknown hidden state. Hidden Markov models (HMM’s) provide a framework for learning and interpreting the hidden states through indirect measurements. These techniques exploit spatial correlation as defined in a Markovian process and can improve decisions made relative to models that do not account for spatial correlation. This presentation examines utilization of HMM’s in an environmental application using observations from magnetometers collected with strip transect support to identify regions of increased magnetic anomaly intensity. This presentation also updates the classic mathematical geosciences problem of patterned search for resource targets to now use strip transect samples and HMM’s. Further developments of using HMM’s with exhaustive 2D observations are proposed and explored.
3• Detecting Significance in Spatially Correlated Processes.
In statistical hypothesis testing, the multiple comparison problem arises when a set of statistical inferences are considered simultaneously, and naïve treatment of these multiple comparisons can return incorrect and misleading results. Within the earth and environmental sciences, correlation across space in both material properties and natural processes often leads to the multiple comparison problem. More recent developments for determining significance across spatially correlated results exploit properties of Gaussian random fields and are examined here. Application to improving understanding of the relationships between vegetation dynamics and the El Nino Southern Oscillation (ENSO) anomalies will be presented. The extremely large 2015-2016 El Nino provides timely motivation to focus on identifying statistically significant responses of vegetation patterns to ENSO. Assigning statistical significance to satellite observations of vegetation response to ENSO has traditionally been done without consideration of the spatial correlation between locations. In this presentation, those approaches are compared to a more rigorous approach that accounts for the spatial correlation within the satellite images.
General Audience – Non Technical
• Smarter Planet 2.0
The Smarter Planet initiative began in 2008 as an effort to take advantage of increasingly instrumented and interconnected systems for more efficient and sustainable use of resources. Since then, significant gains have been made towards these goals, but considerable challenges remain. This presentation will explore progress to date in applying Big Data and analytic tools to improved operation of infrastructure systems and resource allocation with a focus on water and energy. Current research on improved predictive models and optimization approaches to make better decisions and improve sustainability will be covered.
For more information, or to schedule a visit contact Sean at
2015 IAMG Distinguished Lecturer – Gordon Kaufman
Gordon Kaufman is the Morris A. Adelman Professor of Management, Emeritus and a Professor of Statistics at the MIT Sloan School of Management.
Prof. Kaufman is a petroleum industry expert. His research focuses on primary energy resources, with particular attention to the process of discovering oil and gas. He has a long-standing interest in Bayesian econometrics and multivariate analysis as well as in risk analysis of complex strategic problems. Kaufman’s current research interest is how to appraise uncertainties within large systems whose components are logically related in complex ways—such as global climate change models and their impacts, nuclear reactor fault trees, and Bayesian networks—when experts provide incomplete information about these uncertainties. Kaufman has had a long association with the IAMG. Some of Professor Kaufman’s most significant work has been in the area of resource estimation and discovery modelling, as indicated in his publications in IAMG journals (Mathematical Geosciences and Natural Resources research) as well as journals of related professional organizations including the AAPG Bulletin.
I am prepared to give talks on a number of rather different topics:
• “Petroleum Assessment via Hierarchical Modeling”. Ray Faith, Jack Schuenemeyer and I project aggregate gas hydrates in place in three major onshore Alaska assessment units using Bayesian hierarchical modeling
• “Prospect Information, Adaptive Successive Sampling and Oil and Gas Discovery Modeling” broadens traditional discovery process models to include both spatial probabilistic dependencies and adaptive updating of projections of returns to drilling as drilling information accrues.
• “A Generalization of Intra-class Correlation Matrices”. This generalization arises in assessment of the oil and gas potential of the Circum-Arctic — a large unexplored region constituting approximately 6% of the Earth’s surface area — and in assessment of CO2 sequestration capacity in depleted US natural gas reservoirs. We study properties of this class of matrices and derive exact upper and lower bounds on allowable background correlations and propose simple checks that guarantee coherence of the assessed correlation matrix. (This is a technically more advanced topic suitable for a mathematically oriented audience).
