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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
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 Digital Object Identifier Catalog
USGS Organization Geology, Minerals, Energy, and Geophysics Science Center