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Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
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6 Resources
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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...
Ahmmed, B. Stanford University
Apr 04, 2022
3 Resources
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3 Resources
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Wind Speed Dataset with Observation And Copula-based Bias Correction and Multi-Model Fusion at 23 U.S. Locations
This dataset provides two related products of bias-corrected hourly wind speed at 23 U.S. sites spanning coastal, inland, mountainous, and plains regions, designed for energy applications and wind-related research. Physics-based models carry systematic biases relative to observati...
Zhang, W. et al National Laboratory of the Rockies (NLR)
Jun 15, 2026
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4 Resources
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