Contact InformationOffice: E62-437 Sloan School of Management MI
Cambridge, MA 02142Tel: (617) 253-2651Fax: (617) 258-7579E-mail: email@example.com
2014 IAMG Distinguished Lecturer – Eric Grunsky
Dr. Eric Grunsky is a research scientist at the Geological Survey of Canada (GSC), Natural Resources Canada, Ottawa, Ontario. Since 2002, he has worked at the GSC carrying out research in the discovery of geochemical and geological processes from the evaluation of geochemical survey data. Dr. Grunsky makes use of multivariate statistical methods and spatial statistics as the basis of his research. His research has resulted in a procedural approach for processing and interpreting geochemical data. Geochemical data is first explored for “Process Discovery” from which models and hypotheses of geological/geochemical processescan be constructed. Models and hypotheses are constructed then tested by integrating the geochemical data with existing geological/environmental/climatic knowledge within a spatial context. Testing these models is defined as “Process Validation” from which a quantitative probabilistic framework can be constructed within a spatial context. This approach is based on the implicit acceptance of the compositional nature of the data along with the application of multivariate statistical methods to “discover” and “validate” geochemical data.
In 2005, he received the Felix Chayes Medal for Excellence in Research in Statistical Petrology, by the International Association for Mathematical Geosciences (IAMG) and served as Editor-in-Chief for the International Journal, Computers & Geosciences from 2006-2011. In 2012, he was awarded the IAMG Krumbein Medal, the highest award given by the IAMG to senior scientists for career achievement.
Eric has prepared a presentation on the statistical interpretation of geochemical data along with a short course that outlines a sequential approach for processing and interpreting geochemical data collected from surveys:
1. The use of geochemical survey data for predictive geologic mapping at regional and continental scales
2. Short Course: The Interpretation of Geochemical Survey Data
• Compositional Data Analysis
• Discussion on mineral stoichiometry and its effect on compositional data
• Censored Data & Replacement Values
• Levelling Geochemical Datasets
• Spatial Sampling Schemes
• Characterizing Geochemical Data – visual and numerical descriptions
• Multivariate Statistics – discovery and validation
• Classification and Predictive Geochemical Mapping
• Mineral Resource Prediction
• Case Studies from Canada, Australia, United States, Mexico, Indonesia
Email: egrunsky [at] gmail.com
2013 IAMG Distinguished Lecturer – Pierre Goovaerts
Dr. Pierre Goovaerts is chief scientist at Biomedware in Ann Arbor, MI and has 20 years experience in the development and implementation of geostatistical algorithms. He has authored 130 papers in the field of geostatistics and is the author of the 1997 textbook “Geostatistics for Natural Resources Evaluation”.
In the past 8 years, his research has focused on the development and implementation of geostatistical methods for the analysis of aggregated and individual-level health data, including the accurate mapping of rates of cancer incidence and mortality, the space-time analysis and detection of health disparities, as well as the incorporation of rate uncertainty into boundary detection and local cluster analysis. Recently, he developed a methodology to combine both point and areal data in spatial interpolation, with an application to the mapping of the risk of breast cancer late-stage diagnosis across Michigan. He is also exploring the impact of socio-demographic and environmental risk factors on the incidence and late-stage diagnosis of breast and prostate cancers.
Dr. Goovaerts can be contacted at firstname.lastname@example.org or through the chair of the Lecture Committee, Jennifer McKinley, at email@example.com.
Topics of lectures offered
Geostatistics in Practice
Geostatistics provides a set of statistical tools for the analysis of data distributed in space and time. Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, environmental studies, and more recently exposure assessment. This lecture will present a richly illustrated overview of the main steps and outputs of a geostatistical analysis. Examples include the mapping of soil heavy metal concentrations, the prediction of groundwater arsenic concentrations, the computation of volumes of contaminated sediments, the mapping of prostate and breast cancer risks, and the modelling of the spatial density of wild animals in a national park.
