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EGS Collab: 3D Geophysical Model Around the Sanford Underground Research Facility

Feb 06, 2019
14.13 MB
Publicly accessible
This package contains data associated with a proceedings paper (linked below) submitted to the 44th Workshop on Geothermal Reservoir Engineering. The Geophysical Model text file contains density, P and S-wave seismic speeds on a 3D grid. The file has six columns and provides latit...
Authors
Chai, C. et al Lawrence Berkeley National Laboratory
3d seismic structurejoint inversionblack hillssurfegs collab3dseismicmodelingmodelgeophysicsgeophysicaldensityvelocityspeedsanford underground research facilityinversionreservoir engineeringegscollabapiinteractivedata1dprofilemapvisualizationdepth2dp-waves-wave

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

Apr 20, 2020
6.74 MB
Publicly accessible
This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-w...
Authors
Chai, C. et al Oak Ridge National Laboratory
geothermalenergyegs collab3d seismic structuretransfer learningdeep learningmachine learninginteractivesurfp-waves-wavemicroseismic catalogseismic tomographyinteractive visualizationgeophysicsmodelingvelocitymodelmicroseismicitycatalogtransfer-learningdouble-difference tomography3dseismicmeqprocessed datageospatial data

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Apr 04, 2022
1.04 GB
Publicly accessible
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...
Authors
Ahmmed, B. Stanford University
geothermalenergymachine learningartificial intelligenceaiexplorationmodelmodelingprocessed datatraining datatraining datasetremote sensinghidden geothermal resourcesresource detectiondiscoverydevelopmentresourceneural networkprediction