Transport Lagrangian misfit measures and velocity model uncertainty in Bayesian moment tensor inversion
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segam2021-3594806.1.pdf
Description
Published version
Size
1.74 MB
Format
Adobe PDF
Checksum (MD5)
937205a516a56e3392289a86318443c9
Author(s)
Scarinci, Andrea
Marzouk, Youssef
Gu, Chen
Fehler, Michael
Waheed, Umair bin
Kaka, Sanlinn
Dia, Ben M
Date Issued
September 1, 2021
Journal
First International Meeting for Applied Geoscience & Energy
Publisher
Society of Exploration Geophysicists
Citation
Andrea Scarinci, Youssef Marzouk, Chen Gu, Michael Fehler, Umair bin Waheed, Sanlinn Kaka, Ben M. Dia; September 1, 2021. "Transport Lagrangian misfit measures and velocity model uncertainty in Bayesian moment tensor inversion." Proceedings of the First International Meeting for Applied Geoscience & Energy. First International Meeting for Applied Geoscience & Energy. (pp. pp. 1221-1225). ASME.
Version
Final published version
Abstract
Model error is endemic to full waveform inversion (FWI). Bayesian moment tensor inversion is an example of a linear inverse problem that can be heavily affected by errors in the velocity model. The choice of misfit function plays a crucial role in mitigating the effects of these errors. We explore and quantify the benefits of using a newly introduced optimal transport distance, called the Transport- Lagrangian (TL) distance, through a series of synthetic experiments. We generate earthquake data for a heterogeneous 3D model, the SEG/EAGE Overthrust model. For inference, we test several layered-medium models derived from vertical profiles of velocity at single locations in the 3D model. A consistent Bayesian update is established to integrate the aforementioned distance with FWI. Through an appropriate scoring system, we show that, on average, TL distances produce posterior distributions with less bias and less variance than distributions obtained with a standard l2 misfit.
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DOI of Published Version
https://doi.org/10.1190/segam2021-3594806.1