Combining Areal & Point Data in Geostatistical Interpolation: Applications to Soil Science & Medical Geography
A common issue in spatial interpolation is the combination of data measured over different spatial supports. For example, in the field of medical geography information available for mapping disease risk typically includes point data (e.g. patients residence) and aggregated data (e.g. socio-demographic and economic data at the census track level). Similarly, soil measurements recorded at discrete locations on the ground are often supplemented with choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This lecture presents a coherent geostatistical approach to accommodate both areal and point data in the spatial interpolation of continuous attributes, with applications to soil science and medical geography.
The Role of Geostatistics in Medical Geology
Medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This lecture provides an overview of geostatistical methods available for the analysis of geological and health data, with a focus on the issue of error propagation, that is how the uncertainty in input data (e.g. arsenic concentrations) translates into uncertainty about model outputs (e.g. risk of bladder or prostate cancer). Methods for uncertainty propagation, such as Monte-Carlo analysis, are critical for estimating uncertainties associated with spatially-based policies in the area of environmental health, and in dealing effectively with risks.
Geostatistical Mapping of Dioxin and Arsenic in Soils around Point Sources of Contamination
Deposition of pollutants around point sources of contamination, such as incinerators or smelters, can display complex spatial patterns depending on prevailing weather conditions, the local topography and the characteristics of the source. Deterministic dispersion models often fail to capture the complexity observed in the field, resulting in uncertain predictions that might hamper subsequent decision-making, such as delineation of areas targeted for additional sampling or remediation. This lecture describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition with the probabilistic modeling of residual field variability. The approach is used to delineate areas with high level of dioxin TEQ (Toxic Equivalents) around an incinerator, as well as to identify residential parcels for additional sampling and cleaning in the case of arsenic contamination caused by a smelter.
2012 IAMG Distinguished Lecturer – John H (Jack) Schuenemeyer
John H (Jack) Schuenemeyer, the International Association for Mathematical Geosciences (IAMG) Distinguished Lecturer (DL) for 2012, is president of Southwest Statistical Consulting, LLC, and Professor Emeritus of Statistics, Geology and Geography, University of Delaware.He has been a statistical consultant for over 30 years specializing in earth science applications. Jack is an elected Fellow of the American Statistical Association (ASA) and a member of its Committee on Energy Statistics. He is the recipient of the IAMG 2004 John Cedric Griffiths Teaching Award. Jack is a member of the Committee on Resource Evaluation for the American Association of Petroleum Geologists. His current work in earth science activities includes developing statistical methodology for gas hydrate assessment, hierarchical modeling applied to oil and gas resource estimation models, aggregation methodology, and use of analogs for petroleum assessment. Jack’s interests in statistics include modeling, graphics, expert judgment, classification, spatial statistics, and statistical computing. He has been a leader in university-based statistical consulting and education. Jack has authored over 100 research publications, given numerous invited talks, and conducted workshops for scientists from industry and government. His bookStatistics for Earth and Environmental Scientists, coauthored with Dr. L.J. Drew, was published by John Wiley in 2011. Additional information on Dr. Schuenemeyer can be found atwww.swstatconsult.com.
Dr. Schuenemeyer has prepared the following selection of talks suitable for a variety of earth science audiences and technical levels. If there are other topics of interest, please contact him to discuss possibilities.
Those interested in having Dr. Schuenemeyer visit should contact the DL Committee Chairman, Sean McKenna, at firstname.lastname@example.org Dr. Schuenemeyer directly at email@example.com.
1. Gas-hydrate modeling. There is evidence of significant in-place gas hydrate resources in off-shore areas of the world. A mass-balance cell-based model has been developed using stochastic simulation to obtain estimates of available resources. The principal modules are generation, charge, thickness of the hydrate stability zone, concentration, and volume.
2. Aggregation, analogs, dependency and expert judgment. Generally, assessment of resources – oil, gas, minerals, and storage capacity for CO2occur at a low level of aggregation. Frequently, assessors are required to aggregate results to a higher level such as a basin or region. In frontier areas most information comes from expert judgment often based upon analogs. Understanding sources and minimizing bias is critical. Choices made in estimating dependence are a major factor in estimation of uncertainty intervals.
