Improving geothermal resource assessment: a data-driven approach to chemical geothermometry using deep learning.
(2025)
Presentation / Conference Contribution
ALGAIAR, M. 2025. Improving geothermal resource assessment: a data-driven approach to chemical geothermometry using deep learning. Presented at the 2025 SEG (Society of Exploration Geophysicists) Net-zero emissions workshop, 23-25 June 2025, [virtual event].
This study presents a deep learning model trained on a dataset of 674 water samples from Nevada to predict geothermal reservoir temperatures. The model outperforms traditional geothermometers and other machine learning models, achieving high accuracy... Read More about Improving geothermal resource assessment: a data-driven approach to chemical geothermometry using deep learning..