Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

Ahmmed, Bulbul; Vesselinov, Velimir Valentinov

doi:10.1016/j.renene.2022.08.024

Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

Journal Article · Sat Aug 06 00:00:00 EDT 2022 · Renewable Energy

DOI:https://doi.org/10.1016/j.renene.2022.08.024· OSTI ID:1881824

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: EnviTrace, LLC, Santa Fe, NM (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office

Grant/Contract Number:: 89233218CNA000001; SC0022493; SC0022697

OSTI ID:: 1881824

Alternate ID(s):: OSTI ID: 1895275

Report Number(s):: LA-UR-22-22184

Journal Information:: Renewable Energy, Journal Name: Renewable Energy Vol. 197; ISSN 0960-1481

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Machine learning and shallow groundwater chemistry to identify geothermal prospects in the Great Basin, USA

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