First-Break Picking Method Based on the Difference Between Multiwindow Energy Ratios-Reference-Cited by-同舟云学术

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First-Break Picking Method Based on the Difference Between Multiwindow Energy Ratios

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:

Kim Dowan¹^ORCID,Joo Yonghwan¹,Byun Joongmoo²^ORCID

Affiliation:

1. Marine Geology and Energy Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Republic of Korea

2. Reservoir Imaging with Seismic and EM technology using Machine Learning (RISE.ML) Laboratory, Hanyang University, Seoul, Republic of Korea

Funder

Ministry of Science and ICT, South Korea

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10065536.pdf?arnumber=10065536

Reference46 articles.

1. Novel hybrid artificial neural network based autopicking workflow for passive seismic data

2. Characterization of global seismograms using an automatic-picking algorithm

3. Automatic picking of P and S phases using a neural tree

4. An automatic phase picker for local and teleseismic events

5. A robust first-arrival picking workflow using convolutional and recurrent neural networks

Cited by 4 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. A New Data Processing Approach for the SHPB Test Based on PSO-TWER;Applied Sciences;2024-04-25

2. Simultaneous Automatic Picking and Manual Picking Refinement for First-Break;IEEE Transactions on Geoscience and Remote Sensing;2024

3. 3-D Seismic First Break Picking Based on Two-Channel Mask Strategy;IEEE Transactions on Geoscience and Remote Sensing;2024

4. UPNet: Uncertainty-Based Picking Deep Learning Network for Robust First Break Picking;IEEE Transactions on Geoscience and Remote Sensing;2024

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