Monitoring Subsurface Fracture Flow Using Unsupervised Deep Learning of Borehole Microseismic Waveform Data-Reference-Cited by-同舟云学术

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Monitoring Subsurface Fracture Flow Using Unsupervised Deep Learning of Borehole Microseismic Waveform Data

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Duan Chenglong¹^ORCID,Huang Lianjie¹^ORCID,Gross Michael¹^ORCID,Fehler Michael²^ORCID,Lumley David³^ORCID,Glubokovskikh Stanislav⁴

Affiliation:

1. Los Alamos National Laboratory, MS D452, Los Alamos, NM, USA

2. Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

3. Department of Sustainable Earth Systems Sciences and Department of Physics, University of Texas at Dallas, Richardson, TX, USA

4. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Funder

U.S. Department of Energy (DOE) through the Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC

National Nuclear Security Administration (NNSA) of U.S. DOE

U.S. DOE, Office of Fossil Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/36/10354519/10444628-aam.pdf

Reference48 articles.

1. Evidence for Latent Crustal Fluid Injection Transients in Southern California From Long‐Duration Earthquake Swarms

2. On the nature of long-period long-duration seismic events detected during hydraulic fracturing

3. Deep volcanic tremor and magma ascent mechanism under Kilauea, Hawaii

4. Observations of volcanic tremor at Mount St. Helens Volcano

5. Observations of volcanic tremor at Mount St. Helens in April and May 1980;Hofstetter;Bull. Seismolog. Soc. Amer.,1980

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