Abstract
For seismic exploration, excellent seismic records are of extreme significance. However, because of extensive mining over a long period, spare natural resources are primarily distributed in sophisticated areas with weak signals and low signal-to-noise ratios (SNRs). Besides, the incoherent noise overlaps with signals in both the time and frequency domains. All these characteristics place a greater demand on denoising methods. In this article, we propose a multiple attention mechanism-based modular convolutional neural network for desert seismic denoising. To extract weak signals from noisy recordings, two types of attention modules are chosen in this network. The channel-wise mechanism in the enhanced attention module is added to the network to extract detailed information from noisy records. Then, the supervised attention module can learn from the input noisy records again, emphasizing the significant features again to further enhance the denoising capability of our network. Besides, different types of dilated convolution layers are added to the network to extend the receptive field to cover minute information as much as possible. Both synthetic and real seismic denoising experiments show that the proposed network framework can suppress the random noise and recover signals even in low SNR situations.
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This research is supported by the Natural Science Foundation of the Department of Science and Technology of Jilin Province under grant no. 20200201046JC and the National Natural Science Foundation of China under grant 41730422.
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Author 1: Conceptualization, Methodology, Software, Investigation, Formal Analysis; Author 2: Data Curation, Writing - Original Draft; Author 3: Resources, Supervision; All authors reviewed the manuscript.
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Li, J., Qu, R. & Lu, C. Multiple Attention Mechanisms-Based Convolutional Neural Network for Desert Seismic Denoising. Pure Appl. Geophys. 180, 2135–2155 (2023). https://doi.org/10.1007/s00024-023-03255-5
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DOI: https://doi.org/10.1007/s00024-023-03255-5