{"@type": "dcat:Dataset", "DOI": "10.15121/1797280", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Jim Moraga", "hasEmail": "mailto:jmoraga@mines.edu"}, "dataQuality": true, "description": "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, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports.\n\nThe appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Geodatabase%20Design.docx", "description": "Geodatabase Design outline for the Geothermal Exploration AI. Databases made using this design include Brady Hot Springs, Desert Peak and Salton Sea. The design outlines the data organization within each database. This document correlates to the design of the \"DOE Geodatabases.zip\" file within this submission.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Geodatabase Design.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Mineral_Mapping_lit.docx", "description": "The Mineral Mapping Literature Report outlines the methods for remote sensing of geothermal sites and the application of these remote sensing methods. Methods include indicators that point to locations of geothermal sites. The report also summarizes the study areas which include the Salton Sea, Brady's Hot Spring, and Desert Peak.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Mineral Mapping Literature Report.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Deformation%20Analysis%20for%20Brady.docx", "description": "The Deformation Analysis Report outlines methods and technology used for identifying land deformations caused by geothermal activity. The report also includes description of the algorithm and methodology used to analyze and monitor the Brady Hot Spring Geothermal Field's deformation.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Deformation Analysis for Brady.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/LST%20Report%2020201026.docx", "description": "The Land Surface Temperature (LST) Report explains the reasoning behind using LST as an input to the Geothermal Exploration AI algorithm. Included in the document is data from test sites, methodology for analysis, and results for Brady, Desert Peak and Salton Sea geothermal sites.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Land Surface Temperature Report.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Morphology%20Litearture.docx", "description": "Morphology Literature Report for Brady Geothermal Field. This report includes information regarding the use of morphological features as geothermal site indicators for the Geothermal Exploration Artificial Intelligence.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Morphology Literature.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Wells_Fault_Seismic_Borders%20Report%2020201028.docx", "description": "Report about the borders for well, fault, and seismic data. Fault density data was used to define analysis boundaries. The report includes fault data for the three sites (Brady, Desert Peak, and Salton Sea) and reasoning for using fault data for the Geothermal Exploration Artificial Intelligence.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Well Fault and Seismic Borders Report.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Geophysical%20analysis%20results.docx", "description": "Report on Geophysical analysis results for Brady Geothermal Field. Includes seismic mapping and fault line mapping.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Geophysical Analysis Results.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Mineral%20Marker%20Maps.docx", "description": "Maps displaying the results of the Mineral Markers analysis. Maps show anomalies product of hydrothermally altered minerals in the area of interest. ", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Mineral Marker Maps.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/MineralMarkers_Zotero_20200108.zip", "description": "Mineral Markers literature references in the Zotero format. Within the zip there are Zotero cache files along with links to each literature reference.", "format": "zip", "mediaType": "application/zip", "title": "Mineral Markers References Zotero Format.zip"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/DOE_GDB.zip", "description": "Geodatabases for Brady, Desert Peak, and Salton Sea Geothermal Sites. Needs o be used with ArcGIS or other geodatabase compatible GIS software.", "format": "zip", "mediaType": "application/zip", "title": "DOE Geodatabases.zip"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/SVM%20Methodology%2020201026.docx", "description": "Support Vector Machine (SVM) applied to the geothermal exploration report. This report explains why  SVM was used with the Geothermal Exploration Artificial Intelligence. Includes information about the methodology of SVM analysis and the data from SVM analysis.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Support Vector Machine Methodology.docx"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1303/Mineral%20Markers%20Methodology%2020201029.docx", "description": "Report with the Methodology used for using Mineral Markers layer in the Geothermal AI. Applies to Brady and Desert Peak Geothermal Areas. Includes information regarding hyperspectral imaging and the processing of that data.", "format": "docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "Mineral Markers Methodology.docx"}], "identifier": "https://data.openei.org/submissions/7421", "issued": "2021-01-08T07:00:00Z", "keyword": ["geothermal", "energy", "artificial intelligence", "hydrothermally altered minerals", "mineral markers", "SVM", "geodatabase", "well", "fault", "seismic", "AI", "border", "Brady", "Desert Peak", "Salton Sea", "land surface temperature", "deformation", "geophysical", "geophysics", "support vector machine", "hyperspectral", "hyperspectral imaging", "California", "Nevada", "EGS", "blind", "blind system", "deep learning", "machine learning", "exploration", "geospatial data", "short wavelength infrared", "SWIR", "database", "anomaly detection", "site detection", "radar", "hydrothermal", "model", "conceptual model", "Zotero", "raw data", "preproccessed", "processed data", "enhanced geothermal system", "engineered geothermal system", "remote sensing", "ArcGis", "GIS", "InSAR", "Morphology", "Morphological", "morphological features", "TIR", "VNIR", "visible near infrared", "thermal infrared", "code", "Python"], "landingPage": "https://gdr.openei.org/submissions/1303", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2022-01-13T15:25:08Z", "programCode": ["019:006"], "projectLead": "Mike Weathers", "projectNumber": "EE0008760", "projectTitle": "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning", "publisher": {"@type": "org:Organization", "name": "Colorado School of Mines"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-119.2167,39.52053456969254],[-118.695850390625,39.52053456969254],[-118.695850390625,39.9883],[-119.2167,39.9883],[-119.2167,39.52053456969254]]]}", "title": "Appendices for Geothermal Exploration Artificial Intelligence Report"}