Unsupervised 3-D Random Noise Attenuation Using Deep Skip Autoencoder

doi:10.1109/TGRS.2021.3100455

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Unsupervised 3-D Random Noise Attenuation Using Deep Skip Autoencoder

2022-01-01

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Yapo Abolé Serge Innocent Oboué

Yangkang Chen

DOI：10.1109/TGRS.2021.3100455

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Abstract

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Effective random noise attenuation is critical for subsequent processing of seismic data, such as velocity analysis, migration, and inversion. Thus, the removal of seismic random noise with an uncertainty level is meaningful. Attenuating 3-D random noise in a supervised way based on deep learning (DL) is challenging because clean labels are difficult to obtain. Therefore, it is necessary to develop an adaptive unsupervised-based method for random noise attenuation. In this article, we propose a deep-denoising unsupervised learning (DDUL) network to attenuate random noise in 2-D/3-D seismic data. A patching technique is used to split 2-D/3-D seismic data into several patches to be fed into the network, which helps to expand the number of samples for training. We use the fully symmetrical structure of the autoencoder to construct the network. In each corresponding encoder and decoder layer, skip connections are added to enhance the learning of seismic data features. We construct three blocks to extract waveform features in seismic data, i.e., encoder, decoder, and skip blocks. Among them, the skip is connected between the encoder and decoder blocks of each hidden layer. The use of multiple blocks not only improves the network's ability to extract seismic data features but also solves the problem of excessive training parameters caused by hidden layer stacking. Five 2-D/3-D synthetic and field seismic datasets are used to test the denoising performance of our proposed method. The denoising results demonstrate that our proposed method has good signal-preserving and noise attenuation capabilities in real-world applications.

Keywords:

Attenuation

Feature extraction

Noise reduction

Decoding

Noise measurement

Training

Signal to noise ratio

3-D random noise attenuation

autoencoder (AE)

deep learning (DL)

U-Net