End-to-end seismic signals denoising via deep residual convolution and self-attention mechanisms

End-to-end seismic signals denoising via deep residual convolution and self-attention mechanisms

Denoising of seismic waveform signals is crucial for seismic monitoring and seismological research. To this end, we propose an end-to-end deep learning method for denoising seismic waveforms. The method combines the deep convolutional network with the multi-head self-attention mechanism. We employ a...

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Bibliographic Details
Main Authors: ZHAO Botao, KANG Zuheng, HE Yayun, PENG Junqing, ZHANG Xulong, QU Xiaoyang, TAN Yipei, CHEN Yule, XIAO Chunguang, WANG Jianzong
Format: Article
Language:Chinese
Published: China InfoCom Media Group 2025-01-01
Series:大数据
Subjects:
seismic signal denoising
deep learning
convolutional network
self-attention mechanism
residual encoder-decoder
Online Access:http://www.j-bigdataresearch.com.cn/zh/article/111998980/
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http://www.j-bigdataresearch.com.cn/zh/article/111998980/

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