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EAGE AI Committee - Newsletter on AI, ML and all things Digitalization #8/2022

October 2022

Welcome to the latest newsletter prepared by the EAGE A.I. Committee this year. As a group of EAGE members and volunteers they help you navigate the digital world and find the bits that are most relevant to geoscientists.

You are welcome to join EAGE or renew your membership to support the work of the EAGE A.I. Community and access all the benefits offered by the Association.

EAGE Membership Benefits: Join or Renew

Curious to know all EAGE is doing for the digital transformation? 

Visit the EAGE Digitalization Hub



Diffusion Models

Credits: Paul Zwartjes

What: Some developments in A.I. are simply amazing. Like diffusion models. The maths are intriguing, the result are spectacular. They take image generation to a whole new level entirely and put Generative-Adversial-Networks (GANs) and Variational Auto-Encoders in the category of “yeah, we tried those ideas too”. Several institutes are working on combining text interpretation and image generation into network that can generate text and videos based on written words and you can bet that this is a game changer for the digital design industry.

A really short explanation is that diffusion probabilistic models are parameterized Markov chains models trained to gradually denoise data. This exciting new development already has its first application in geophysics, with an application to seismic data processing problems by Durall et al.



Probabilistic Machine Learning: Advanced Topics

Credits: Lukas Mosser

What: As a follow up to the excellent first edition Probabilistic Machine Learning, Kevin Murphy has released a second volume titled “Probabilistic Machine Learning: Advanced Topics”

This edition will cover fundamentals, inference, prediction, generation, discovery and action as key topic areas. For the engaged practitioner you will find many relevant concepts such as Bayesian statistics, Markov Chain Monte Carlo, Gaussian Processes, Interpretability, Generative Models, Causality and much, much more.

The full edition is set to release in early 2023 and is available as a draft.



RedFlag – a new python package for machine learning input quality checks

Credits: Ashley Russell

What: The famous Matt Hall, the creator behind softwareunderground, has a new python package that aims to catch data preparation problems automatically to try and eliminate data biases in machine learning models.  The package is newly released and seeking contributions – so if you are interested in helping out head over to Github:

Additionally a full blog post with examples can be found on Matt’s blog:




Discover EAGE Learning Resources on A.I. and machine learning

EAGE Digitalization Conference 2023

This newsletter is edited by the EAGE A.I. Committee.

NameCompany / InstitutionCountry
Ashley RussellEquinorNorway
Jan H. van de MortelIndependentNetherlands
George GhonCapgeminiNorway
Cédric M. JohnQueen Mary University of LondonUnited Kingdom
Lukas Mosser Earth Science AnalyticsAustria
Oleg OvcharenkoNVIDIAUnited Arab Emirates
Nicole GrobysWintershall DeaGermany
Robert FergusonUniversity of CalgaryCanada
Ruslan MiftakhovGeoplatUnited Kingdom
Surender ManralSchlumbergerNorway

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Siddharth Misra


Prof Dr Siddharth Misra’s research focuses on improving subsurface characterization and prospect evaluation for the exploration of hydrocarbons, minerals and water resources.

His major contribution is in the theory of electromagnetic responses of geological formations to various charge polarization phenomena. The theory has enabled him to introduce a multi-frequency electromagnetic log-inversion technique to remove dielectric effects for improved estimation of hydrocarbon pore volume.


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