High-frequency full-waveform inversion with deep learning for seismic and medical ultrasound imaging
Name
segam2020-3426935.1.pdf
Description
Published version
Size
3.06 MB
Format
Adobe PDF
Checksum (MD5)
26963a6994514e6a5a548b9f3e5b3c02
Author(s)
Feigin, Micha
Makovsky, Yizhaq
Freedman, Daniel
Anthony, Brian W
Date Issued
September 30, 2020
Journal
SEG Technical Program Expanded Abstracts 2020
Publisher
Society of Exploration Geophysicists
Citation
Micha Feigin, Yizhaq Makovsky, Daniel Freedman, Brian W. Anthony; September 1, 2020. "High-frequency full-waveform inversion with deep learning for seismic and medical ultrasound imaging." Proceedings of the SEG Technical Program Expanded Abstracts 2020. SEG Technical Program Expanded Abstracts 2020. (pp. pp. 3492-3496). ASME.
Version
Final published version
Abstract
Sound speed inversion is a key problem in seismic imaging for the purpose of high-resolution imaging and ground structure analysis. In the medical ultrasound imaging domain, longitudinal sound speed recovery, as opposed to shear wave velocity methods, has seen little use, mostly due to the resources required. However both research and new hardware are showing interesting applications and implications (Nebojsa Duric and Littrup 2018) Classic full waveform inversion (FWI) and Travel time tomography methods require large time and computational resources, and often, human in the loop interaction. This makes them inapplicable for most medical imaging applications. FWI also generally requires a good initial condition and low-frequency content in the signal for stable convergence. In this work we analyze the applicability of a deep-learning based approach for high frequency FWI. We present results on single-shot recovery using simulated data employing characteristic parameters for both medical ultrasound and seismic datasets. Results show great potential for interactive frame rate approximate solution.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1190/segam2020-3426935.1
