Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA. (September 2022)

• Record Type: Journal Article
• Authors: Ahmmed, Bulbul; Vesselinov, Velimir V.
• Is Part Of: Renewable energy. Volume 197(2022)
• Page Start: 1034
• Page End: 1048
• Publication Date: 2022-09
• Subjects: Play fairway analysis -- Geothermal resources -- Machine learning -- Geochemistry; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.08.024
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This study discovers various geothermal prospects in the Great Basin, USA based on shallow groundwater chemical (geochemical) data. The geochemical data are expected to include hidden (latent) information that is a proxy for geothermal prospectivity. We processed the sparse geochemical data in the Great Basin at 14, 341 locations including 18 attributes. Next, a non-negative matrix factorization with customized k-means clustering is applied to the geochemical data matrix that automatically finds three hidden geothermal signatures representing modestly, moderately, and highly confident geothermal prospects. The algorithm also evaluated the probability of occurrence of these types of resources through the studied region. There is a consistency between regional geothermal prospectivity as estimated by our ML methodology and the traditional play fairway analysis conducted over a portion of the study area. We also identify the dominant data attributes associated with each signature. Finally, our ML analyses allow us to reconstruct attributes from sparse into continuous over the study domain. The predicted continuous attributes can be used for future detailed geothermal explorations in the Great Basin. Highlights: Analyzed highly sparse 18 shallow water chemistry attributes at 14, 341 locations in the Great Basin. An unsupervised machine learning tool called non-negative matrix factorization with k-means clustering was used to analyze the data. Discovered modestly, moderately, and highly confident geothermal prospects. Identified key attributes defining each type of geothermal prospect. Converted sparse to continuous data distribution.