RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location Using P- and S-Wave Arrivals-Reference-Cited by-同舟云学术

RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location Using P- and S-Wave Arrivals

Published:2023 Issue: Volume:61 Page:1-10
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Yangkang¹^ORCID,Savvaidis Alexandros¹^ORCID,Fomel Sergey¹^ORCID,Saad Omar M.²^ORCID,Chen Yunfeng³

Affiliation:

1. Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, USA

2. National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Egypt

3. Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Zhejiang, Hangzhou, China

Funder

Texas Seismological Network Project and

Texas Consortium for Computational Seismology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10015782.pdf?arnumber=10015782

Reference21 articles.

1. Exploring the potentials and limitations of the time-reversal imaging of finite seismic sources

2. Reverse time migration for microseismic sources using the geometric mean as an imaging condition

3. Investigating the possibility of locating microseismic sources using distributed sensor networks

4. A Compact Program for 3D Passive Seismic Source-Location Imaging

5. High‐resolution and robust microseismic grouped imaging and grouping strategy analysis

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System;Journal of Geophysical Research: Machine Learning and Computation;2024-09

2. AiRs: Adapter in Remote Sensing for Parameter-Efficient Transfer Learning;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

4. Deep learning with soft attention mechanism for small-scale ground roll attenuation;GEOPHYSICS;2023-11-30

5. An Energy Focusing-Based Scanning and Localization Method for Shallow Underground Explosive Sources;Electronics;2023-09-10