3. Analyzing multivariate geochemical data. The use of univariate, multivariate, and graphical procedures are illustrated using a geochemical data set. This study involves clustering, classification, and common factors. Transformations and outlier detection are also important considerations.
4. Statistical analysis in the earth sciences using R – a language and environment for statistical computing and graphics. Numerous routines are available for general data analysis and earth science applications. Graphic, univariate, multivariate, time series and spatial applications will be presented. This is also a short course.
2011-2012 IAMG Distinguished Lecturer – Amilcar Soares
Amilcar Soares, the Distinguished Lecturer for 2011-2012, is a Professor at the Instituto Superior Técnico in Portugal and is also head of the Centro de Modelização de Reservatórios Petrolíferos at IST. He has been extremely active in promoting mathematical geology, worldwide, particularly geostatistics, since the 1980’s. Soares is one of the world leaders in applying geostatistics in environmental engineering with recent work on characterizing desertification and he is making considerable impact on applications to practical problems, not just in theoretical developments. Amilcar has also organized one of the Geostat Congresses and two of the geoENV conferences and has been an IAMG member for many years.
More information can be found on the web pages of Centro de Modelização de Reservatórios Petrolíferos: http://cmrp.ist.utl.pt/
Anyone interested in hosting Dr. Soares at their institution, please contact the Chairman of the DL Committee, Sean McKenna, at: firstname.lastname@example.org
Lecture #1 –Introduction to geostatistics for environmental applications and natural resources evaluation: Basic concepts and examples.
The monitoring and management of environmental projects of soil contamination and soil degradation, air pollution in urban and industrial sites, surface water and groundwater contamination usually share common problems and challenges related with the complexity of natural phenomena involved, as well as a general scarcity of available information. Geostatistical methodologies have been widely employed in attempting to tackle these problems, namely with regard to natural resource evaluation, development of sampling strategies, characterizing hot-spots or time periods with high pollutant concentrations, and management of the uncertainties and risks of different phases of the project.
This presentation will provide the non-specialists with the basic geostatistical concepts behind the use of stochastic simulations in the assessment of uncertainty and risk.
These concepts are illustrated with a large set of real case studies in environmental and natural resource evaluation and management: contaminated sites, soil degradation, water contamination, air quality, climatic variables, forests, and mineral resources.
Lecture #2 – Monitoring and control of desertification and drought phenomena by using geostatistical methods with Earth Observation data.
Desertification is a phenomenon that is affecting two-thirds of the planet. Most of the affected areas belong, geographically and politically, to developing countries with almost no resources to combat its effects. It is extremely important to identify local or regional desertification indicators in order to observe, control and manage the critical situations. However, because local resources are generally not available for their observation and analysis these indicators become worthless as a tool to combat desertification.
Hence Earth Observation data (primarily no-cost satellite images) are a fundamental tool for monitoring the dynamics of desertification phenomena in order to manage critical situations in those countries.
In this presentation, Geostatistical and image analysis classification methods used to characterize the spatial and temporal behavior of individual biophysical factors of desertification (climate, vegetation, water and soil) are discussed. Based on the dynamics of extreme climatic factors (droughts and floods) and the behavior of vegetation and soil through time for the crucial land use classes, joint geostatistical interpolation methods produce regional indicators of desertification. Real case studies of selected semi-arid regions in Portugal, Mozambique and Brasil are presented in order to illustrate those methodologies. This is the kernel of DESERTWATCH (Extended), a project supported by the European Space Agency, which is a tool to characterize desertification indicators at the global scale.
In the second part of this talk, stochastic simulations of the main spatial and space-time patterns of drought phenomena, which are affecting most of the occidental Mediterranean region, are presented as a tool for the assessment of high-risk areas in response to extreme climatic phenomena, as well as for the management of the main water basins.
Lecture #3 –Joint use of geostatistics and deterministic models to integrate the dynamic characteristics of physical phenomena in environmental applications .
