Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

Stephen C. Brown; Mark F. Coolbaugh; Jacob DeAngelo; James E. Faulds; Michael Fehler; Chen Gu; John H. Queen; Sven Treitel; Connor M. Smith; Eli Mlawsky

USGS · 70217661

Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada

Stephen C. Brown·Mark F. Coolbaugh·Jacob DeAngelo·James E. Faulds·Michael Fehler·Chen Gu·John H. Queen·Sven Treitel·Connor M. Smith·Eli Mlawsky

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Abstract

A study is underway to apply machine learning methods to evaluate natural resource potential. In particular, we are considering the search for blind geothermal systems in Nevada. Beginning with the data and experience from the previous Nevada play fairway analysis project, we are building models in TensorFlow/Keras and gaining experience toward predicting the geothermal resource potential as a probability map. During the first year of this project we have encountered several issues particular to using geological and geophysical data sets with these tools. Through an illustrative example we develop a promising workflow for future use as more data become available and are analyzed.

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Source-reported bounding extent: 35.002085° to 42.002207° latitude; -120.005746° to -114.039648° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeX RIS

Stephen C. Brown, Mark F. Coolbaugh, Jacob DeAngelo, James E. Faulds, Michael Fehler, Chen Gu, John H. Queen, Sven Treitel, Connor M. Smith, Eli Mlawsky. 2020. Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada. https://pubs.usgs.gov/publication/70217661

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