Deep Learning for Efficient Microseismic Location Using Source Migration‐Based Imaging-Reference-Cited by-同舟云学术

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Deep Learning for Efficient Microseismic Location Using Source Migration‐Based Imaging

Published:2022-03 Issue:3 Volume:127 Page:
ISSN:2169-9313
Container-title:Journal of Geophysical Research: Solid Earth
language:en
Short-container-title:JGR Solid Earth

Author:

Zhang Qingshan¹^ORCID,Zhang Wei²³^ORCID,Wu Xinming¹^ORCID,Zhang Jie¹^ORCID,Kuang Wenhuan³,Si Xu¹

Affiliation:

1. School of Earth and Space Sciences University of Science and Technology of China Hefei China

2. Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology Southern University of Science and Technology Shenzhen China

3. Department of Earth and Space Sciences Southern University of Science and Technology Shenzhen China

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JB022649

Reference50 articles.

1. Joint location and source mechanism inversion of microseismic events: benchmarking on seismicity induced by hydraulic fracturing

2. Source location using time-reverse imaging

3. Moment tensor migration imaging

4. Is there a future for passive seismic?

5. Reverse travel time imaging of microseismic location

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2. MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments;Applied Sciences;2024-06-28

3. High-Precision Microseismic Source Localization Using a Fusion Network Combining Convolutional Neural Network and Transformer;Surveys in Geophysics;2024-06-14

4. Noise source localization using deep learning;Geophysical Journal International;2024-05-06

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