Simultaneous-shot inversion for PDE-constrained optimization problems with missing data

TitleSimultaneous-shot inversion for PDE-constrained optimization problems with missing data
Publication TypeSubmitted
Year of Publication2018
AuthorsMichelle Liu, Rajiv Kumar, Eldad Haber, Aleksandr Y. Aravkin
KeywordsFull-waveform inversion, low-rank interpolation, Optimization, private
Abstract

Stochastic optimization is key to efficient inversion in PDE-constrained optimization. Using `simultaneous shots', or random superposition of source terms, works very well in simple acquisition geometries where all sources see all receivers, but this rarely occurs in practice. We develop an approach that interpolates data to an ideal acquisition geometry while solving the inverse problem using simultaneous shots. The approach is formulated as a joint inverse problem, combining ideas from low-rank interpolation with full-waveform inversion. Results using synthetic experiments illustrate the flexibility and efficiency of the approach.

Notes

Submitted to Inverse Problem on April 29, 2018.

URLhttps://www.slim.eos.ubc.ca/Publications/Private/Submitted/2018/liu2018ssi/liu2018ssi.pdf
Citation Keyliu2018ssi