Yong Ma
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    • Method and system for generalizable deep learning framework for seismic velocity estimation robust to survey configuration
    • Deep learning-based DAS to geophone data transformation
    • Deep Learning-based Quantitative Carbon Geosequestration Monitoring at the Illinois Basin-Decatur Project
    • Imaging distributed acoustic sensing-to-geophone conversion data: A field application to CO2 sequestration data
    • Robust Deep Learning Seismic Velocity Inversion Over Varying Acquisitions
    • Comparing DAS and geophone data and imaging for monitoring CO2 sequestration
    • Estimate near-surface velocity with reversals using deep learning and full-waveform inversion
    • Imaging CO2 Sequestration with Converted DAS-to-Geophone Data
    • Imaging through gas clouds: The application of CSI and FWI in Bohai, China
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    • Application of image-guided full waveform inversion to a 2D ocean-bottom cable data set
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    • Quasi-Newton full-waveform inversion with a projected Hessian matrix
    • Waveform-based velocity estimation from reflection seismic data
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    • Full Waveform Inversion with reflection energies
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    • The effects of multi-scale heterogeneities on wave-equation migration
    • Effects of vapour bubbles on acoustic and temperature distributions of therapeutic ultrasound
    • Noninvasive temperature estimation by detecting echo-strain change including thermal expansion
    • Phase-coded multi-pulse technique for ultrasonic high-order harmonic imaging of biological tissues in vitro
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Robust Deep Learning Seismic Velocity Inversion Over Varying Acquisitions

Jan 1, 2023·
Yong Ma
,
Weichang Li
· 0 min read
PDF Cite
Type
Journal article
Publication
IEEE Geoscience and Remote Sensing Letters
Last updated on Jan 1, 2023
Deep Learning
← Imaging distributed acoustic sensing-to-geophone conversion data: A field application to CO2 sequestration data Jan 1, 2023
Comparing DAS and geophone data and imaging for monitoring CO2 sequestration Jan 1, 2022 →

© 2024 Yong Ma. This work is licensed under CC BY NC ND 4.0

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