Short Courses

Short Courses

The IAMG Organising Committee invite you to participate in one the many Short Courses on offer at The International Association for Mathematical Geosciences Conference. 

These Short Courses provide intensive training to help advance your career, update your skills and knowledge, or meet specific legislative requirements.

More information on Short Courses dates, time, location and cost will be available on Wednesday 1 March 2017. To keep up to date with announcements register your interest now!

The Short Courses that have been confirmed are as follows:

Short Course 1: Introduction to compositional data analysis

Compositional data are vectors showing the relative importance of the parts of a whole. Typical examples are data in percentages, ppm, ppb, molar composition, or the like, common in many fields of science, particularly in the geosciences. The classical statistical analysis of this data type suffers from many problems, including spurious correlation. As a solution to these problems, J. Aitchison introduced the log-ratio approach in the eighties. Since then, progress has been made in understanding the geometry of the sample space, the D part simplex. This course provides an introduction to the theory and practice of statistical analysis of compositional data aspects, as well as a forum for informal discussion on more advanced topics. It also provides an introduction to available software, which includes Codapack and some packages in R. Codapack is freeware (http://imae.udg.edu/codapack/) specifically developed for the statistical analysis of compositional data. It is an easy to use tool for elementary exploratory analysis and is used in most course activities. For the treatment of zeros the R package zCompositions is used, and for some more demanding statistical techniques, other R packages (compositions, robCompositions) are introduced. 

Date: Monday 28 August – Friday 1 September 2017
Time: 9:00am – 6:00pm
Venue: CSIRO
Cost: A$500
Registration: Opens Wednesday 1 March 2017

Course Outline:

  1. Hypothesis underlying any statistical analysis (sample space, scale operations).
  2. The Aitchison geometry of the simplex.
  3. Representation in coordinates; distributions in the simplex.
  4. Exploratory analysis (centre, variation matrix, biplot, dendrogram, balances).
  5. Irregular data: zeroes, outliers, missing data.
  6. Random compositions. Normal distribution in the simplex.
  7. Methods of multivariate analysis with compositional data: regression, MANOVA, discriminant analysis and cluster analysis.
  8. Compositional processes.

Course Schedule:

  • Morning: Lectures
  • Afternoon: Practicals using participants own data

Course Instructors: Prof Vera Pawlowsky-Glahn and Prof Juan José Egozcue

Juan José EgozcueJuan José Egozcue studied Physics, oriented to Geophysics and Meteorology, at the University of Barcelona (Spain). He obtained his PhD in the same university with a dissertation on maximum entropy spectral analysis (1982). He taught several topics of Applied Mathematics and Probability and Statistics at the Civil Engineering School in the Universidad Politécnica de Cataluña (UPC, Barcelona, Spain). He became Full Professor in 1989, at the UPC, and he retired on February 1st 2016. He is now Emeritus professor of UPC.  His research activities are presently centered in two lines: estimation of natural hazards using Bayesian methods, and statistical analysis of compositional data, with special emphasis in the geometry of the sample space, the simplex.  He has been leader of several research projects. Most of them on hazard and vulnerability of coastal and port structures under action of severe ocean-waves. At present, he participates actively in a project on compositional data analysis. Research contributions have been published in many journal articles and most important results are summarized in the book “Modeling and Analysis of Compositional Data” (Wiley 2015), co-authored by V. Pawlowsky-Glahn and R. Tolosana-Delgado.

Prof Vera Pawlowsky-Glahn 200x230Dr. Pawlowsky-Glahn is professor at the University of Girona, Department of Computer Science, Applied Mathematics, and Statistics. She studied Mathematics at the University of Barcelona, Spain, and obtained her PhD (doctor rerum naturam) from the Free University of Berlin, Germany. Before going to Girona, she was professor in 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 books. Together with A. Buccianti she has acted as editor of a book in honour of J. Aitchison in 2011 published by Wiley, who has also published in 2015 a textbook on modelling and analysis of compositional data, co-authored with J.J. Egozcue and R. Tolosana-Delgado. Until 2008, she was the leader of a research group on this topic involving professors from different Spanish universities. 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. Prof. Dr. 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, was nominated Distinguished Lecturer of IAMG in 2007, and received the J.C. Griffiths Teaching Award in 2008. From 2008 to 2012 she was President of IAMG and is Past-President for the period 2012-2016. From 2015 to 2017 she is President of the Association for Compositional Analysis, founded in L’Escala (Spain) in June 2015.

Short Course 2: RGeostats to review geostatistical concepts

Geostatistics has now gained momentum and is commonly used in domains as diverse as Mining, Oil & Gas, Air Water and Soil quality monitoring and many other fields. Geostatistics provide a powerful and flexible suit of procedures to analyze and map almost any type of spatial data. It also provides sound techniques for uncertainty evaluation and risk assessment.
The team of Geostatistics of Mines ParisTech (formerly headed by Prof. G. Matheron) has decided to help disseminating the theory by producing the package RGeostats. This package, developed on the R platform, is available on http://cg.ensmp.fr/rgeostats where the user can also find some interesting pieces of information (tutorials, helps, FAQ).
The 2 days workshop gives an opportunity to review the main concepts of Geostatistics.
The attendee will put them in practice to solve specific problems on real data sets, building relevant workflows with the RGeostats package.

