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Volume 69, Issue 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random noise and preserve the coherent seismic signal. The input data are divided into several patches, and each patch is encoded to extract the meaningful features. Following this, the extracted features are decompressed to retrieve the seismic signal. Skip connections are used between the encoder and decoder parts, allowing the proposed algorithm to extract high‐order features without losing important information. Besides, dropout layers are used as regularization layers. The dropout layers preserve the most meaningful features belonging to the seismic signal and discard the remaining features. The proposed algorithm is an unsupervised approach that does not require prior information about the clean signal. The input patches are divided into 80% for training and 20% for testing. However, it is interesting to find that the proposed algorithm can be trained with only 30% of the input patches with an effective denoising performance. Four synthetic and four field examples are used to evaluate the proposed algorithm performance, and compared to the deconvolution and the singular spectrum analysis. The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.

© 2021 European Association of Geoscientists & Engineers © 2020 European Association of Geoscientists & Engineers
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/content/journals/10.1111/1365-2478.13062
2021-04-18
2024-04-20

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http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.13062
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A fully unsupervised and highly generalized deep learning approach for random noise suppression
Geophysical Prospecting 69, 709 (2021); https://doi.org/10.1111/1365-2478.13062
/content/journals/10.1111/1365-2478.13062
/content/journals/10.1111/1365-2478.13062
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  • Article Type: Research Article
Keyword(s): Deep learning; Random noise suppression

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