3-D Seismic Fault Detection Using Recurrent Convolutional Neural Networks With Compound Loss-Reference-Cited by-同舟云学术

3-D Seismic Fault Detection Using Recurrent Convolutional Neural Networks With Compound Loss

Published:2023 Issue: Volume:61 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ma Xiao¹,Yao Gang²^ORCID,Zhang Feng¹,Wu Di¹

Affiliation:

1. State Key Laboratory of Petroleum Resources and Prospecting and the College of Geophysics, China University of Petroleum (Beijing), Beijing, China

2. State Key Laboratory of Petroleum Resources and Prospecting and the Unconventional Petroleum Research Institute, China University of Petroleum (Beijing), Beijing, China

Funder

National Key Research and Development Program of China

Research and Development Department of China National Petroleum Corporation

National Natural Science Foundation of China

Science Foundation of China University of Petroleum

State Key Laboratory of Petroleum Resources and Prospecting

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10124454.pdf?arnumber=10124454

Reference46 articles.

1. Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps

2. R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation

3. Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data

4. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation;alom;arXiv 1802 06955,2018

5. Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

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