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

Uložené v:
Podrobná bibliografia
Vydané v:Geothermics Ročník 106; s. 102576
Hlavní autori: Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E.R., Siler, D.L., Karra, S., Middleton, R.S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier 01.12.2022
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Bibliografia: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