Date: Friday 1 September – Saturday 2 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$180
Registration: Opens Wednesday 1 March 2017

Course Instructors: Didier Renard and Nicolas Desassis

Didier Renard Renard graduated from the Ecole des Mines de Saint-Etienne, France. He is now a senior geostatistician and has been working in the Team of Geostatistics in the Center of Geosciences of Mines ParisTech for more than 30 years.
At the head of the Petroleum group, he actively contributes to the inception, development and testing of new algorithms. He is one of the main authors of several well-known geostatistical commercial packages such as BLUEPACK and more recently ISATIS (©Geovariances). He is the main author of RGeostats which provides a complete toolbox of geostatistical methods on the R platform.
Besides series of consulting activities for petroleum industry (Shell, Statoil, ENI, Total) he is involved in educational activities, teaching courses to students of Mines ParisTech and having trained several hundreds of practitioners all over the world.
Nicolas Desassis Nicolas Desassis is currently a research fellow in the Geostatistics team of the Centre de Géosciences of MINES ParisTech. He received a master (2003) in biostatistics and a PhD (2007) in statistics from the University of Montpellier 2, France (partnership with the Biostatistics for Spatial Process laboratory, INRA, Avignon, France). He also worked at INRIA (National Institute of Research in Automatic and Informatics) (2008) on spatio-temporal simulation of forest dynamics. Dr. Desassis’s research interests are in the area of geostatistics and spatial modelling more specifically in the inference of the spatial models (automatic variogram fitting, plurigaussian models…) and in conditional simulations. He also contributes to develop new Bayesian methods for inverse problems in geophysics.

Course Outline:

1) The Variogram and its extension (variogram cloud, variogram map) and the Model selection (fitting procedures)
2) Estimation using an optimal linear predictor: Kriging (simple, ordinary, intrinsic) and its extension (cross-validation). Neighborhood specification.
3) Multivariate Geostatistics. Bivariate statistics (covariance, linear model …) and multivariate spatial tools (cross-variogram and cross-covariance). Linear Model of Coregionalization. Multivariate estimation technique (Co-Kriging) and its extensions (collocated Co-Kriging, External Drift concept …). Factorial Kriging Analysis.
4) Simulations. Spatial law and Gaussian random function (anamorphosis). Conditional laws. Gibbs technique; Sequential Gaussian Simulation (SGS) algorithms; Turning bands algorithm; Stochastic Partial Derivative Simulations
5) Simulations of (one or several) categorical variables. Principles and inference of the parameters of the spatial characteristics. Using one or several underlying Gaussian Random Functions (PluriGaussian). Conditioning.

Equipment: Participants are expected to bring their own laptops with R and RStudio installed.

Short Course 3: Uncertainty in 3D Geological Modelling

This workshop will explore the basics of 3D geological modelling and uncertainty analysis using implicit modelling codes. Workshop participants will have the opportunity to see these techniques applied to the 3D geology of a complexly deformed terrain. The transfer of geological uncertainty into geophysical inversion codes will be presented as an example of transferring knowledge on uncertainty to downstream applications.

Each session will close with a group discussion that covers current capabilities and future trends in combining geological and geophysical uncertainty, including improved data to geometry engines, new visualisation and analysis tools for uncertainty estimation, and extension of these techniques to other downstream applications.

Date: Saturday 2 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$130
Registration: Opens Wednesday 1 March 2017

Course Instructors: Prof Mark Jessell & Dr Mark Lindsay, Dr Li Nan, Dr Vitaliy Ogarko, Evren Paykuz-Charrier, Jérémie Giraud

MJ Mark Jessell is a Winthrop Professor and Western Australian Fellow at the Centre for Exploration Targeting at The University of Western Australia. His scientific interests revolve around microstructure studies (the Elle platform), integration of geology and geophysics in 2 and 3D (the WA_In3D project), and the tectonics and metallogenesis of the West African Craton (WAXI).
ML Mark Lindsay is a Research Fellow at the Centre for Exploration Targeting, School of Earth Sciences, the University of Western Australia. Mark’s research focusses on the complexities of uncertainty and ambiguity in 3D geological and mineral potential modelling, and the process and psychology of data interpretation. Mark is working toward a stochastic approach to modelling that attempts to understand the relative importance of different data types in answering geoscientific questions, including those pertaining to mineral exploration, environmental and ground water management, and landform studies.
NL Dr Nan Li is a PostdDoc at the Centre for Exploration Targeting at The University of Western Australia and Associated Professor at Chinese Academy of Geological Sciences. The interesting researches of Nan’s are 3D modelling geometry program and Model-based prospectivity mapping at depth by 3D models and uncertainty
VO Dr Vitaliy Ogarko completed his MSc in Applied Mathematics and Informatics at Novosibirsk State University in 2008, focusing on parallel iterative methods for solving 3D diffusion-convection boundary value problems. He gained a PhD at the University of Twente in 2014, studying the mechanical and physical behaviour of highly polydisperse (in size) granular- and colloidal-like 3D systems, using theory and computer modelling. He is currently a main developer and architect of the 3D parallel geophysical joint inversion code Tomofast3D.
EP Evren Pakyuz-Charrier is pursuing a PhD in Geostatistics and Geological 3D modelling at UWA. His work involves advanced propagation of uncertainty through the use of Monte Carlo sampling over geometric inputs. He is main developer of the Common Uncertainty Research Engine.
JG Jeremie Giraud graduated as a geophysicist from the University of Grenoble, after which he joined the petroleum industry to work on integration problems applied to reservoir characterization. More recently he decided to pursue other challenges and started a PhD at the Centre for Exploration Targeting (University of WA) to focus on his main interest in the geosciences: integrated joint inversions.


