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Geology and Geomodeling×

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

Oct 01, 2021
5.68 MB
Publicly accessible
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Authors
Siler, D. et al United States Geological Survey
geothermalenergynmfkbrady hot springsmachine learningmlbhsnonnegative matrix factorization k-meanshydrothermalbradyk-meansclusteringnonnegative matrix factorizationmatrix factorizationgeothermalcloudsmarttensorsunsupervised3d well data3d geologic mapgeologic structurefaultsstressgeologycharacterizationgeologic modelproductioncode

Sentinel-1 Input Data for PSInSAR Analysis

Apr 29, 2021
305.43 GB
Publicly accessible
Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a l...
Authors
Moraga, J. Colorado School of Mines
geothermalenergyinsarpsinsarsentinel-1synthetic aperture radarsatellite imagingaiartificial intelligencedeep learningmachine learningaerial photographyremote sensingsite detectionanomaly detectioninput datageologydisplacementsubsoilphenomenaprocessed dataradarimaginginterferometricanalysissoil deformationgeospatial data