Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

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Bibliographic Details
Published in:Geothermics Vol. 106; p. 102576
Main Authors: Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E.R., Siler, D.L., Karra, S., Middleton, R.S.
Format: Journal Article
Language:English
Published: United States Elsevier 01.12.2022
Subjects:
Custom k-means clustering
Feature extraction
GEOTHERMAL ENERGY
Hidden geothermal resources
Hidden signatures
Non-negative matrix factorization
Unsupervised machine learning
ISSN:0375-6505, 1879-3576
Online Access:Get full text
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Description
Bibliography:USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
USDOE Office of Energy Efficiency and Renewable Energy (EERE). Geothermal Technologies Office
USDOE Office of Science (SC), Engineering & Technology. Office of Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs
USDOE National Nuclear Security Administration (NNSA)
AC05-76RL01830; 89233218CNA000001; SC0021570; SC0022697; SC0022493
PNNL-SA-167650; LA-UR-21-23829
ISSN:0375-6505
1879-3576
DOI:10.1016/j.geothermics.2022.102576