Course Outline: History of 3D modelling and inversion, Introduction to geological uncertainty, Software Demonstrations: manual tuning, parameter sweeps and data uncertainties in 3D, Introduction to geophysical inversion & petrophysical uncertainty, Software Demonstrations: geologically and petrophysically constrained geophysical inversion, Future Directions

Short Course 4: Characterization of Geophysical Signals Using Multifractal Analysis

Multifractal analysis has been proved to be one of the efficient nonlinear data adaptive signal analysis techniques that can unravel the hidden information from various nonlinear geophysical signals. It has drawn a great deal of attention of scientists in varied disciplines of science and engineering, such as geomagnetism, atmospheric turbulence, space-time rainfall, ocean wind waves, fluid dynamics, seafloor bathymetry, geophysical well-logging and climate change studies among others for its unique ability to help improve the interpretation of the data. The multifractal analysis has been formalized into a thorough mathematical framework to determine the multifractal behaviour of nonlinear signals. In this short course, we shall discuss determination of the multifractal behaviour of geophysical signals using Multifractal Detrended Fluctuation Analysis (MFDFA). The MFDFA, which in fact, is a generalization of detrended fluctuation analysis (DFA), provides a comprehensive understanding of the multifractal behaviour of the signals through the multifractal singularity spectrum as well as the Hurst exponents, estimated in a modified least-squares sense.

The course is mainly designed for scientists and researchers to help view their research problems in a new angle with this novel signal analysis technique. The course will cover basic principles and a thorough introduction of fractals and multifractals, with a special reference to their applications in (i) ionospheric TEC (total electron content) data analysis, (ii) geomagnetism and (iii) geophysical well-log data analysis. Special emphasis is given to interpretation of the analyses results.

Date: Saturday 2 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$80
Registration: Opens Wednesday 1 March 2017

Course Instructor: Prof. E. Chandrasekhar

ECDr. E. Chandrasekhar is currently a full Professor of Geophysics at Department of Earth Sciences, IIT Bombay, Powai, Mumbai, India. After obtaining Masters degree in Geophysics from Osmania University, Hyderabad, India, he joined Indian Institute of Geomagnetism (IIG), in Mumbai, as a researcher. While serving at IIG, he completed his Ph.D in Physics from University of Mumbai in 1999 with specialization in electromagnetic induction techniques. Later he accepted the prestigious JSPS postdoctoral fellowship and worked at Kyoto University, Japan, during 2000-2002.  He was awarded a visiting scientist position by CNPq of Brazil, and worked at Observatorio Nacional, Rio de Janeiro, Brazil, during 2003-04, before joining IIT Bombay as a faculty in 2004. He was awarded the DAAD short term visiting fellowship to visit GeoForschungsZentrum (GFZ), Potsdam, Germany in 2010. Recognizing the importance of various novel signal analyses techniques such as, wavelet analysis, fractal and multifractal analysis, and empirical mode decomposition analysis in geophysics, Prof. Chandrasekhar applied them to a variety of geophysical signals of different origins. As a lead editor, he brought out a book on “Wavelets and Fractals in Earth System Sciences”, published by CRC Press, Taylor and Francis group, UK in 2013. His research interests are: Geophysical signal processing, electromagnetic induction studies, and Geomagnetism.

Course Outline:

Morning Session: Introduction to Fractals and Multifractals. Differences between multifractality of geometrical shapes and time series. Different methods to determine the multifractality of signals. Introduction to DFA and MFDFA.

Afternoon Session: Application of MFDFA to (i) Ionospheric TEC data, (ii) Geomagnetism and (iii) Geophysical Well log data.

Short Course 5: Image Analysis, Visualisation and Geological Pattern Recognition for Mineral Exploration

Geological interpretation of a variety of exploration datasets such as remote sensing, geophysical, geochemical and topographic data is a routine task for mineral explorers, and effective use of image analysis, visualisation and pattern recognition techniques aids fast, objective and repeatable analysis of data. This 2-day course will offer a jargon-less and easy-to-understand exposition of the concepts and applications digital image processing and visualisation techniques that are relevant for exploration targeting. The contents will include the concepts of image types and storage, image correction and reconstruction, georegistration, image enhancements and image filtering in spatial and frequency domains and image display as well as pattern recognition using supervised and unsupervised machine learning. Practical applications of these techniques in mineral exploration will be presented with a particular emphasis on analysing geophysical potential field images and satellite remote sensing data, for the detection and mapping of structures, anomalies, lithological variations, hydrothermal alterations. The exposition would be followed by demonstration and hands-on exercises.

