1D elastic full-waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm-Gibbs sampler approach

2016 ◽  
Vol 65 (1) ◽  
pp. 64-85 ◽  
Author(s):  
Mattia Aleardi ◽  
Alfredo Mazzotti
Keyword(s):  
Genetic Algorithm ◽  
Gibbs Sampler ◽  
Waveform Inversion ◽  
Hybrid Genetic Algorithm ◽  
Uncertainty Estimation ◽  
Full Waveform Inversion ◽  
Full Waveform

scholarly journals Ensemble-based uncertainty estimation in Full Waveform Inversion

2019 ◽  
Author(s):  
J Thurin ◽  
R Brossier ◽  
L Métivier
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Waveform Inversion ◽  
Uncertainty Estimation ◽  
Low Rank ◽  
Full Waveform Inversion ◽  
Acoustic Frequency ◽  
Full Waveform ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform

SUMMARY 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 ones 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 to access 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. The combination of ensemble transform Kalman filter with full waveform inversion is discussed along with the scheme design and algorithmic details that lead to our mixed application. Both synthetic and field-data results are presented, along with the biases that are associated with the limited rank ensemble representation. Finally, we review the open questions and developments perspectives linked with data assimilation applications to the tomographic problem.

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