Preliminary Program

Preliminary Program Overview

Preliminary program of IAMG2019
Date Time Event
Saturday, 8/10/2019

9:00 am
9:00 am - 5:00 pm

Registration begins
Short courses with lunch buffet
Sunday, 8/11/2019 9:00 am
9:00 am - 5:00 pm
6:00 - 8:30 pm
Registration begins
Short courses with lunch buffet
Ice-break reception
Monday, 8/12/2019
8:00 am
8:30 am
9:00 - 10:30 am
10:30 - 11:00 am
11:00 - 12:30 pm
12:30 - 1:30 pm
1:30 - 3:00 pm
3:00 - 3:30 pm
3:30 - 6:00 pm
Registration begins
Conference Inauguration
Plenary session
Coffee break
Parallel sessions
Lunch buffet
Poster session
Coffee break
Parallel sessions
Tuesday, 8/13/2019 8:00 am
8:30 am- 10:00 am
10:00 - 10:30 am
10:30 - 12:30 pm
12:30 - 1:30 pm
1:30 - 3:00 pm
3:00 - 3:30 pm
3:30 - 6:00 pm
7: 00 - 9:00 pm
Registration begins
Plenary session
Coffee break
Parallel sessions
Lunch buffet
Poster session
Coffee break
Parallel sessions
Dinner/ Awards Presentation at Nittany Lion Inn
Wednesday, 8/14/2019 &
Thursday, 8/15/2019
8:00 am
8:30 - 9:30 am
9:30 - 10:00 am
10:00 - 12:30 pm
12:30 - 1:30 pm
1:30 - 3:00 pm
3:00 - 3:30 pm
3:30 - 6:00 pm
Registration begins
General assembly (W) / Plenary session (Th)
Coffee break
Parallel sessions
Lunch buffet
Plenary session (W) / Parallel sessions (Th)
Coffee break
Parallel sessions
Friday, 8/16/2019 8:30 am - 6:00 pm Field trips (optional)

Keynote Speakers

Susan M. Agar

Dr. Susan Agar is a leader for innovations in geoscience at the Aramco Global Research Center in Houston. In this role, she steers research for subsurface technologies destined to support geoscientists of the future in Saudi Aramco. During 20-plus years in industry, she has contributed through various research leadership and senior advisory roles in leading global energy companies, pursuing research, teaching and consulting in over 25 countries. Before joining Aramco in 2014, she defined and led the ExxonMobil (FC)2 Alliance, a multi-national, multi-disciplinary academic-industry alliance involving 160 researchers and industry professional in 14 universities in N. America and Europe. She also led initiatives related to emerging and disruptive technologies and evaluations of the global gas endowment as represented in the ExxonMobil Energy Outlook. Prior to her industry career, Dr. Agar was a tenured faculty member at Northwestern University and a visiting faculty member at Stanford University. She holds a Ph.D. from Imperial College, an MBA degree from the Wharton School at the University of Pennsylvania. In 2016, she received an Honorary Doctorate of Science from Heriot Watt University in recognition for her outstanding contributions to reservoir engineering and petroleum geoscience and was recently a finalist for the “Innovative Thinker” at the World Oil Awards.

Talk title: Driving Rapid Transformations in the Energy Industry: the Convergence of Emerging Technologies and Mathematical Geoscience

The ongoing transformation of the energy industry is responding to multiple factors, including growing energy demand with population growth, the growth of unconventional and renewable energy resources, hybrid energy systems and the mitigation of influences on climate change. Beyond widespread and fast-moving digital transitions, the industry is adopting new operational practices and rapidly translating technologies from other industries to meet energy needs. In this context, the convergence of emerging technologies with mathematical geoscience has opened new research avenues, not only for the energy industry but for many areas of natural resource and environmental management. This evolution is hybridizing skill sets used by geoscientists, creating new partnerships and changing organizations. New paradigms are strengthening industry adoption of technologies and research advances related to mathematical geoscience

Peter Filzmoser

Peter Filzmoser is full professor at the statistics department of the Vienna University of Technology. His main research interests include robust statistics, methods for compositional data analysis, statistical computing with R, and many more. He is author of the books: "Applied Compositional Data Analysis. With Worked Examples in R (Springer 2018), "Introduction to Multivariate Statistical Analysis in Chemometrics" (CRC Press, 2009), and "Statistical Data Analysis Explained. Applied Environmental statistics with R (Wiley, 2008). Currently, he is head of the Research Unit in Computational Statistics, Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology.