Date: Saturday 2 September 2017 – Sunday 3 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$180
Registration: Opens Wednesday 1 March 2017

Course Instructors: Eun-Jung Holden and Alok Porwal

Eun-Jung HoldenEun-Jung (EJ) Holden is a Research Professor at the University of Western Australia (UWA). She was trained as a computer scientist specialising in image analysis and pattern recognition and made a transition to geoscience in 2006. She established and leads the Geodata Algorithms Team at the Centre for Exploration Targeting (CET) within UWA. The team spans the boundaries of computational science and geoscience, and has been developing innovative geodata analytics tools by working closely with the Geological Survey of Western Australia (GSWA) and the resource industry. Their collaboration with industry resulted in three commercial software products, namely the CET Grid Analysis and the CET Porphyry Detection extensions for Geosoft Oasis Montaj, and the Image and Structure Interpretation Workspace for ALT’s WellCAD, which are used widely by mining and petroleum companies and consultancies around the world. Their collaboration with GSWA over the past three years resulted in the development of the Integrated Exploration Platform, a GIS based data interpretation support platform for mineral explorers, which was launched as a GSWA product in 2016. The Team won various awards including Laric Hawkins Memorial Innovation Award at the 23rd International Geophysics Conference and Exhibition in Melbourne in 2013; and the UWA Vice Chancellor Award in Impact and Innovation in 2015.

Alok PorwalAlok Porwal is a full professor in the Centre for Studies in Resources Engineering at Indian Institute of Technology Bombay. He is PhD in mathematical geology and mineral exploration from ITC, the Netherlands. Before joining IIT Bombay, he was R/Associate Professor at Centre for Exploration Targeting at the University of Western Australia, where he continues to hold adjunct positon. His research interests include geologic remote sensing (terrestrial and planetary), spectroscopy of geologic material, GIS-based Mineral prospectivity modelling, machine learning and artificial intelligence in geological studies. Basically an academic, he works very closely with the industry and has been involved in a large number of projects for mining industry in Australia, Africa and India. Alok is an Associate Editor of Ore Geology Reviews, and a member of the Advisory Board of Natural Resources Research. He is currently editing a special issue of Ore Geology Reviews on geologic remote sensing. A prolific researcher, he has published over 60 highly cited papers in international journals. He teaches Masters courses on principles of remote sensing, remote sensing for mineral and hydrocarbon exploration, spectroscopy of planetary material, resource potential modelling and geospatial data analysis methods. He also conducts regular short-term training courses for mining industry as well as governmental and intragovernmental agencies. For more details, please visit http://www.csre.iitb.ac.in/~alok/group/faculty.html

Course outline:
Digital image processing basics for mineral exploration including image types and display; Remote sensing data: types, preprocessing (including atmospheric corrections) and georegistration; Image enhancement and spatial and frequency domain image filtering; Principal component and maximum noise fraction transformations: theory and applications; Supervised and unsupervised machine learning for image classification

Equipment:
The participants should bring their own laptops, preferably with the image processing software ENVI installed. Free, fully functional evaluation version of ENVI can be requested from http://www.harrisgeospatial.com/ContactUs.aspx . Since these are limited period license, the participants are advised to obtain them shortly before the workshop.

Short Course 6: Mathematical and statistical basics of prospectivity modeling

Prospectivity modeling aims at the prediction of the georeferenced conditional probability of the presence or absence of a target given favorable or prohibitive georeferenced predictors, or the construction of a two classes {0, 1} classification of the target. More specifically, the objective is to recognize locations for which the predicted conditional probability is a relative maximum. Despite the ubiquity of mathematical/statistical methods and approaches to prospectivity modeling, the mathematical assumptions to authorize them do not seem to be too well communicated. The subject of the course are various widely used methods and procedures of prospectivity modeling like weights of-evidence and its variants, logistic and Cox regression, artificial neural nets, and others. The course puts special emphasis on their mathematical/statistical origins, their modeling assumptions, and their relationships to one another. The objective of the course is to provide participants with a better insight to distinguish different approaches and check criteria of their proper application.

Date: Saturday 2 September 2017 – Sunday 3 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$180
Registration: Opens Wednesday 1 March 2017

Course Presenter: Helmut Schaeben

HSHelmut Schaeben has been  professor of Geomathematics and Geoinformatics in the Department of Geosciences, Geoengineering and Mining atTU Bergakademie Freiberg, Germany since 1996. A mathematician by education he spent his professional life with geo- and material scientists at universities in Aachen (Germany), Berkeley (CA, USA), Bonn (Germany), Metz (France) and eventually Freiberg. He holds a doctorate and a habilitation both from RWTH Aachen. His general professional interest is the development of mathematical and informatical models and their numerical realisations for applications to geological and material sciences. He serves on the editorial board of Mathematical Geosciences and Geomathematics. Having contributed to the EU Project “Nano–particle Products from New Mineral Resources in Europe – ProMine” (2009-2013) which marked a major shift in European raw material politics, he became increasingly interested in the mathematics and statistics of prospectivity modeling. On the occasion of the 34th IGC in Brisbane he received the John C. Giffiths Teaching Award 2012 from International Association for Mathematical Geosciences (IAMG).


