Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 2
• Main Authors: Joshua, Pwavodi, Marquis, Guy, Maurer, Vincent, Glaas, Carole, Montagud, Anais, Formento, Jean‐Luc, Genter, Albert, Darnet, Mathieu
• Format: Journal Article
• Language: English
• Published: American Geophysical Union/Wiley 01.06.2024
• Subjects: deep neural networks; Earth Sciences; geothermal energy; machine learning algorithm; mineral volume; petrophysical properties; Sciences of the Universe; upper Rhine graben; Advancing Interpretable AI

Accurately determining the mineralogical composition of rocks is essential for precise assessments of key petrophysical properties like effective porosity, water saturation, clay volume, and permeability. Mineral volume inversion is particularly critical in geological contexts characterized by heter...