Unsupervised Deep Learning for Ground Roll and Scattered Noise Attenuation

Liu, Dawei; Sacchi, Mauricio D.; Wang, Xiaokai; Chen, Wenchao

doi:10.1109/tgrs.2023.3325324

IEEE Trans. Geosci. Remote Sensing

2023

DOI: 10.1109/tgrs.2023.3325324

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Unsupervised Deep Learning for Ground Roll and Scattered Noise Attenuation

Dawei Liu,

Mauricio D. Sacchi,

Xiaokai Wang

et al.

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2024

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Cited by 9 publications

References 69 publications

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Seismic data interpolation with a recurrent inference mechanism

Sun,

Zhang,

Liu

et al. 2024

Journal of Geophysics and Engineering

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Seismic data interpolation is a significant procedure in seismic data processing as the highly complete data is extremely useful for the subsequent imaging and interpretation workflows. Recently, numerous deep learning based techniques have been utilized to help improve the reconstruction quality. Supervised learning approaches are able to predict the results in a second, but they depend heavily on the training data, which therefore limits the generalized applications. In contrast, unsupervised learning methods are applicable to any data, but they need much more computational time and prior knowledge to be implemented. Especially, the recovery of aliased and consecutively missing data is incredibly challenging. To solve this problem, we propose a novel framework for Seismic Interpolation using Recurrent Inference Mechanism(SIRIM). Integrating the advantages of supervised and unsupervised learning paradigms, we build a specific convolutional recurrent inference network such that it is able to learn the dynamic priors when plugged in the physics-informed iterative algorithm, as well as directly reconstruct various types of incomplete data. We validate SIRIM in comparison with some traditional and learning-based methods. The performance on synthetic and field data illustrates the effectiveness and robustness of our proposed approach which outperforms baseline methods in terms of aliased and consecutively missing data.

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Seismic data interpolation with a recurrent inference mechanism

Sun,

Zhang,

Liu

et al. 2024

Journal of Geophysics and Engineering

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Swin Transformer for Seismic Denoising

Li,

Liu,

Wang

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Gabor-Based Learnable Sparse Representation for Self-Supervised Denoising

Liu,

Cheng,

Alkhalifah

2024

IEEE Trans. Geosci. Remote Sensing

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Unsupervised Deep Learning for Ground Roll and Scattered Noise Attenuation

Cited by 9 publications

References 69 publications

Seismic data interpolation with a recurrent inference mechanism

Seismic data interpolation with a recurrent inference mechanism

Swin Transformer for Seismic Denoising

Gabor-Based Learnable Sparse Representation for Self-Supervised Denoising

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