| Title: |
Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France |
| Authors: |
Joshua, Pwavodi; Marquis, Guy; Maurer, Vincent; Glaas, Carole; Montagud, Anais; Formento, Jean‐luc; Genter, Albert; Darnet, Mathieu |
| Contributors: |
Ecole et Observatoire des Sciences de la Terre (EOST); Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS); Bureau de Recherches Géologiques et Minières (BRGM); Institut Terre Environnement Strasbourg (ITES); École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS); Électricité de Strasbourg Géothermie (ES Géothermie); Compagnie Générale de Géophysique (CGG) |
| Source: |
EISSN: 2993-5210 ; Journal of Geophysical Research. Machine Learning and Computation ; https://brgm.hal.science/hal-04630958 ; Journal of Geophysical Research. Machine Learning and Computation, 2024, 1 (2), ⟨10.1029/2024jh000154⟩ |
| Publisher Information: |
CCSD; American Geophysical Union/Wiley |
| Publication Year: |
2024 |
| Collection: |
Institut national des sciences de l'Univers: HAL-INSU |
| Subject Terms: |
Advancing Interpretable AI; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
| Description: |
International audience ; 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 heterogeneity, such as in the Upper Rhine Graben (URG), where both carbonate and siliciclastic formations are prevalent. The estimation of mineral volumes poses challenges that involve both linear and nonlinear relationships associated with geophysical data. To address this complexity, our methodology strategically integrates the robust insights from standard statistical approaches with three machine learning (ML) algorithms: multi‐layer perceptron, random forest regression, and gradient boosting regression. Furthermore, we propose a new hybrid ensemble model that incorporates a weighted average of multiple ML approaches to predict mineral composition within the Muschelkalk and Buntsandstein formations of the URG. ML techniques for mineral composition prediction in these formations exhibit robust predictive performance. The predicted mineral volumes align closely with quantitative estimates derived from X‐ray diffraction analysis. Additionally, they are in good qualitative agreement with mineral descriptions obtained from cores and cuttings of the Muschelkalk and Buntsandstein formations. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1029/2024jh000154 |
| Availability: |
https://brgm.hal.science/hal-04630958; https://brgm.hal.science/hal-04630958v2/document; https://brgm.hal.science/hal-04630958v2/file/Journal%20of%20Geophysical%20Research%20%20Machine%20Learning%20and%20Computation%20-%202024%20-%20Joshua%20-%20Rock%20Mineral%20Volume%20Inversion%20Using_.pdf; https://doi.org/10.1029/2024jh000154 |
| Rights: |
https://creativecommons.org/licenses/by-nc-nd/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.B94AD604 |
| Database: |
BASE |