The Geothermal Artificial Intelligence for Geothermal Exploration

dc.authorid Cavur, Mahmut/0000-0002-1256-2700
dc.authorid Moraga, Jim/0000-0003-4483-9900
dc.authorid Soydan, Hilal/0000-0001-9877-2356
dc.authorwosid Cavur, Mahmut/AEB-6168-2022
dc.contributor.author Moraga, J.
dc.contributor.author Çavur, Mahmut
dc.contributor.author Duzgun, H. S.
dc.contributor.author Cavur, M.
dc.contributor.author Soydan, H.
dc.contributor.other Management Information Systems
dc.date.accessioned 2023-10-19T15:12:15Z
dc.date.available 2023-10-19T15:12:15Z
dc.date.issued 2022
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, Turkey en_US
dc.description.abstract Exploration 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.sponsorship US Department of Energy [DE-EE0008760] en_US
dc.description.sponsorship This 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.citationcount 9
dc.identifier.doi 10.1016/j.renene.2022.04.113 en_US
dc.identifier.endpage 149 en_US
dc.identifier.issn 0960-1481
dc.identifier.issn 1879-0682
dc.identifier.scopus 2-s2.0-85129493624 en_US
dc.identifier.scopusquality Q1
dc.identifier.startpage 134 en_US
dc.identifier.uri https://doi.org/10.1016/j.renene.2022.04.113
dc.identifier.uri https://hdl.handle.net/20.500.12469/5389
dc.identifier.volume 192 en_US
dc.identifier.wos WOS:000798630200002 en_US
dc.identifier.wosquality Q1
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Pergamon-Elsevier Science Ltd en_US
dc.relation.ispartof Renewable Energy en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 32
dc.subject Geophysical Methods En_Us
dc.subject Energy En_Us
dc.subject Systems En_Us
dc.subject Deposits En_Us
dc.subject Desert En_Us
dc.subject Geothermal exploration en_US
dc.subject Geophysical Methods
dc.subject Machine learning en_US
dc.subject Energy
dc.subject Arti ficial intelligence en_US
dc.subject Systems
dc.subject Automated labeling en_US
dc.subject Deposits
dc.subject Geophysics for exploration en_US
dc.subject Desert
dc.subject Geothermal energy en_US
dc.title The Geothermal Artificial Intelligence for Geothermal Exploration en_US
dc.type Article en_US
dc.wos.citedbyCount 26
dspace.entity.type Publication
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