Talk title: Outliers and compositional data

Statistical data analysis should always be done with care if outliers are present in the data, since they have the potential to spoil the analysis. However, usually it is not clear if multivariate data contain outliers, and in particular, if such outliers would affect the statistical method to be used. Diagnostic plots of the results from the analysis will only reveal outliers if the method itself is robust against the outliers. Moreover, the impact of outliers depends on the statistical model being used. Identifying outliers in compositional data is even more tricky because their values are unusual not in the absolute but in a relative sense. With the log-ratio approach for compositional data analysis, outliers could even be artificially created by including variables with extremely low and unreliable values - a frequent practical issue. We will discuss these problems and provide more detailed insight, propose some possible approaches to cope with these issues, and illustrate them at real data, mainly from the field of geochemistry.

Alessandra Menafoglio

Alessandra Menafoglio is an Assistant Professor in Statistics at the Department of Mathematics of the Politecnico di Milano, within the laboratory for modeling and scientific computing (MOX). Her research interests focus on the study of innovative statistical models and methods for the analysis of complex and large data (such as curves and images), with particular emphasis on spatially distributed complex data. In the last years, she focused on the problems of modeling, prediction (kriging) and stochastic simulation for very general types of data, in the context of Object Oriented Spatial Statistics. More recently, she also investigated computational intensive methods to address the issues arising when the data objects are distributed over irregularly shaped and textured regions. Alessandra Menafoglio obtained her PhD in Mathematical Models and Methods in Engineering in 2015 at Politecnico di Milano. Her doctoral thesis was awarded in 2016 with the "Eni Award, Debut in Research Prize". She is first author of the 2016 Editor Choice Award winner of the journal Water Resources Research. In 2018, she was awarded the "2018 Young Statistician Award" given by the ENBIS Scientific Society (European Network for Business and Industrial Statistics).

Talk title: Object Oriented Spatial Statistics: an approach to the analysis of georeferenced complex data

The analysis of complex data distributed over large or highly textured regions poses new challenges for spatial statistics. Object Oriented Spatial Statistics (O2S2) is a recent system of ideas and methods that allows to analyze high dimensional and complex data when their spatial dependence is an important issue. We present the key concepts of O2S2, as a general approach to analyze and predict georeferenced complex data, interpreted as objects in appropriate mathematical spaces. Examples of object data include functional data, distributional data or tensors. We discuss the extension of key geostatistical concepts (e.g., stationarity) and methods (e.g., Kriging) to the context of O2S2, and discuss recent extensions of these methods that permit the the analysis of object data distributed over complex regions. Here, we shall ground our developments on computational intensive methods, based on random domain decompositions of the study domain. The presented models and methods will be illustrated through real environmental case studies. (joint work with P. Secchi)

Vera Pawlowsky-Glahn

Dr. Vera Pawlowsky-Glahn studied Mathematics at the University of Barcelona, Spain, and received her PhD (doctor rerum naturam) in 1986 from the Free University of Berlin, Germany. She has been titular professor at the School of Civil Engineering of the Technical University of Catalonia (UPC) in Barcelona, Spain (1986-2000), and full professor of Statistics at the Department of Computer Science, Applied Mathematics, and Statistics of the University of Girona, Spain (2001-2018). Since October 2018 she is emeritus professor of Statistics at the University of Girona, Spain. Her main research topic is, since 1982, the statistical analysis of compositional data (CoDa) both in the spatial and non-spatial case. She developed together with Dr. J.J. Egozcue the sample space structure to CoDa. The impact of the methodology has grown exponentially in the last years, as CoDa are present in all applied sciences. She has over 120 publications and 3 books related to this topic. Her research group on compositional data analysis involves professors from different Spanish universities. They started a workshop series, known as CoDaWork, which 8th edition will take place in 2019 in Terrassa (Spain). Her research has received regularly financial support from the Spanish Ministry for Education and Science. Vera has been vice-chancellor at UPC 1990-1994, head of the Department of Computer Science and Applied Mathematics at the University of Girona 2004-2005, and dean of the Graduate School of the University of Girona 2005−06. The International Association for Mathematical Geosciences (IAMG) has been an important professional society in her career, supporting and promoting her research. She received from IAMG the William Christian Krumbein Medal in 2006, she was Distinguished Lecturer in 2007, and received the J.C. Griffiths Teaching Award in 2008. She was the 2008−2012 President and the 2012−2016 Past-President of IAMG. She was the founding President of the Association for Compositional Data (L’Escala (Spain), 2015), and is the 2017-2021 Past-President of the same association. She has been elected the 2019 IAMG Matheron Lecturer.

