Real‐time deep‐learning inversion of seismic full waveform data for CO <sub>2</sub> saturation and uncertainty in geological carbon storage monitoring-Reference-Cited by-同舟云学术

Real‐time deep‐learning inversion of seismic full waveform data for CO ₂ saturation and uncertainty in geological carbon storage monitoring

Published:2022-04-07 Issue: Volume: Page:
ISSN:0016-8025
Container-title:Geophysical Prospecting
language:en
Short-container-title:Geophysical Prospecting

Author:

Um Evan Schankee¹,Alumbaugh David¹,Lin Youzuo²,Feng Shihang³

Affiliation:

1. Earth and Environmental Sciences Lawrence Berkeley National Laboratory Berkeley CA 94720 USA

2. Earth and Environment Division Los Alamos National Laboratory Los Alamos NM 87545 USA

3. Theoretical Division Los Alamos National Laboratory Los Alamos NM 87545 USA

Publisher

Wiley

Subject

Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.13197

Reference67 articles.

1. High-resolution characterization of a CO2 plume using crosswell seismic tomography: Cranfield, MS, USA

2. Alumbaugh D. Commer M. Crandall D. Gasperikova E. Harbert W. Li Y. et al. (2021) Development of a multi‐scale synthetic data set for the testing of subsurface co2storage monitoring strategies. AGU Meeting.

3. ACCURACY OF FINITE‐DIFFERENCE MODELING OF THE ACOUSTIC WAVE EQUATION

4. Deep-learning tomography

5. An effective crosswell seismic traveltime-estimation approach for quasi-continuous reservoir monitoring

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