Sebnem Duzgun, professor and Fred Banfield Distinguished Endowed Chair of Mining Engineering at Colorado School of Mines, has been awarded funding from the U.S. Department of Energy to apply new machine learning techniques to geothermal exploration.
Duzgun will receive $500,000 over 18 months for her project, which was one of 10 nationwide recently selected for DOE funding.
Specifically, Duzgun and her team plan to use machine learning techniques to analyze remote-sensing hyperspectral images, with the goal of developing a way to identify the presence of geothermal resources based on surface characteristics.
To do that, the researchers will develop a new methodology to automatically label data from hyperspectral images using existing geological, geophysical and drill hole data and use this data in developing convolutional neural networks (CNN) as deep learning models (DLM) that can predict the presence of geothermal resources based on surface characteristics with high accuracy.
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From the Global Geothermal News archives:
From the Global Geothermal News archives:
- Friday, May 3, 2019 - USA: DOE Announces Funding for Geothermal Exploration Machine Learning Projects
- Monday, April 22, 2019 - Science & Technology: Using Machine Learning to Better Predict Seismic Activity During Geothermal Exploration
- Thursday, July 19, 2018 - USA: $3.6 Million Available for Projects Involving Machine Learning for Geothermal Energy
- Wednesday, May 23, 2018 - Science & Technology: Algorithms Pick Out Hidden Signals that Could Boost Geothermal Energy Production
- Tuesday, May 8, 2018 - USA: GTO Seeks Input on Machine Learning for Geothermal Energy and the Geosciences
