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
    • Offshore application of non-uniform optimal sampling survey design
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    • Reflection full waveform inversion and its application to land data
    • Full waveform inversion with dynamic image warping
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    • Application of image-guided full waveform inversion to a 2D ocean-bottom cable data set
    • Image-guided sparse-model full waveform inversion
    • Quasi-Newton full-waveform inversion with a projected Hessian matrix
    • Waveform-based velocity estimation from reflection seismic data
    • A projected Hessian matrix for full waveform inversion
    • Full Waveform Inversion with reflection energies
    • Full waveform inversion with image-guided gradient
    • 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
    • Investigation of superharmonic sound propagation and imaging in biological tissues in vitro
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    • Improvement of tissue harmonic imaging using the pulse-inversion technique
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Deep Learning-based Quantitative Carbon Geosequestration Monitoring at the Illinois Basin-Decatur Project

Jan 1, 2023·
Mohamad Alattas
,
Vladimir Kazei
,
Tao Lin
,
Yong Ma
,
Weichang Li
· 0 min read
PDF Cite
Type
Conference paper
Publication
AGU Fall Meeting Abstracts
Last updated on Jan 1, 2023
Deep Learning
← Deep learning-based DAS to geophone data transformation Jan 1, 2023
Imaging distributed acoustic sensing-to-geophone conversion data: A field application to CO2 sequestration data Jan 1, 2023 →

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

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