Geochemistry and machine learning: methods and benchmarking

Abstract Thanks to the recent progress in numerical methods and computer technology, the application fields of artificial intelligence (AI) and machine learning methods (ML) are growing at a very fast pace. The field of geochemistry for nuclear waste management has recently started using ML for the acceleration of numerical simulations of reactive transport processes, for the improvement of multiscale and multiphysics couplings efficiency, and for uncertainty quantification and sensitivity analysis. Several case studies indicate that the use of ML based approaches brings an overall acceleration of geochemical and reactive transport simulations between one and four orders of magnitude. This paper presents a benchmarking exercise that aims at providing a set of reference data and models for developing and applying ML techniques for geochemical and reactive transport simulations. Several well-known geochemical speciation codes are used to generate systematically a consistent set of high-quality chemical equilibrium data, to be used as input for the training of several ML methods. Two benchmarks are formulated, each with multiple levels of gradually increasing degree of complexity. The first benchmark focuses on cement chemistry, while the second one considers uranium sorption on a clay mineral. The performance of different ML techniques is then evaluated in terms of their numerical efficiency and accuracy. A speedup of several orders of magnitude is observed. The benefits and the limitations of different ML based techniques are then analysed and highlighted.

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Publication Details

Journal
Environmental Earth Sciences
Published
2025-02-18
DOI
https://doi.org/10.1007/s12665-024-12066-3
Citations
10
Primary Topic
Mineral Processing and Grinding
Type
article
Field-Weighted Citation Impact
7.13

Funders

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Geochemistry and machine learning: methods and benchmarking

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10 citations
Environmental Earth Sciences
Mineral Processing and Grinding
7.13
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Geochemistry and machine learning: methods and benchmarking

Marco De Lucia, Sergey V. Churakov, Nikolaos I. Prasianakis, Diederik Jacques, J. Samper, Benoît Cochepin, Changbing Yang, Eric Laloy, J.C.L. Meeussen, Mary Savino, Emilie Coene, Francis Claret, G. D. Miron, M. Leconte, Ersan Demirer, Olaf Kolditz, Dmitrii A. Kulik, Andrés Idiart, Javier Samper-Pilar
article en
10 citations

Abstract

Abstract Thanks to the recent progress in numerical methods and computer technology, the application fields of artificial intelligence (AI) and machine learning methods (ML) are growing at a very fast pace. The field of geochemistry for nuclear waste management has recently started using ML for the acceleration of numerical simulations of reactive transport processes, for the improvement of multiscale and multiphysics couplings efficiency, and for uncertainty quantification and sensitivity analysis. Several case studies indicate that the use of ML based approaches brings an overall acceleration of geochemical and reactive transport simulations between one and four orders of magnitude. This paper presents a benchmarking exercise that aims at providing a set of reference data and models for developing and applying ML techniques for geochemical and reactive transport simulations. Several well-known geochemical speciation codes are used to generate systematically a consistent set of high-quality chemical equilibrium data, to be used as input for the training of several ML methods. Two benchmarks are formulated, each with multiple levels of gradually increasing degree of complexity. The first benchmark focuses on cement chemistry, while the second one considers uranium sorption on a clay mineral. The performance of different ML techniques is then evaluated in terms of their numerical efficiency and accuracy. A speedup of several orders of magnitude is observed. The benefits and the limitations of different ML based techniques are then analysed and highlighted.

Environmental Earth SciencesVol. 84(5)
Universidade da Coruña (ES), University of Bern (CH), Helmholtz Centre for Environmental Research (DE), AgroParisTech (FR), Université Paris-Saclay (FR), Paul Scherrer Institute (CH), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Amphos 21 (Spain) (ES), Belgian Nuclear Research Centre (BE), Laboratoire d'Énergétique Moléculaire et Macroscopique, Combustion (FR), Institute of Geological Sciences (AM), GFZ Helmholtz Centre for Geosciences (DE), Bureau de Recherches Géologiques et Minières (FR), Nuclear Research and Consultancy Group (NL), Université Jean Monnet (FR)
Helmholtz-Gemeinschaft
Openalex Percentile: Top 3%
Mineral Processing and Grinding
7.13
DOIOpen Access PDFOpenAlex
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