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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.
Study Area
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Machine learning for natural resource assessment: An application to the blind geothermal systems of Nevada |
| Volume | 44 |
| Year Published | 2020 |
| Language | English |
| Publisher | Geothermal Resources Council |
| Contributing office(s) | Geology, Minerals, Energy, and Geophysics Science Center |
| Description | 13 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Conference publication |
| Larger Work Title | Geothermal Resources Council Transactions |
| First page | 920 |
| Last page | 932 |
| Country | United States |
| State | Nevada |
| Google Analytic Metrics | Metrics page |