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Full-Text Articles in Other Earth Sciences
Discovering Hidden Geothermal Signatures Using Non-Negative Matrix Factorization With Customized K-Means Clustering, V. V. Vesselinov, B. Ahmmed, M. K. Mudunuru, J. D. Pepin, E. R. Burns, D. L. Siler, S. Karra, R. S. Middleton
Discovering Hidden Geothermal Signatures Using Non-Negative Matrix Factorization With Customized K-Means Clustering, V. V. Vesselinov, B. Ahmmed, M. K. Mudunuru, J. D. Pepin, E. R. Burns, D. L. Siler, S. Karra, R. S. Middleton
United States Geological Survey: Staff Publications
Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in …
Discovering Hidden Geothermal Signatures Using Non-Negative Matrix Factorization With Customized K-Means Clustering, V.V. Vesselinov, B. Ahmmed, M.K. Mudunuru, J.D. Pepin, E.R. Burns, D.L. Siler, S. Karra, R.S. Middleton
Discovering Hidden Geothermal Signatures Using Non-Negative Matrix Factorization With Customized K-Means Clustering, V.V. Vesselinov, B. Ahmmed, M.K. Mudunuru, J.D. Pepin, E.R. Burns, D.L. Siler, S. Karra, R.S. Middleton
United States Geological Survey: Staff Publications
Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in …