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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

Programs and Code for Subsurface and MultiSite Geothermal Exploration Artificial Intelligence

Sep 01, 2023
337.85 kB
Awaiting release
This dataset provides Python scripts supporting both subsurface and surface geothermal exploration AI models developed for the project "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning." It includes two main components: (1) scripts fo...
Authors
Demir, E. and Duzgun, S. Colorado School of Mines
geothermalenergygeothermal explorationsubsurfacesurfacemulti-siteartificial intelligenceaimachine learningmldeep learninghyperspectral imaging3d modeling2d classificationvoxel dataraster dataland surface temperaturemineral markersk-meansdata processingtensorflowpythoncodegpuexploration

Programs and Code for Geothermal Exploration Artificial Intelligence

Apr 27, 2021
710.72 kB
Publicly accessible
The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including...
Authors
Moraga, J. Colorado School of Mines
geothermalenergycodershell scriptsgeothermal aimachine learningself organizing mapk-meanspythonaiartificial intelligencedeep learningexplorationgeothermal explorationremote sensingblindsite detectionlstland surface temperaturenumpyrastertensorflowk meananomaly detectionlandsat adr lstsbatchslurmshell

Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

Sep 01, 2020
109.94 MB
Publicly accessible
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: brady_som_output.gri, brady_som_output.grd, brady_som_output.* desert_som_output.gri, desert_som_output.grd, desert_som_outpu...
Authors
Moraga, J. et al Colorado School of Mines
geothermalenergygeothermal explorationhydrothermal mineral alterationsland surface temperaturefault densitypsinsarsubsidenceupliftbrady hot springsdesert peaknevadaconvolutional neural networkfallonmachine learningmodelhydrothermalmineraltemperaturerastergeospatial datageotifftraining datatraining dataset

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

Oct 01, 2021
7.12 MB
Publicly accessible
In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydr...
Authors
Siler, D. and Pepin, J. United States Geological Survey
geothermalenergypca3d geologic modelgeologic modelgeologycharacterizationmachine learningmlbhsbrady hot springsprincipal component analysisproductionstressfaultsrbradyhydrothermalgeologic structureunsupervised3d well datacodegeothermicgeophysics

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