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 | Duzgun, H. S. | |
dc.contributor.author | Cavur, M. | |
dc.contributor.author | Soydan, H. | |
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.citation | 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.institutionauthor | Çavur, Mahmut | |
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.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 |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 463fefd7-0e68-4479-ad37-0ea65fa6ae01 | |
relation.isAuthorOfPublication.latestForDiscovery | 463fefd7-0e68-4479-ad37-0ea65fa6ae01 |