Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Li, Shu; Yang, Xi; Liu, Haonan; Cai, Yao; Peng, Zhenming

doi:10.3390/en13020372

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Wu et al propose a multitask learning scheme to simultaneously solve fault detection, edge-preserving smoothing and normal estimation by using a single U-net architecture [15]. Besides fault detection, deep learning techniques have also been widely applied in many other fields of seismic data processing and interpretation, e.g., noise attenuation [16,17], 3D reservoir modeling [18] and seismic data interpolation and reconstruction [19,20]. Recently, transfer learning has also attracted the attention of many researchers [21,22].…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples

2021

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Fault interpretation is an important part of seismic structural interpretation and reservoir characterization. In the conventional approach, faults are detected as reflection discontinuity or abruption and are manually tracked in post-stack seismic data, which is time-consuming. In order to improve efficiency, a variety of automatic fault detection methods have been proposed, among which widespread attention has been given to deep learning-based methods. However, deep learning techniques require a large amount of marked seismic samples as a training dataset. Although the amount of synthetic seismic data can be guaranteed and the labels are accurate, the difference between synthetic data and real data still exists. To overcome this drawback, we apply a transfer learning strategy to improve the performance of automatic fault detection by deep learning methods. We first pre-train a deep neural network with synthetic seismic data. Then we retrain the network with real seismic samples. We use a random sample consensus (RANSAC) method to obtain real seismic samples and generate corresponding labels automatically. Three real 3D examples are included to demonstrate that the fault detection accuracy of the pre-trained network models can be greatly improved by retraining the network with a few amount of real seismic samples.

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Section: Introductionmentioning

confidence: 99%

Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples

2021

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“…Other seismic random noise attenuation methods include the nonlocal means method (Bonar and Sacchi, 2012) and the sparsity and low-rank regularization method (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Convolutional sparse coding for noise attenuation in seismic data

Liu

Lü

2021

GEOPHYSICS

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We propose convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising phases. Seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then, we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic data show that CSC can learn a set of shifted invariant filters, which can reduce the redundancy of learned filters in the traditional sparse-coding denoising method. CSC achieves good denoising performance when training by the noisy data and better performance when training on a similar but noiseless data set. The numerical results from the field data test indicate that CSC can effectively suppress seismic noise in the complex field data. By excluding filters with coherent noise features, the proposed method can further attenuate the coherent noise and separate ground roll.

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Denoising research of petrographic thin section images with the global residual generative adversarial network

Pan

et al. 2023

Journal of Petroleum Science and Engineering

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Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Cited by 5 publications

References 23 publications

Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples

Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples

Convolutional sparse coding for noise attenuation in seismic data

Denoising research of petrographic thin section images with the global residual generative adversarial network

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