USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics Data
January 12, 2022
This package contains gravity and magnetics data and products generated for the Nevada Machine Learning (NVML) project (DE-FOA-0001956). Data products contained in this release consist of grids and vector data. Grids include: primary anomaly maps (isostatic and PSG), match-filtered maps, horizontal gradient (HG) maps, confidence maps, and maps showing density of specific key structural features. The vector data in this release include the gravity stations, HGM of gravity and magnetics, ?generalized? lineations for gravity and magnetics, gravity and magnetic lineation terminations and intersections, and ?well-constrained? HGM saddles.
Citation Information
Publication Year | 2022 |
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Title | USGS Contributions to the Nevada Geothermal Machine Learning Project (DE-FOA-0001956): Geophysics Data |
DOI | 10.5066/P9676O1M |
Authors | Jacob DeAngelo, Jonathan M Glen, Tait E Earney, Branden J Dean, Laurie A Zielinski, Brent T. Ritzinger |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Geology, Minerals, Energy, and Geophysics Science Center |
Rights | This work is marked with CC0 1.0 Universal |