Uncertainty Quantification in Full Waveform Tomography with Ensemble Data-Assimilation

Session: Full-Waveform Inversion: Recent Advances and Applications
Type: Oral
Date: 4/29/2020
Time: 02:45 PM
Room: 120 + 130
Description: 

Uncertainty estimation and quality control are critically missing in most geophysical tomographic applications. The few solutions to cope with that issue are often left out in practical applications when these grow in scale and involve complex modeling. We present a joint full-waveform inversion and ensemble data assimilation scheme, allowing local Bayesian estimation of the solution that brings uncertainty estimation to the tomographic problem. This original methodology relies on a deterministic square root ensemble Kalman filter commonly used in the data assimilation community: the ensemble transform Kalman filter. Combined with a 2D visco-acoustic frequency-domain full-waveform inversion scheme, the resulting method allows access to a low-rank approximation of the posterior covariance matrix of the solution. It yields uncertainty information through an ensemble-representation, that can conveniently be mapped across the physical domain for visualization and interpretation. We present the combination of ensemble transform Kalman filter with full-waveform inversion along with the scheme design and algorithmic details that lead to our mixed application. Both synthetic and field-data results are considered, along with the biases that are associated with the limited rank ensemble representation. Finally, we review the open questions and development perspectives linked with data assimilation applications to the tomographic problem.

Presenting Author: Julien Thurin

Authors