https://github.com/leouieda/tgif2018

Seminar given at the Department of Earth Sciences of the University of Hawaii at Manoa
https://github.com/leouieda/tgif2018

earth-science fatiando-a-terra geophysics machine-learning python

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

• Host: GitHub
• URL: https://github.com/leouieda/tgif2018
• Owner: leouieda
• License: cc-by-4.0
• Created: 2018-09-18T20:13:40.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-10-13T00:06:26.000Z (almost 6 years ago)
• Last Synced: 2024-06-11T17:29:52.753Z (4 months ago)
• Topics: earth-science, fatiando-a-terra, geophysics, machine-learning, python
• Homepage: https://doi.org/10.6084/m9.figshare.7203344
• Size: 15.1 MB
• Stars: 3
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Machine Learning Lessons for Geophysics

[Leonardo Uieda](http://leouieda.com/)

TGIF Seminar given at the

[Department of Earth Sciences](http://www.soest.hawaii.edu/GG/index.html)

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](https://doi.org/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](flyer/flyer-image.png)

*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




This content is licensed under a Creative Commons Attribution

4.0 International License.