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Machine Learning Lessons for Geophysics

Leonardo Uieda

TGIF Seminar given at the Department of Earth Sciences of the University of Hawaii at Manoa on October 12, 2018.

Info
Time 3:30 pm - Friday - October 12, 2018
Room 723
Slides doi:10.6084/m9.figshare.7203344

Abstract

Machine learning is the new trend sweeping across the Earth Sciences. From the oil and gas industry to oceanography, these algorithms are being trained to solve previously unsolvable (or extremely tedious) problems. But what exactly is "machine learning" and what can be done with it? In this talk, I will present a brief high-level overview of some of the core concepts and interesting applications of machine learning methods to geoscience data. I will also explore some of the best practices and techniques that can be applied to geophysical inversion and interpolation methods, including a new method for gridding 3-component GPS data.

Sample application of ML in geophysics Example machine learning applications: (a) predicting facies from well logs (b) automatically tuned gridding of GPS velocities.


The facies prediction example (a) by Hall (2016; https://doi.org/10.1190/tle35100906.1) is licensed Creative Commons Attribution.

Notes

The slides were made in Google Docs. Fonts are Roboto Mono, Aldrich, and Barlow from Google Fonts.

License

Creative Commons License
This content is licensed under a Creative Commons Attribution 4.0 International License.

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Seminar given at the Department of Earth Sciences of the University of Hawaii at Manoa

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