Course Outline: Two morning sessions, 2 x 1.5h lectures each: Probability, odds, logits, conditional probability, Bayes formula; Correlation, stochastic independence, conditional stochastic independence (CI); Checking CI, testing CI, Hammersley-Clifford theorem; Spatial resolution, indicator transform, spatially induced stochastic dependence; Weights of evidence, generalized weights-of-evidence, weights-of-evidence assuming CI; Ordinary logistic regression, logistic regression including interaction terms; Statistical Learning: Artificial neural nets, etc.; Point processes, Cox regression

Two afternoons, 2 x 1.5h hands-on exercises using the free statistical software R installed on participants’ own laptop or notebook. R-scripts and data sets will be provided by instructor.the following will be covered: Basic calculations with R; Plotting map images with R; Function to run weights-of-evidence; R function glm to run logistic regression; R packages for other methods

Equipment: Participants are expected to bring their own laptops with R and RStudio installed.

Short Course 7: Mathematical Morphology in Interpolations and Extrapolations

Data available at multiple spatial / spectral / temporal scales pose numerous challenges to the data scientists. Of late researchers paid wide attention to handle such data acquired through various sensing mechanisms to address intertwined topics—like pattern retrieval, pattern analysis, quantitative reasoning, and simulation and modelling—for better understanding spatiotemporal behaviours of several terrestrial phenomena and processes. Various original algorithms and techniques that are mainly based on mathematical morphology (Matheron 1975, Serra 1982, Soille 2010, Sagar 2010, 2013) have been developed and demonstrated. This course that presents fundamentals of mathematical morphology and their involvement in interpolations and extrapolations with applications in geosciences and geoinformatics would be useful for those with research interests in image processing and analysis, remote sensing and geosciences, geographical information sciences, spatial statistics and mathematical morphology, mapping of earth-like planetary surfaces, etc. This course will be offered in two parts on 03-09-2017. In the morning shift all the fundamental morphological transformations would be covered. The applications of those transformations, covered in the first shift, to understand the morphological interpolations and extrapolations would be covered with several case studies in the second shift

Date: Sunday 3 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$130
Registration: Opens Wednesday 1 March 2017

Course Instructor: Prof B. S. Daya Sagar

Picture1B. S. Daya Sagar is a full Professor of the Systems Science and Informatics Unit (SSIU) at the Indian Statistical Institute. Sagar received the M.Sc and Ph.D de-grees from the Faculty of Engineering, Andhra University, Visakhapatnam, India, in 1991 and 1994 respectively. He is also the first Head of the SSIU. Earlier, he worked in College of Engineering, Andhra University, and Centre for Remote Imaging Sensing and Processing (CRISP), The National University of Singapore in various positions during 1992-2001. He served as Associate Professor and Researcher in the Faculty of Engineering & Technology (FET), Multimedia University, Malaysia during 2001-07. His research interests include mathematical morphology, GISci, digital image pro-cessing, fractals and multifractals their applications in extraction, analyses, and modeling of geophysical patterns. He has published over 75 papers in journals, and has authored and/or guest edited 9 books and/or special theme issues for journals. He recently authored a book entitled “Mathematical Morphology in Geomorphology and GISci,” CRC Press: Boca Raton, 2013, p. 546. He recently co-edited a special issue on “Filtering and Segmentation with Mathematical Morphology” for IEEE Journal on Selected Topics in Signal Processing (v. 6, no. 7, p. 737-886, 2012). He is an elected Fellow of Royal Geographical Society (1999), Indian Geophysical Union (2011), and was a member of New York Academy of Science during 1995-96. He received Dr. Balakrishna Memorial Award from Andhra Pradesh Akademi of Sciences in 1995, Krishnan Gold Medal from Indian Geophysical Union in 2002, and ‘Georges Matheron Award-2011 (with Lecturership)” of International Association for Mathematical Geosciences. He is the Founding Chairman of Bangalore Section IEEE GRSS Chapter. He is on the Editorial Boards of Computers & Geosciences, and Frontiers: Environmental Informatics. More details about him can be seen at http://www.isibang.ac.in/~bsdsagar

Course Outline:

Morning Session: Introduction to Mathematical Morphology: (i) Binary Mathematical Morphology, (ii) Grayscale Mathematical Morphology, (iii) Geodesic and Graph Morphology. 

Afternoon Session: Mathematical Morphology in Spatial Interpolations and Extrapolations: (i) Conversion of point-data into polygonal map via SKIZ and WSKIZ, (ii) Visualisation of spatiotemporal behaviour of discrete maps via generation of recursive median elements, (iii) Morphing of grayscale DEMs via morphological interpolations, and (iv) Ranks for pairs of spatial fields via metric based on grayscale morphological distances

Short Course 8: Image Processing using Python

This is a hands-on course on digital image processing using the open source package scikit-image, an image processing library with algorithms and utilities for use in research, education and industry. It is developed by an active, international team of collaborators, providing a well-documented system based in the Python programming language. Applications of each topic will be given using optical microscopic images.

Date: Sunday 3 September 2017
Time: 9:00am – 12:00pm
Venue: The University of Notre Dame
Room: TBC
Cost: A$130
Registration: Opens Wednesday 1 March 2017

Course Presenter: Alexandre Fioravante de Siqueira

ADDr Alexandre de Siqueira is a specialist in Image Processing and Computer Vision applied to photomicrographs. Open source and open science advocate, he is the author of the book ‘Octave – Seus primeiros passos na programação científica’ (Octave – Your first steps on scientific programming, free translation), a core member on the scikit-image community, and writer on www.programmingscience.org. Currently he is a Postdoctoral Researcher at University of Campinas, Brazil, and TU Bergakademie Freiberg, Germany, working on methods to automatically count and measure tracks on photomicrographs of different minerals.”

