IAMG2026
The 24. annual conference of the IAMG
August 23 - 28, 2026, Montreal, Canada

Short Courses

Comming soon

Mathematical Morphological (Spatial) Algorithms in Surfaces↑

  • organized by: B. S. Daya Sagar
  • Venue: coming soon
  • Date: August 23, 2026
  • Time: coming soon
  • Min. number of participants: 5
  • Max. number of participants: 15
  • costs per participant: coming soon
  • Attendance Requirements: -
  • Recommended prerequisites:

Introduction

Many micro-scale to macro-scale surfaces represented in numerous ways—(e.g.: Digital Elevation Models: an Important Source of Data for Geoscientists, IEEE Geoscience and Remote Sensing Magazine, v. 8, no. 4, p. 138-142. 10.1109/MGRS.2020.3031910; rock surfaces; floodplain surfaces, material and mineral surfaces, etc))—contains rich geometric, morphologic and topologic information, but hidden for naked eyes. Unraveling such information from the surfaces of relevance to the Earth and Planetary sciences is a challenge. Such information from time-dependent and time independent surfaces is essential to model and visualize terrestrial and planetary surficial phenomena and processes. Geoscientists with appropriate mathematical knowledge can better exploit the full potential of the surfaces that have hitherto been analyzed via classical mathematical and statistical techniques. Mathematical Morphology is an area of geoscience that most people don’t realize will literally change the way they look at the surfaces of relevance to Earth and Planetary studies! This short course would provide a glimpse of how mathematical morphology could be employed to treat surfaces to derive scientific outcomes. Geoscience communities involving all its sub-branches some way or the other are familiar with surfaces of varied types acquired through varied sensing mechanisms. Mathematical morphology, a powerful artificial intelligence tool, is the basis for developing the spatial morphological algorithms that deal with those surfaces of relevance to the Earth and Planetary sciences. With the advent of the availability of accurate data at multiple spatial, spectral and temporal scales from various sources, the simulation, reconstruction, and prediction of surficial changes and processes on discrete time scales are feasible. With over three and a half decades of knowledge in showing applications of mathematical morphology, fractal geometry and chaos theory in understanding terrestrial phenomena and processes, Professor Daya Sagar deals with first lecturing on Mathematical Morphology, followed by analyzing such surfaces through mathematical morphology. This proposed tutorial would provide required knowledge to the interested audience on mathematical morphology in analyzing surfaces in a firm quantitative manner.
  • Audience would gain knowledge on morphological thinking to better understand the terrestrial and planetary surfaces processes.
  • Audience would be able to employ mathematical morphological operations and transformations in studies related to geosciences, remote sensing and geospatial data sciences.
  • Audience would be able to reason the importance of differential morphology in the context of studying the surficial morphological changes of relevance to terrestrial and planetary studies, with examples on rock surfaces, porous-medium, DEMs.
  • Audience with reasonable knowledge in basic set theory and the surfaces would benefit significantly in understanding the morphological equations, illustrations, and spatial (morphological) algorithms that would be explained in a span of six hours.

Machine learning tools for mineral systems modelling and mineral predictive mapping↑

  • organized by: Geological Survey of Finland (GTK), Finland and Beak Consultants GmbH, Germany
  • Venue: coming soon
  • Date: August 23, 2026
  • Time: coming soon
  • Min. number of participants: 5
  • Max. number of participants: 15
  • costs per participant: coming soon
  • Attendance Requirements: -
  • Recommended prerequisites:

Topic introduction:

Machine learning has proven to be applied successfully to mineral exploration targeting in the last decade. The aim of this short course is to provide an introduction into artificial intelligence applied to mineral predictive mapping to generate drilling targets during mineral exploration and to estimate mineral resource for the assessment of critical raw materials.

Course description:

In the first part of the short course, a short review will be provided about the concept of mineral predictive mapping. Following up, different machine learning algorithms are being presented and discussed on how they may be used for mineral predictive mapping. The second main part of the short course is strongly focusing on the practical hands-on-training for a newly developed open source tool for mineral predictive mapping (MPM) for exploration and assessment of critical raw materials called “EIS” – Exploration Information System. The demonstrated software tool is open-source and will be able to run for free on the computer of the participants.