Talk title: Compositional data in geostatistics

Problems with compositional data, like spurious correlation and negative bias, are well known in the Geosciences. Not so well known is the fact that the same problems appear when dealing with regionalized compositions. These problems are illustrated, and a solution is presented that is based on the principle of working in coordinates using isometric logratio representations. The proposed approach offers not only a tool for standard geostatistical studies, but also a way of modelling crossvariograms based on the matrix valued variation variogram. Moreover, it allows to overcome usual inconsistencies with indicator kriging through simplicial indicator kriging. One concern about the proposed solutions is that available compositional methods lead to a representation of results in proportions, while often practical applications require results in the original units-non-closed forms. In order to solve such a problem, specific tools to recover original units have been studied. In summary, the main aspects related to the modelling and analysis of regionalized compositions have found satisfactory solutions.

Phillipe Renard

Dr. 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 Paris School of Mines in 1996. His research focuses on stochastic hydrogeology and aquifer. In geostatistics, he has developed multiple- point statistics methods and their applications to a wide range of problems from 3-D geological modeling to the simulation of climate variables. Renard has been the editor of Hydrogeology Journal, president of the geoENVia association and manages the Hydrogeologist Time Capsule.

Talk title: Karst aquifer modeling, state of the art and challenges

Karstic aquifers are characterized by the presence of rare but highly permeable karstic conduits embedded in a carbonate matrix of lower permeability. This highly heterogeneous structure results from the dissolution of the matrix by acidic water and a self-reinforcing process. Karst aquifers can present very fast flow and contaminant transfer in the conduits. Consequently, these aquifers are often highly vulnerable to groundwater pollution and extremely sensitive to climate fluctuations. In recent years, significant progresses have been made to model karstic reservoirs. In this presentation, we will discuss several of these modeling aspects, including techniques that can be used to simulate the geometry of karstic networks (often only partially known), flow and transport simulation methods, but also the speleogenesis processes and in particular the role of ocean tides in the case of coastal aquifers.

Wenlei Wang

Wenlei Wang is an associate professor in the field of Mineral Resources Quantitative Assessment and Geo-information Analytical Methodology at the Institute of Geomechanics, Chinese Academy of Geological Sciences. He obtained his PhD at York University, Canada in 2013. After that, he came back to China and worked in China Geological Survey. With his geology background and skills of spatial information technology, his records include: proposing “fault trace-oriented” and anisotropic singularity index estimation algorithms, developing “geologically constrained” multi-source data integration model and introducing geographic regression analysis (GWR) model to mineral prediction. He was funded by the grant of Chinese National Natural Science Foundation-Outstanding Youth Foundation in 2018. Since 2019, he is the editorial board member of the journal of Applied Computing and Geosciences.

Talk title: Geo-information extraction and integration in support of mineral exploration

Mineral exploration according to methods of mathematical geosciences generally includes mineralization related geo-information extraction and integration. In this talk, singularity-based algorithms and applied research are discussed for geological feature recognition and mineral potential mapping by the processing of geophysical, geochemical and geological data with the use of novel GIS and computer-based techniques. Singularity theory initially proposed in former studies by Dr. Qiuming Cheng several years ago is helpful for identifying target areas in mineral exploration and the study of regional geochemical pollution patterns. The frequently applied square window-based methods are limited to investigate anisotropy of mineralization related anomalies. This study further developed the analytical algorithm of singularity theory, and proposed “Geological Window”-based singularity algorithms. Compared with other singularity estimation methods, the new technique objectively identifies anisotropic geochemical features and investigates the non-linear geochemical behaviors. Geographic regression analysis (GWR) model well practiced in social science was mainly utilized to characterized spatial non-stationary relations among social factors, e.g., between housing price and density of population, education level, traffic facility, etc. GWR model is currently applied to depict non-stationary influences of tectono-magmatic processes on ore material accumulation in spatial scenario. Examining spatial distributions of regression coefficients, it can be inferred that mineralization at different locations was caused by interactivities of multiple geo-processes to different degrees. This applied research which helps to improve understanding of local metallogeny can be a useful reference to future regression analysis on geological issues.

News

Long abstract submission extended to May 6

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Sponsors

Aramco Services Company

Chevron

Penn State Institute for CyberScience

USGS

EMS Energy Insitute, Penn State University

Penn State Institute of Energy and the Environment