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Microseismic Signal Reconstruction From Strong Complex Noise Using Low-Rank Structure Extraction and Dual Convolutional Neural Networks

Zhang, Chao ; van der Baan, Mirko

IEEE transaction on neural networks and learning systems, 2023-07, Vol.PP, p.1-11

United States: IEEE

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  • Título:
    Microseismic Signal Reconstruction From Strong Complex Noise Using Low-Rank Structure Extraction and Dual Convolutional Neural Networks
  • Autor: Zhang, Chao ; van der Baan, Mirko
  • Assuntos: Complex noise removal ; Convolution ; convolutional neural network ; Convolutional neural networks ; Feature extraction ; low-rank structure extraction ; Matrix decomposition ; microseismic signal reconstruction ; Signal reconstruction ; Signal to noise ratio ; Training
  • É parte de: IEEE transaction on neural networks and learning systems, 2023-07, Vol.PP, p.1-11
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Microseismic signal reconstruction from complex nonrandom noise is challenging, especially when the signal is disrupted or completely covered by strong field noise. Various methods often assume that signals are laterally coherent or the noise is predictable. In this article, we propose a dual convolutional neural network preceded by a low-rank structure extraction module to reconstruct signals hidden by strong complex field noise. Preconditioning by low-rank structure extraction is the first step in removing high-energy regular noise. The module is followed by two convolutional neural networks with different complexity to achieve better signal reconstruction and noise removal. In addition to the combination of synthetic and field microseismic data, natural images are also used in the training due to their correlation, complexity, and completeness, which contributes to increasing the generalization of the networks. The results from synthetic and real datasets demonstrate superior signal recovery, which cannot be achieved by using solely deep learning, low-rank structure extraction, or curvelet thresholding. Algorithmic generalization is demonstrated using independently acquired array data excluded during training.
  • Editor: United States: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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