Specific topics of the short course include:

  • Short introduction to mineral predictive mapping concepts,
  • Review of machine learning algorithms applied to mineral predictive mapping, e.g. neural networks, self-organizing maps, fuzzy logic, random forest,
  • Hands-on-training on open source tool “EIS” for mineral systems approach and mineral predictive mapping:
    “EIS” provides a library of tools for using mineral systems approach and mineral predictive mapping in a combined graphical user interface. It runs under Quantum GIS (QGIS) on Windows systems. Training tutorial starts from installation of EIS Toolkit and EIS Plugin on the computer leading up to the final compilation of a mineral prospectivity map using the EIS Wizard in QuantumGIS,
  • Case studies for mineral predictive mapping from Europe and Northern America
  • Presentation of on-going new developments in mineral exploration and prospectivity mapping.

Top “takeaways”:

This short course will provide best practices, new ideas and insights in mineral exploration, especially with regards to application of the newly developed open source for mineral predictive mapping. At the end of the course, participants will be able to run mineral predictive mapping on their own computer using the provided and trained open-source software tools.

Practical use of multiple-point statistics algorithms - within Python - to generate heterogeneous 3D property fields↑

  • organized by: Guillaume Pirot, School of Earth and Oceans, Centre for Exploration Targeting, The University of Western Australia, Australia
  • Venue: coming soon
  • Date: August 23, 2026
  • Time: coming soon
  • Min. number of participants: 5
  • Max. number of participants: 15
  • costs per participant: coming soon
  • Attendance Requirements: Participants are required to bring their own laptop. Prior to the workshop, participants should have a functioning Anaconda distribution https://www.anaconda.com/download installed on their laptop.
  • Recommended prerequisites: Participants should have some basic knowledge of coding with Python, that they can get by following the fundamentals of Python tutorials available at https://swcarpentry.github.io/python-novice-inflammation/index.html

Objectives

Spatial heterogeneity is a key driver of groundwater flow and transport. Despite the existence of multiple tools to generate geologically realistic property fields, those are not sufficiently used. The main objective of this workshop is to familiarise practitioners with the use of multiple-point statistics algorithms that enable the generation of complex spatial pattern from a training image while honouring local observations.

Content

Introduction to multiple-point statistics (MPS), familiarization with the geone Python library, bring your own project to work on or modelling of the Lower Burdekin Delta aquifer using MPS conditionally to interpretation of legacy boreholes.

Fundamental Deep Learning Concepts for Applied Geoscientists↑

  • organized by: Xiao Xia Liang, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States and Dany Lauzon, Tao Wen
  • Venue: coming soon
  • Date: August 23, 2026
  • Time: coming soon
  • Min. number of participants: 5
  • Max. number of participants: 15
  • costs per participant: coming soon
  • Attendance Requirements: -
  • Recommended prerequisites:

This short course focuses on the fundamental concepts of deep learning (DL). Topics include data cleaning and preparation, activation functions, hyperparameter tuning, model optimization through forward and backward propagation, and regularization techniques. These core concepts will be explored through DL models applied to geoscience case studies. Students will gain hands-on experience implementing these methods and develop a deeper understanding of how model design choices influence prediction quality and uncertainty.

Several DL architectures relevant to geoscience applications will be introduced. Students will begin with Multilayer Perceptrons (MLPs) to establish the theoretical foundations of deep learning, followed by a Convolutional Neural Network (CNN) to demonstrate spatial feature extraction from geological data. The course will then cover a Recurrent Neural Network (RNN) Autoencoder and a Variational Autoencoder (VAE) to illustrate representation learning, latent space interpretation, and probabilistic modeling of subsurface uncertainty.

The objective of this course is to teach the fundamentals of deep learning to anyone who wishes to enter into the field or deepen their understanding of its core concepts through the development and application of multiple DL architectures.

Machine Learning for Geostatisticians↑

  • organized by: Michael J. Pyrcz, The University of Texas at Austin
  • Venue: coming soon
  • Date: August 23, 2026
  • Time: coming soon
  • Min. number of participants: 5
  • Max. number of participants: 15
  • costs per participant: coming soon
  • Attendance Requirements: -
  • Recommended prerequisites:

Description

Machine learning is emerging as a powerful tool for enhancing geostatistical workflows. This is not a departure from tradition—geostatistics has always evolved by integrating theory and methods from other disciplines. If a method helps solve the problem, then it belongs in geostatistics. Spend a day with Professor Michael Pyrcz of The University of Texas at Austin as we build the theory and practice of machine learning, progressing from probability and data analytics to inferential and predictive modeling. Throughout the course, we will explicitly connect machine learning concepts to geostatistical principles, culminating in a discussion of how geostatistics can, in turn, improve machine learning workflows. Learning will be maximized through comprehensive notes, well-documented demonstrations, and interactive dashboards designed to make abstract concepts concrete and actionable.