Seismic Fault Identification Based on Multi-Scale Dense Convolution and Improved Long Short-Term Memory Network

Seismic Fault Identification Based on Multi-Scale Dense Convolution and Improved Long Short-Term Memory Network

Aiming at the local, global and temporal morphological characteristics of faults in seismic profiles, this paper proposes the MCD-ABiLSTM method for fault identification. The method uses multi-channel, convolution kernels of different sizes and convolution of different depths to extract multi-scale...

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Bibliographic Details
Main Authors: Liang Guo, Ran Xiong, Jilong Zhao, Hui Wang, Zhao Chen, Xuan Zou
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Deep learning
LSTM
ABiLSTM
MCD
seismic fault identification
Online Access:https://ieeexplore.ieee.org/document/10301494/

Internet

https://ieeexplore.ieee.org/document/10301494/

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