Course Outline: Installing Anaconda, a Python distribution, Handling images using Python, Pre-processing and enhancing images, Image binarization and segmentation, Region properties and labels

Short Course 9: Advanced Analytics for Evaluating and Interpreting Geochemical Data

Participants of this workshop will learn methods for data analytics in geochemistry. Over the last decade there has been a rapid growth in the application of data analytics for data-driven business decisions in virtually every industry. In the next 10 years the mining industry will have to rapidly adopt and apply the power of data analytics to the ever-growing volume of geochemical data sets. However, geochemical data have unique mathematical properties and should not be analyzed without consideration of its structure. Geochemical data are “compositions”; and by definition, must sum to a constant (e.g.100%) and therefore none of the components (elements/oxides) are free to vary independently. This special property of geochemical data can lead to erroneous results when standard data analytics methodologies are applied.
The workshop will cover the application of ratios and logratios to compositional data; molar element ratio methods; multivariate methods including principal component analysis, cluster analysis, discriminant analysis, classification and regression trees, multi-fractals, linear/non-linear geostatistics.

Date: Sunday 3 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$130
Registration: Opens Wednesday 1 March 2017

Course Presenters: Natalie Caciagli, Juan Carlos Ordonez-Calderon, Eric Grunsky, Qiuming Cheng, Raimon Tolosana-Delgado, Cliff Stanley, Jennifer McKinley, Ute Mueller, June Hill

 Natalie Caciagli Natalie Caciagli (Ph.D.) is a Senior Geochemist at Kinross Gold where she works on a wide range of international projects in Exploration, Mine Planning, Resource Estimation, Geometallurgy and Production. She combines compositional data analysis and data science techniques and she has applied Artificial Intelligence (AI) and machine learning tools to mining projects, integrating data in geochemistry, lithology, alteration mineralogy, and metallurgy. Natalie has an MSc in Geology from the University of California, Los Angeles and a PhD in Geology from the University of Toronto in Canada. She has presented at numerous workshops and academic conferences, as well as authoring a number of research papers. Her experience in mining analytics spans North America, South America and, West Africa.
 Qiuming Cheng Qiuming Cheng is a Canadian mathematical geoscientist. Qiuming is currently a full professor jointly in the Department of Earth and Space Science and Engineering, and in the Department of Geography, at York University, Toronto.[1] He received the William Christian Krumbein Medal in 2008 from the International Association for Mathematical Geosciences.[2] He was the President of the International Association for Mathematical Geosciences (2012-2016). He is currently the President of the International Union of Geological Sciences (IUGS).[
 Eric Grunsky Eric Grunsky is an adjunct professor at the University of Waterloo, Canada and is currently Secretary General for the IAMG. Eric is been noted for his research in the application of multivariate statistical methods and spatial statistics applied to geochemical data.
 June Hill June Hill is a research scientist with CSIRO Mineral Resources. Her current research interests are in automating the interpretation of drill hole data. Her fields of expertise include compositional data analysis, spatial data analysis and 3D geology modelling. June has a PhD in structural and metamorphic geology and a Masters of Engineering Science in pattern recognition.
 Dr Jennifer McKinley Dr Jennifer McKinley, Reader, School of Natural and Built Environment, Queen’s University Belfast. As a Chartered Geologist, I currently hold a number of international roles including: President of the International Association of Mathematical Geoscientists (2016-2020); Trustee and Member of Council of the Geological Society London, Communications Officer for the IUGS-IFG (Initiative on Forensic Geology) and Secretary for the Royal Irish Academy Geosciences and Geographical Sciences committee. My research has focused on the application of spatial analysis techniques, including geostatistics, compositional data analysis and Geographical Information Science (GIS), to soil geochemistry, environmental and criminal forensics, human health, slope instability, airborne geophysics and weathering studies. I have authored more than 100 scientific articles, including peer-reviewed journal articles; 1 co-authored book (Geoforensics); peer-reviewed book chapters; technical reports and numerous international conference contributions. Interdisciplinary collaboration and strong partnership working with multiple stakeholders, underpins all of my research, culminating in strong international associations.
 Ute Mueller  Ute Mueller is an associate professor in mathematics at Edith Cowan University in Perth Australia. She has been involved in research on geostatistical methods for the last 20 years and her current focus is on the geostatistical modelling (simulation and estimation) of multivariate data, including compositional data. Of particular interest is the quantification of uncertainty.  
Juan Carlos Ordóñez Juan Carlos Ordóñez is as a Geochemist with Hudbay Minerals and an adjunct professor at the Harquail School of Earth Sciences at Laurentian University. His key expertise includes applied geochemistry and field geology of mineral deposits. Juan Carlos approaches geochemistry from a compositional data analysis and data analytics perspective to integrate geochemistry with geological, mineralogical, and geometallurgical data. He has an MSc and a PhD in petrology and geochemistry from, respectively, Shimane University in Japan and the University of Windsor in Canada. He has over 15 years of experience dealing with geochemical data in diverse mineral systems such as porphyry, skarn, VMS, IOCG, sedimentary hosted, and epithermal deposits in 10 different countries spanning Precambrian shields to Cordilleran settings.
 Cliff Stanley Cliff Stanley  received his MSc and PhD from the University of British Columbia, both under the supervision of Alastair Sinclair. Cliff then served in several post-graduate research posts and as Adjunct Professor in economic geology at the Mineral Deposit Research Unit at UBC. In 1998, Cliff became assistant professor in applied geochemistry in the Department of Earth and Environmental Science at Acadia University in Nova Scotia. At present, Cliff is a full professor with research interests centred around numerical applications in geochemistry and petrology. ratio analysis to lithogeochemical data.
 Raimon Tolosana-Delgado Raimon Tolosana-Delgado is an engineering geologist and holds a PhD in environmental technology and physics, specialist in compositional data analysis and geostatistics, particularly applied to the Earth sciences. In the last 15 years, he has coauthored 40 papers in peer reviewed journals. Currently he is executive vice-president of the International Association for Mathematical Geosciences, and works at the Department of Modelling and Valuation of the Helmholtz Institute Freiberg for Resource Technology (Germany) doing research on mineral exploration models, geometallurgical orecharacterisation and modeling, or minerals processing simulation and optimisation.

