210: Unveiling Seismic Secrets - Inside Machine Learning's Black Box

"It's ​not ​like ​machine ​learning ​will ​solve ​all ​the ​problems. ​It's ​not ​a ​magical ​tool."

David Lubo-Robles highlights his award-winning paper that utilized novel machine learning methods to enhance interpretability in seismic volume data from the Gulf of Mexico.

Discover the power of two open-source tools - SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) - in enhancing the interpretability of machine models. David takes us through his team's research that garnered an Honorable Mention for Best Paper in Interpretation. He also shares his journey into geophysics, driven by a fascination with the Earth and energy discovery.

Listeners will gain insight into the critical role of input quality in machine learning outcomes, the importance of balancing datasets, and the necessity of geoscientific validation. The episode also addresses common misconceptions about machine learning in geophysics, emphasizing the need for critical thinking and geological knowledge to apply these advanced techniques.