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

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Geothermics Jg. 106; S. 102576
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: United States Elsevier 01.12.2022
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Bibliographie: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