 

Course Outline:

Part 1.- Geochemical data
(a) Understanding the nature of geochemical data – brief intro
(b) Selecting suitable extraction methods geochemical methods and sample design
(c) Choosing the right sampling density (spatial), also known as support
(d) data preprocessing, imputation and levelling

Part 2.- Molar Element Ratios
(a) The effects/benefits of using element ratios to overcome some aspects of closure
(b) Pearce/Generalized Element Ratio methods for identifying geochemical processes.
– Examples: use in mineral exploration programs

Part 3.- Understanding the compositional nature of geochemical data
(a) Log ratios are the tools used to evaluate the relative relationships of geochemical data
(b) Basic theory of log ratio methods / clr-ilr-alr
(c) exploratory analysis for CoDa ternary, PCA, Balances

Part 4.- Statistical tools for Geochemical compositions – Applications
(a) Model selection and model evaluation
Model selection and validation
Variable selection: Removing noise
Training and test datasets: Teaching geochemistry to recognize patterns
Model bias versus model variance: Understanding overfitting
Cross-validation: Tools for selecting the best model
(c) PCA/Cluster analysis/Random Forests
(d) Bayesian/kernel methods for exploration & mining
(e) multi-fractals in exploration
(f) Utilizing the geospatial context – Minimum/maximum Autocorrelation Factor Analysis

Short Course 10: Multiscale Analysis of Hydrothermal Mineralising Systems

This workshop will explore the consequences of considering hydrothermal mineralising systems as giant chemical reactors at all scales from lithospheric to nanometre. Workshop participants will have the opportunity to see how nonlinear behaviour leaves its mark as apparently stochastic distributions of alteration assemblages, mineralisation and structures. Examples of such irregular (apparently random) distributions will be analysed and seen to be deterministic, containing all the information required to understand why the ore body is small or large and whether it is well or poorly endowed.

Each session will close with a group discussion that covers current capabilities and future trends in analysing and quantifying the episodic and localisation behaviour recorded in the multifractal paragenetic sequence and deformation history through use of many tools (including fractal and multifractal analysis, recurrence plots and recurrence networks) developed by studies of other nonlinear systems.

Date: Sunday 3 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$130
Registration: Opens Wednesday 1 March 2017

Course Instructors: Prof Bruce Hobbs, Prof Alison Ord, Dr Mark Munro, Prof Jorn Kruhl, Dr Klaus Gessner, Dr Weronika Gorczyk, Dr Chris Gonzalez.

Bruce Hobbs Bruce Hobbs is a Research Fellow at CSIRO in Perth and Adjunct Professor in the Centre for Exploration Targeting at The University of Western Australia. His present interests are in applying the tools developed for nonlinear dynamical systems over the past 50 years or so to large data sets on alteration assemblages, deformation and mineralisation in hydrothermal systems in order to extract information of relevance to metal discovery.
Alison Ord Alison Ord is an Honorary Professor in the Centre for Exploration Targeting at The University of Western Australia. Her present interests are in applying the tools developed for nonlinear dynamical systems, particularly multifractal analysis and recurrence plots, to large data sets on alteration assemblages, deformation and mineralisation in mineralising systems in order to quantify and fingerprint various classes of hydrothermal mineralising systems.

Course outline:

An outline of the behaviour of non-linear systems; fractals, multifractals, singularity spectra, recurrence plots, joint recurrence plots, attractors. Geotectonic modelling to determine sources of CO2 rich fluids. Analysis of spatial distribution of mineralisation using recurrence plots. Analysis of drill-hole alteration, mineralisation and multi-element data sets. Interpretation of multifractal spectra and recurrence plots in terms of nonlinear dynamics.

 

Short Course 11: Young Scientist Course

In the scope of IAMG2017, there will be a course specifically targeting the early career researchers and postgraduate students. The course aims at contributing to the early career researchers’ professional career. 

