| Title: |
Refining tomography with generative neural networks trained from geodynamics ; Affiner la tomographie avec des réseaux de neurones génératifs entraîné par la géodynamique |
| Authors: |
Santos, Théo; Bodin, Thomas; Soulez, Ferréol; Ricard, Yanick; Capdeville, Yann |
| Contributors: |
Centre de Recherche Astrophysique de Lyon (CRAL); École normale supérieure de Lyon (ENS de Lyon); Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS); Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement (LGL-TPE); Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS); Observatoire des Sciences de l'Univers de Lyon (OSUL); Laboratoire de Planétologie et Géosciences UMR_C 6112 (LPG); Le Mans Université (UM)-Université d'Angers (UA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST); Nantes Université - pôle Sciences et technologie; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie; Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ); ANR-10-LABX-0066,LIO,Lyon Institute of Origins(2010) |
| Source: |
ISSN: 0956-540X. |
| Publisher Information: |
CCSD; Oxford University Press (OUP) |
| Publication Year: |
2024 |
| Collection: |
Université Jean Monnet – Saint-Etienne: HAL |
| Subject Terms: |
Statistical methods; Tomography; Bayesian inference; Inverse theory; Machine learning; [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]; [STAT.AP]Statistics [stat]/Applications [stat.AP]; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
| Description: |
International audience ; Inverse problems occur in many fields of geophysics, wherein surface observations are used to infer the internal structure of the Earth. Given the non-linearity and non-uniqueness inherent in these problems, a standard strategy is to incorporate a priori information regarding the unknown model. Sometimes a solution is obtained by imposing that the inverted model remains close to a reference model and with smooth lateral variations (e.g. a correlation length or a minimal wavelength are imposed). This approach forbids the presence of strong gradients or discontinuities in the recovered model. Admittedly, discontinuities, such as interfaces between layers, or shapes of geological provinces or of geological objects such as slabs can be a priori imposed or even suggested by the data themselves. This is however limited to a small set of possible constraints. For example, it would be very challenging and computationally expensive to perform a tomographic inversion where the subducting slabs would have possible top discontinuities with unknown shapes. The problem seems formidable because one cannot even imagine how to sample the prior space: is each specific slab continuous or broken into different portions having their own interfaces? No continuous set of parameters seems to describe all the possible interfaces that we could consider. To circumvent these questions, we propose to train a Generative Adversarial neural Network (GAN) to generate models from a geologically plausible prior distribution obtained from geodynamic simulations. In a Bayesian framework, a Markov chain Monte Carlo algorithm is used to sample the low-dimensional model space depicting the ensemble of potential geological models. This enables the integration of intricate a priori information, parametrized within a low-dimensional model space conducive to efficient sampling. The application of this approach is demonstrated in the context of a downscaling problem, where the objective is to infer small-scale geological structures ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/gji/ggae240 |
| Availability: |
https://hal.science/hal-04670793; https://hal.science/hal-04670793v1/document; https://hal.science/hal-04670793v1/file/Santos_2024_GJI_ggae240.pdf; https://doi.org/10.1093/gji/ggae240 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.ABC3BFBD |
| Database: |
BASE |