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Seismic Tomography, Image Segmentation and Deep Learning

B4d7c0850e0dace81d10c5d2212d5aaad9713e6b

Speaker

Xu Yang, University of California, Santa Barbara

Time

2020.05.20 14:00-15:00

Venue

Online—ZOOM APP

ZOOM Info

Conference ID: 973-479-66457
PIN Code: 112683

Abstract

Seismic tomography is a scientific field using realistic earthquake data to analyze the inner structure of our Earth. In this talk, we present a natural connection of three-dimensional seismic tomography to image segmentation problems, which we solve efficiently using deep neural networks with a UNet architecture. It is challenging to obtain sufficient valid data to train neural networks, and we overcome it by developing a fast synthetic data generator using multi-scale asymptotic analysis. The accuracy and parallelizability of the proposed algorithm is illustrated by comparing to the spectral element method. Moreover, the developed multi-scale algorithm can be also used to accelerate various standard applications in seismic tomography, including seismic traveltime tomography and full waveform inversion.

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