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GIS Resource Compilation Map Package Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Jun 01, 2021
831.16 MB
Publicly accessible
This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups incl...
Authors
Brown, S. et al Nevada Bureau of Mines and Geology
geothermalenergynevadamachine learningmap packagegispcanmfbnnannelmgeochemistrygeophysicsheat flowslip and dilationstructureplay fairwaypfaexplorationcharacterizationgreat basindlipdilationgeodatabasehydrothermaldatamodelsprocessed datapaleo-geothermal featurestest sittessupervisedunsupervisedcultural

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

May 14, 2021
6.01 MB
Publicly accessible
Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...
Authors
Marone, C. et al Pennsylvania State University
geothermalenergymatlabgeophysicsseismiccodeprocessed datamicroseismicityaiartificial intelligencedeep learningmachine learningacousticsacoustic emissionsseismic forcastingseismic predictionfaultfault propertiesshear stresstime to failureexperimentexperimental datalab databiaxial shear experimentbiaxial shear apparatusfriction

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

Appendices for Geothermal Exploration Artificial Intelligence Report

Jan 08, 2021
2.76 GB
Publicly accessible
The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...
Authors
Duzgun, H. et al Colorado School of Mines
geothermalenergyartificial intelligencehydrothermally altered mineralsmineral markerssvmgeodatabasewellfaultseismicaiborderbradydesert peaksalton sealand surface temperaturedeformationgeophysicalgeophysicssupport vector machinehyperspectralhyperspectral imagingcalifornianevadaegsblindblind systemdeep learningmachine learningexplorationgeospatial datashort wavelength infraredswirdatabaseanomaly detectionsite detectionradarhydrothermalmodelconceptual modelzoteroraw datapreproccessedprocessed dataenhanced geothermal systemengineered geothermal systemremote sensingarcgisgisinsarmorphologymorphologicalmorphological featurestirvnirvisible near infraredthermal infraredcodepython