Work

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

Year: 2022

Type: article

Source: Geothermics

DOI: 10.1016/j.geothermics.2022.102576

Authors: V.V. Vesselinov, B. Ahmmed, M.K. Mudunuru, J.D. Pepin, E.R. Burns, D.L. Siler, S. Karra, R.S. Middleton

Institutions: Carbon Management Canada, Carbon Solutions (United States), Los Alamos National Laboratory, Pacific Northwest National Laboratory, United States Geological Survey, Environmental Molecular Sciences Laboratory

Language: English

Topic: Geothermal Energy Systems and Applications

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: bronze

Funders: U.S. Department of Energy, Small Business Innovation Research, U.S. Geological Survey, Office of Energy Efficiency and Renewable Energy, National Nuclear Security Administration

Awards: G1017065088, G2194767069, G5343514568, PHYSICS-INFORMED AI/ML OF GEOCHEMICAL DATASETS FOR CHARACTERIZATION, PARAMETERIZATION, AND PREDICTION OF CONTAMINANT TRANSPORT AND REMEDIATION PROCESSES, GT-COM: EQUITABLE AND INCLUSIVE TOOL FOR COMMUNITY-BASED GEOTHERMAL DEVELOPMENT

OpenAlex ID: W4297510491

FWCI: 0.658

Cites: 63

Cited by: 19

Related to: 10