Refining tomography with generative neural networks trained from geodynamics - Archive ouverte HAL
Article Dans Une Revue Geophysical Journal International Année : 2024

Refining tomography with generative neural networks trained from geodynamics

Affiner la tomographie avec des réseaux de neurones génératifs entraîné par la géodynamique

Résumé

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 from a smooth seismic tomographic image.
Fichier principal
Vignette du fichier
Santos_2024_GJI_ggae240.pdf (6.16 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04670793 , version 1 (13-08-2024)

Identifiants

Citer

T Santos, T Bodin, F Soulez, Y Ricard, Yann Capdeville. Refining tomography with generative neural networks trained from geodynamics. Geophysical Journal International, 2024, 238 (3), pp.1676 - 1695. ⟨10.1093/gji/ggae240⟩. ⟨hal-04670793⟩
101 Consultations
12 Téléchargements

Altmetric

Partager

More