Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1007/s12665-024-12066-3 |
Lizenz  |
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| Titel (primär) | Geochemistry and machine learning: methods and benchmarking |
| Autor | Prasianakis, N.I.; Laloy, E.; Jacques, D.; Meeussen, J.C.L.; Miron, G.D.; Kulik, D.A.; Idiart, A.; Demirer, E.; Coene, E.; Cochepin, B.; Leconte, M.; Savino, M.E.; Samper-Pilar, J.; De Lucia, M.; Churakov, S.V.; Kolditz, O.
; Yang, C.; Samper, J.; Claret, F. |
| Quelle | Environmental Earth Sciences |
| Erscheinungsjahr | 2025 |
| Department | ENVINF |
| Band/Volume | 84 |
| Heft | 5 |
| Seite von | art. 121 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Daten-/Softwarelinks | https://doi.org/10.5281/zenodo.14904784 |
| Supplements | https://static-content.springer.com/esm/art%3A10.1007%2Fs12665-024-12066-3/MediaObjects/12665_2024_12066_MOESM1_ESM.pdf |
| Keywords | Machine learning; Geochemistry; Nuclear waste management; Numerical methods |
| 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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30553 |
| Prasianakis, N.I., Laloy, E., Jacques, D., Meeussen, J.C.L., Miron, G.D., Kulik, D.A., Idiart, A., Demirer, E., Coene, E., Cochepin, B., Leconte, M., Savino, M.E., Samper-Pilar, J., De Lucia, M., Churakov, S.V., Kolditz, O., Yang, C., Samper, J., Claret, F. (2025): Geochemistry and machine learning: methods and benchmarking Environ. Earth Sci. 84 (5), art. 121 10.1007/s12665-024-12066-3 |
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