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DeepGEM: Generalized Expectation-Maximization for Blind Inversion

Gao, Angela F. and Castellanos, Jorge C. and Yue, Yisong and Ross, Zachary E. and Bouman, Katherine L. (2021) DeepGEM: Generalized Expectation-Maximization for Blind Inversion. In: 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems foundation , La Jolla, CA, pp. 1-12. ISBN 9781713845393.

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Typically, inversion algorithms assume that a forward model, which relates a source to its resulting measurements, is known and fixed. Using collected indirect measurements and the forward model, the goal becomes to recover the source. When the forward model is unknown, or imperfect, artifacts due to model mismatch occur in the recovery of the source. In this paper, we study the problem of blind inversion: solving an inverse problem with unknown or imperfect knowledge of the forward model parameters. We propose DeepGEM, a variational Expectation-Maximization (EM) framework that can be used to solve for the unknown parameters of the forward model in an unsupervised manner. DeepGEM makes use of a normalizing flow generative network to efficiently capture complex posterior distributions, which leads to more accurate evaluation of the source's posterior distribution used in EM. We showcase the effectiveness of our DeepGEM approach by achieving strong performance on the challenging problem of blind seismic tomography, where we significantly outperform the standard method used in seismology. We also demonstrate the generality of DeepGEM by applying it to a simple case of blind deconvolution.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Gao, Angela F.0000-0001-8574-8728
Castellanos, Jorge C.0000-0002-0103-6430
Yue, Yisong0000-0001-9127-1989
Ross, Zachary E.0000-0002-6343-8400
Bouman, Katherine L.0000-0003-0077-4367
Additional Information:This research was carried out at the Jet Propulsion Laboratory and the California Institute of Technology under a contract with the National Aeronautics and Space Administration and funded through the President’s and Director’s Research and Development Fund (PDRDF). This work was sponsored by Beyond Limits, Jet Propulsion Laboratory Award 1669417, NSF Award 2048237, and generous gifts from Luke Wang and Yi Li. Additionally, we would like to thank He Sun for many helpful discussions. We declare no competing interests.
Group:Seismological Laboratory, Astronomy Department, Center for Geomechanics and Mitigation of Geohazards (GMG)
Funding AgencyGrant Number
JPL President and Director's FundUNSPECIFIED
Record Number:CaltechAUTHORS:20221026-200931912
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117607
Deposited By: Tony Diaz
Deposited On:26 Oct 2022 21:44
Last Modified:26 Oct 2022 21:44

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