Bayes in Grenoble: Julien Thurin

on the July 2, 2019

July, 2nd at 2:00 pm
Julien Thurin (ISTerre) will talk about "Uncertainty estimation in seismic tomography with ensemble Data Assimilation". The event will take place on July, 2 at 2:00 pm at Imag building.
Bayes in Grenoble is a new reading group on Bayesian statistical methods. The purpose of this group is to gather the Grenoble Bayesian community on a monthly basis around noteworthy papers. Those can equally focus on theory, methods, learning, applications, computations, etc, and can be seminal papers as well as recent preprints, as soon as they relate to Bayes.

The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk.

React on social media: #BIGseminar

2 July 2019, Julien Thurin (ISTerre)

Uncertainty estimation in seismic tomography with ensemble Data Assimilation

Full Waveform Inversion (FWI) seek to estimate subsurface properties, based on ill-posed and computationally challenging inverse problem-solving. This methodology involves minimizing a data-misfit between synthetic wavefield computed from a prior subsurface estimate, and sparse indirect recorded waveform data, generally located at the surface. Although it is possible to come down to a reasonable data-fit, obtaining high-resolution subsurface models, the intrinsic properties of FWI make the process complicated: based on quasi-Newton local optimization schemes, making any claims on the validity of a unique solution is an unsound exercise. Therefore, it is crucial that FWI depart from the deterministic-mono solution frame, and move toward a more statistical approach, integrating uncertainty quantification at the heart of its processes. To that extent, we propose a new methodological development to recast our problem in the Bayesian inference framework, by borrowing and applying ensemble methods coming from the Data Assimilation (DA) community. We investigated uses of such methodologies on synthetic and real data tests, by applying a combination of quasi-newton FWI optimization scheme, and well known, Ensemble Transform Kalman Filter from the DA community. On top of proposing an ETKF-FWI scheme to estimate uncertainty, we also study the importance of prior knowledge and the challenges of ensemble representativity for high-dimensional state estimate problem such as FWI.

Published on June 25, 2019

Practical informations


Room 106
IMAG building