Distinguished Lecturer

A. Description

The Distinguished Lecturer prepares a series of lectures preferably on a variety of subjects in the mathematical geosciences to be presented in places where IAMG Annual Meetings are not normally held.

B. Guidelines

The IAMG council voted in 2000 to establish a Distinguished Lecture series and approved a committee charged with implementing the recommendations contained in the report of the IAMG Lecture Series Commission (July 2000).

The purpose of the IAMG Distinguished Lecture series is to demonstrate to the broader geological community the power of mathematical geology to address routine geological interpretation and to deliver this knowledge to audiences in selected parts the world. Therefore, the Lectures Committee is seeking nominations for outstanding individuals who meet the following criteria:

  • A demonstrated ability to communicate mathematical concepts to a general geological audience.
  • A clear enthusiasm for mathematical geology.
  • Recognition for work in their field.
  • Established skill in working with individuals and in group discussions on geological problems.

The Distinguished Lecturer must be ready to travel and to perform the following duties:

  • Prepare and present a lecture suitable for a general geological audience.
  • Prepare and present one or two lectures on a more specialized topic.
  • Interact and hold discussions with individuals, both professionals and students, on applications of mathematical geology to local problems of interest.

Letters of nomination should include a curriculum vitae of the nominee and a short statement summarizing the ways in which he or she fulfills the nomination criteria.
Letters should be directed to the Chair of the Distinguished Lecture Series Committee by e-mail to :

christien.thiart@uct.ac.za

Or by regular mail to :

Christien Thiart
Department of Statistical Sciences University of Cape Town
Private Bag Rondebogch 7700
South Africa

C. Current Distinguished Lecturer (2024) – Michael Pyrcz

Michael Pyrcz is a professor in the Cockrell School of Engineering, and the Jackson School of
Geosciences, at The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also the principal investigator of the Energy Analytics freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin, an associate editor for Computers and Geosciences, and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences. Michael has written over 60 peer-reviewed publications, a Python package for spatial data analytics, and co-authored a textbook on spatial data analytics, ‘Geostatistical Reservoir Modeling’.
All of Michael’s university lectures are available on his YouTube channel with links to 100’s of Python
interactive dashboards and well-documented workflows, to support his students and working professionals with evergreen content. To find out more about Michael’s work and shared educational resources visit his website.
YouTube Channel: www.youtube.com/GeostatsGuyLectures
GitHub Repositories: https://github.com/GeostatsGuy/
Personal Website with Inventory of Shared Resources: www.michaelpyrcz.com
e-mail: mpyrcz@austin.utexas.edu

Abstracts:

Integrating Geostatistics into Data Analytics and Machine Learning
The subsurface resource industry has a long history of working with large, complicated geoscience and engineering datasets. Our community has been working with ‘big data’ for decades, driving the development and establishment of the geostatistical toolbox. As a result, we are uniquely prepared for the data science revolution, as we are further down the road of data-driven workflow development and have insights and experiences to share with the other scientific and engineering domains. The foundation of geostatistics, domain expertise, spatial continuity, scale and uncertainty remain essential, even with advanced deep learning architectures; therefore, many new data analytics and machine learning technologies are not ready off-the-shelf for subsurface applications. I elucidate opportunities to integrate geostatistics into data analytics and machine learning for success in the subsurface.

Deep Learning for Building Subsurface Models
The transition from variogram-based to object- and rule-based modeling has been motivated by an increasing necessity to integrate geological heterogeneity into our conditional subsurface uncertainty models to support development decision making. Now, new generative, deep learning models have emerged as the next step along this evolutionary path towards realism. It is time to pause, take stock, review these generative methods, and critically evaluate their performance, e.g., to evaluate their conditional performance in a quantitative manner. When do they work well and when do they break?

I Can Teach You Machine Learning in 1 Hour
Subsurface engineers and geoscientists are familiar with data, statistics and models; therefore, they are well-positioned to add machine learning skills to their toolkit. I know this because I have taught machine learning to over 2,000 working subsurface professionals over the past couple of years. Spend an hour with me and I’ll leverage your subsurface expertise to get you started with machine learning.

New Ideas for Mathematical Geoscience Education from Data Science
New open source tools are changing the way we build subsurface workflows. We now leverage the world’s brilliance to code less, work faster, learn more from our systems, and get more done. In addition, these new tools are helping us communicate and teach. Interactive slides with live plots, interactive dashboards and well-documented workflows are all enhancing education beyond the typical static slides and textbooks. The best way to learn mathematical geoscience is to play with data, models and systems. New open source tools make this a reality in the classroom. I’ll show you how to do this to improve your communication and impact in academia or industry.

 

D. Upcoming Distinguished Lecturer (2025) – Jeff Boisvert

Dr. Jeff Boisvert is a professor in Civil and Environmental Engineering, School of Mining and Petroleum Engineering, at the University of Alberta and co-director of the Centre for Computational Geostatistics (CCG). His research focuses on spatial modeling and geostatistics in the fields of mining, energy, and wildfire. He uses spatial numerical models to improve decisions in mine design, resource/reserve estimation, wildland fire management/response, and well planning/placement/management.
Lecture 1: The future of machine learning and model validation in the earth sciences: The good, the bad, and the ugly
The earth sciences are being transformed by advances in machine learning (ML) and artificial intelligence. From optimizing mineral exploration and hydrocarbon production to improving wildfire prediction and management, these methods offer exciting opportunities for modeling and decision-making. However, these advances bring challenges with model validation, which is critical for ensuring that predictions are robust, reasonable, and actionable.
This lecture will delve into the evolving role of ML in the mining, hydrocarbon, or wildfire industry, highlighting successes, pitfalls, and future prospects. “The Good” will explore case studies and implementations where ML has significantly improved modeling, decision making, and inference. “The Bad” will examine common pitfalls, including data biases, overfitting, and the misuse of algorithms without understanding domain constraints. Finally, “The Ugly” will confront the ethical and operational risks posed by poorly validated models, emphasizing the importance of transparency and domain experts.
We will discuss best practices for integrating ML into earth sciences while addressing the complexities of model validation. At the request of the attendees, the focus of this lecture will be on mining, hydrocarbon, or wildfire applications.Lecture 2: Understanding, incorporating, and assessing the impact of uncertainty in our decisions: Why are we still so reluctant?
Uncertainty is an inherent aspect of decision-making in the earth sciences, particularly in mining, hydrocarbon, and wildfire domains. Whether estimating ore reserves, forecasting hydrocarbon production, or predicting wildfire behavior, uncertainties arise from data limitations, model assumptions, and complex natural processes. However, often there is still a reluctance to fully embrace uncertainty in decision-making frameworks. Why do we hesitate to quantify, incorporate, and communicate uncertainty? What are the consequences of this reluctance? What opportunities are we missing?
This lecture explores the technical, psychological, and organizational barriers to addressing uncertainty in earth sciences. We will examine real-world examples where underestimating uncertainty led to costly errors, as well as cases where effectively managing uncertainty improved outcomes. By contrasting these scenarios, we will highlight and quantify the critical role uncertainty plays in making informed decisions. Key themes include strategies for quantifying and visualizing uncertainty, integrating uncertainty into predictive models, and fostering a cultural shift toward embracing probabilistic thinking. At the request of the attendees, the focus will be on applications in mining or wildfire management.

E. Past Distinguished Lecturers

View list of previous lecturers.