Uncertainty quantification for Wavefield Reconstruction Inversion using a PDE free semidefinite Hessian and randomize-then-optimize method

TitleUncertainty quantification for {Wavefield} {Reconstruction} {Inversion} using a {PDE} free semidefinite {Hessian} and randomize-then-optimize method
Publication TypeConference
Year of Publication2016
AuthorsZhilong Fang, Chia Ying Lee, Curt Da Silva, Tristan van Leeuwen, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Page1390-1394
Month10
KeywordsSEG, Uncertainty quantification, WRI
Abstract

The data of full-waveform inversion often contains noise, which induces uncertainties in the inversion results. Ideally, one would like to run a number of independent inversions with different realizations of the noise and assess model-side uncertainties from the resulting models, however this is not feasible because we collect the data only once. To circumvent this restriction, various sampling schemes have been devised to generate an ensemble of models that fit the data to within the noise level. Such sampling schemes typically involve running multiple inversions or evaluating the Hessian of the cost function, both of which are computationally expensive. In this work, we propose a new method to quantify uncertainties based on a novel formulation of the full-waveform inversion problem – wavefield reconstruction inversion. Based on this formulation, we formulate a semidefinite approximation of the corresponding Hessian matrix. By precomputing certain quantities, we are able to apply this Hessian to given input vectors without additional solutions of the underlying partial differential equations. To generate a sample, we solve an auxiliary stochastic optimization problem involving this Hessian. The result is a computationally feasible method that, with little overhead, can generate as many samples as required at small additional cost. We test our method on the synthetic BG Compass model and compare the results to a direct-sampling approach. The results show the feasibility of applying our method to computing statistical quantities such as the mean and standard deviation in the context of wavefield reconstruction inversion.

Notes

(SEG, Dallas)

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2016/fang2016SEGuqw/fang2016SEGuqw.html
DOI10.1190/segam2016-13879108.1
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2016/fan...

Citation Keyfang2016SEGuqw