The Geothermal Artificial Intelligence for Geothermal Exploration

dc.authoridCavur, Mahmut/0000-0002-1256-2700
dc.authoridMoraga, Jim/0000-0003-4483-9900
dc.authoridSoydan, Hilal/0000-0001-9877-2356
dc.authorwosidCavur, Mahmut/AEB-6168-2022
dc.contributor.authorMoraga, J.
dc.contributor.authorDuzgun, H. S.
dc.contributor.authorCavur, M.
dc.contributor.authorSoydan, H.
dc.date.accessioned2023-10-19T15:12:15Z
dc.date.available2023-10-19T15:12:15Z
dc.date.issued2022
dc.department-temp[Moraga, J.; Soydan, H.] Colorado Sch Mines, Dept Min Engn, 1610 Illinois St, Golden, CO 80401 USA; [Duzgun, H. S.] Colorado Sch Mines, Fred Ban field Distinguished Endowed Chair & Prof, Min Engn, Golden, CO 80401 USA; [Cavur, M.] Kadir Has Univ, Management Informat Syst Dept, TR-34083 Istanbul, Turkeyen_US
dc.description.abstractExploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). We processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal/non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.(c) 2022 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipUS Department of Energy [DE-EE0008760]en_US
dc.description.sponsorshipThis project has been funded by the US Department of Energy grant number DE-EE0008760, made use of the High-Performance Computing (HPC) and other facilities at the Colorado School of Mines, and benefitted from the availability of satellite data from ESA's Sentinel and NASA's LANDSAT projects, the geological information from the Nevada Bureau of Mines and Geology, and USGS. Additionally, we would like to thank professor Ge Jin, Assistant Professor of Geophysics at Colorado School of Mines, for his input in the selection of layers for the AI.en_US
dc.identifier.citation9
dc.identifier.doi10.1016/j.renene.2022.04.113en_US
dc.identifier.endpage149en_US
dc.identifier.issn0960-1481
dc.identifier.issn1879-0682
dc.identifier.scopus2-s2.0-85129493624en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage134en_US
dc.identifier.urihttps://doi.org/10.1016/j.renene.2022.04.113
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5389
dc.identifier.volume192en_US
dc.identifier.wosWOS:000798630200002en_US
dc.identifier.wosqualityQ1
dc.institutionauthorÇavur, Mahmut
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofRenewable Energyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGeophysical MethodsEn_Us
dc.subjectEnergyEn_Us
dc.subjectSystemsEn_Us
dc.subjectDepositsEn_Us
dc.subjectDesertEn_Us
dc.subjectGeothermal explorationen_US
dc.subjectGeophysical Methods
dc.subjectMachine learningen_US
dc.subjectEnergy
dc.subjectArti ficial intelligenceen_US
dc.subjectSystems
dc.subjectAutomated labelingen_US
dc.subjectDeposits
dc.subjectGeophysics for explorationen_US
dc.subjectDesert
dc.subjectGeothermal energyen_US
dc.titleThe Geothermal Artificial Intelligence for Geothermal Explorationen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublication463fefd7-0e68-4479-ad37-0ea65fa6ae01
relation.isAuthorOfPublication.latestForDiscovery463fefd7-0e68-4479-ad37-0ea65fa6ae01

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