Date: Sunday 3 September 2017
Time: TBC
Venue: The University of Notre Dame, Room TBC
Cost: TBC
Registration: Opens Wednesday 1 March 2017

Short Course 12: Practical Stratigraphic Forward Modelling

Stratigraphy is the scale-independent preserved record of many interacting erosion, transport and depositional processes and events. Numerical stratigraphic forward modelling (SFM) attempts to simulate the physical, chemical and biological processes that have been, are, and will be responsible for stratal architecture over time scales from seconds to millions of years. We can only demonstrate that we understand these processes by making quantitative predictions about stratal architecture and rock properties, away from observations, at all scales and time intervals from the Pre-Cambrian to the future. Using this technology earth scientists can develop quantitative multiple working hypotheses of basin fill and the response of depositional systems to future climate change. The course will focus on hands-on use of the Sedsim SFM software, and the workflow needed to develop and test depositional and stratigraphic understanding at a variety of scales. The treatment of theory will be limited and there is a strong emphasis on using SFM programs in real-world settings such that their capabilities and limitations are well understood. A range of practical applications from flume tank to continental scales will be discussed.  Copies of the latest trial version of Sedsim and documentation will be provided beforehand together with exercises and test data sets.  Participants will be expected to have their own lap-top computer and pre-load the Trial-Sedsim software and Exercises.

Date: Friday 8 September 2017
Time: 9:00am – 6:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$130
Registration: Opens Wednesday 1 March 2017

Course Instructor: Dr. Cedric M. Griffiths

Cedric GriffithsCedric Griffiths is Director of StrataMod and Adjunct Professor at Curtin University. He has held positions as Nordic Council Research Professor in Petrophysics at NTNU, Trondheim, and the South Australian Chair of Petroleum Geology at the University of Adelaide. He has worked for BP Research and Sintef, in Norway, and CSIRO in Australia on various aspects of quantitative stratigraphic modelling. Cedric holds a PhD from Newcastle University in the UK, is an Associate Editor of Terra Nova, and over the past 30 years has published numerous peer‐reviewed papers in the fields of quantitative stratigraphy, stratigraphic forward modelling, petroleum geology, coastal process modelling and petrophysics.

 

Course outline:  

Morning Session:   Introduction to Stratigraphic Forward Modelling: Historical review, current status, trend and future: Sedsim (algorithm, parameters, problem identification and specification):  Use of Trial-Sedsim and discussion of the input files. Simple worked class exercise. 

Afternoon Session: Carbonate and organic exercises: Digital flume tank exercises: Basin Scale Exercise: Constraint and Verification: Summary.

Short Course 13: Chemometric methods applied to spectral sensing data for exploration and resource characterisation

Modal mineral abundances are important parameters for the whole mine life cycle, from exploration, to mining and processing of ore and gangue materials. Selected mineral species can, for example, represent vector minerals to hydrothermal ore deposits or have significant impact on mill performance or flotation circuits. Spectral sensing technologies provide a cost-effective and fast solution for acquiring large volumes of mineralogical data that are required to fully represent  variations of mineral assemblages across a resource. To obtain modal mineralogy, spectral data usually have to be calibrated with independent analytical methods such as QXRD or QEMSCAN. The physicochemical information contained within the spectral signatures can also be used to model, for example, geochemical indices and geometallurgical parameters.

This half day course aims introduce some of the chemometric methods that can be applied to visible and infrared reflectance data for modelling modal abundances of selected minerals using The Spectral Geologist software (TSGTM; https://research.csiro.au/thespectralgeologist/). Furthermore, hands-on exercises will be used to introduce the modelling of geochemical indices and geometallurgical parameters that are commonly important for exploration and/or resource characterisation. A particular focus of this workshop will be on investigating the drivers behind some of the obtained correlations and modelling results, which is important to evaluate the significance of the developed models.

Date: Friday 8 September 2017
Time: 9:00am – 12:00pm
Venue: The University of Notre Dame, Room TBC
Cost: A$80
Registration: Opens Wednesday 1 March 2017

Course Instructor: Dr. Carsten Laukamp

CLCarsten Laukamp is a senior research geoscientist at CSIRO Mineral Resources, based in Perth, Australia. In 2007, Carsten obtained his Doctorate of Science (Dr. rer. Nat.) from the Ruprecht Karls University in Heidelberg, Germany, based on his study of the structural and fluid system evolution in the Otavi Mountain Land (Namibia) and its significance for the genesis of sulphide and non-sulphide mineralisation. Before joining CSIRO, Carsten was a postdoctoral research fellow at James Cook University in Townsville, where he worked in the pmd*CRC on the evaluation of hyperspectral remote sensing data for mapping hydrothermal alteration footprints in the Mount Isa Inlier. In Carsten’s current position with CSIRO he aims to unravel the physicochemistry of minerals using lab, field and remote visible and infrared reflectance spectroscopy, working with microscopic to continent-scale data.

Course outline:  
Introduction to Mineral Spectroscopy and The Spectral Geologist Software
Overview of mineralogical, geochemical and geometallurgical parameters important for exploration and resource characterisation of hydrothermal and/or iron ore deposits
Hands on case studies for modelling modal mineral abundance, geochemical indices and geometallurgical parameters 

Short Course 14: Resource and Reserve Classification through JORC and VALMIN Codes

Presented by Dr Jacqui Coombes

Date: TBC
Time: TBC
Venue: The University of Notre Dame, Room TBC
Cost: TBC
Registration: Opens Wednesday 1 March 2017