Geostatistics has been used over the last decades as a tool for pollutant characterization and uncertainty assessment in a wide range of environmental applications, like polluted soil sites, air quality, surface water and groundwater contamination, (geoENV conferences proceedings). Geostatistical models basically share an identical framework, i.e., assuming the variables of interest as spatial, or space-time, stationary random functions. Measures of uncertainty and mean behavior of those variables are determined through local or global conditional distribution functions (cdfs) by using estimation (indicator or multiGaussian kriging) or stochastic simulation algorithms. Cost functions and risk analysis are then derived from these local or global cdfs.
However, more complex phenomena cannot be satisfactorily represented with those common approaches, mostly when they present a determinant dynamic component that is hard to directly account for in geostatistical models as in, for example, the flow of contaminants at the surface, in different soil layers or in deep aquifers, or the dynamics of contaminant plumes in air in industrial or urban areas. Usually, in order to integrate the influence of such dynamic factors, deterministic dynamic simulators are used separately in inverse modeling approaches, such as in groundwater problems, to “calibrate” and update parameters that have been characterized by geostatistical techniques.
This presentation addresses some new proposals to approach such problems. The integration of main dynamic characteristics through hybrid models is presented. This presentation is guided by two real case studies: i) The main vectors of a fluid flow (given by a dynamic simulator), are used to characterize local anisotropy behavior of the phenomenon in the stochastic simulation of the main sediment pollutants in a coastal lagoon (Horta et al, 2010); ii) The results of a Gaussian plume simulation of air pollution emissions of an industrial area are downscaled with multi-scale simulation (Liu and Journel, 2008) by using local point data of monitoring stations and block data from the deterministic model (Pereira, et al., 2010 ). This algorithm shows to be a nice solution for mitigating the non-exact and coarse scale results of deterministic Gaussian plume simulations, which is a very common practice in air quality characterization.
Lecture #4 – : New methods of stochastic seismic inversion in Petroleum Applications of Geostatistics
Geostatistics has been commonly used in forward modeling and in inverse modeling to integrate seismic information in stochastic fine grid models. The quality of seismic data, the downscaling of seismic attributes to the fine grid of the well measurements are still valuable challenges to which existing geostatistical methods only give partial answers.
Seismic inversion is an established geophysical technique whereby the rock property of acoustic impedance is estimated directly from seismic amplitude data. Seismic inversion is based on an iterative approach to minimize an objective function – the difference between the forward convolution of the reflectivity of an impedance model and the real seismic amplitudes.
In this presentation a new seismic inversion methodology – global stochastic inversion GSI – is proposed based on i) direct sequential simulation and co-simulation approaches for “transforming” 3D images of acoustic impedances in an iterative process, and ii) following the sequential procedure of the genetic algorithms optimization to converge the transformed images towards an objective function.
This is presented in two different situations of seismic inversion: acoustic inversion, where the GSI is applied to a post stack seismic signal; and elastic inversion where the stochastic inversion is applied to a pre-stack seismic. In this last framework, a new method for reproducing the bi-distribution of acoustic impedances Ip and Is is presented. In both acoustic and elastic inversion approaches of GSI, maps of final porosity and corresponding uncertainty are obtained.
Case studies of Brasilian, African and Middle East reservoirs are presented.
2009-2010 IAMG Distinguished Lecturer – Roussos Dimitrakopoulos
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.
Institutions interested in having Prof. Dimitrakopoulos visit should contact the DL Committee Chairman, Sean McKenna, at email@example.com
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.
2008 IAMG Distinguished Lecturer – Prof. Donald E. Myers
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.
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.
Distinguished Lecturer 2007 – Prof. Dr. Vera Pawlowsky-Glahn
Department of Computer Science and Applied Mathematics
University of Girona, Spain
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.
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.
IAMG Distinguished Lecturer 2005 – Dr. Lawrence J. Drew
Dr. Lawrence J. Drewof 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 (firstname.lastname@example.org) or to contact Sean McKenna, chair of the DL Committee, at (email@example.com)
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.
IAMG Distinguished Lecturer 2004 – Dr. Frederik P. Agterberg
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.
2002 IAMG Distinguished Lecturer – Dr. John C. Davis (Kansas Geological Survey)
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 (firstname.lastname@example.org). 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.