Unsupervised Learning for Seismic Internal Multiple Suppression Based on Adaptive Virtual Events

Wang, Kunxi; Hu, Tianyue; Wang, Shangxu

doi:10.1109/tgrs.2022.3169481

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Wang et al. (2022a) proposed a seismic multiple suppression method (Siahkoohi et al., 2019a; Wang et al., 2022b) using the DNN based on data augmentation training, which had a certain ability to work across work areas under transfer learning (Siahkoohi et al., 2019b). Denoising CNNs (DnCNN) and U‐Net CNNs are two of the most commonly used network structures in data denoising (Ronneberger et al., 2015; Zhang et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Das et al (2019) proposed a wave impedance inversion method based on CNN, which predicted wave impedance from generated samples when the local medium model and source wavelet phase were outside the training set. Wang et al (2022a) proposed a seismic multiple suppression method (Siahkoohi et al, 2019a;Wang et al, 2022b) using the DNN based on data augmentation training, which had a certain ability to work across work areas under transfer learning (Siahkoohi et al, 2019b). Denoising CNNs (DnCNN) and U-Net CNNs are two of the most commonly used network structures in data denoising (Ronneberger et al, 2015;Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

Wang

Mao

Song

et al. 2022

Geophysical Prospecting

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Within the field of seismic data acquisition with active sources, the technique of acquiring simultaneous data, also known as blended data, offers operational advantages. The preferred processing of blended data starts with a step of deblending, that is separation of the data acquired by the different sources, to produce data that mimic data from a conventional seismic acquisition and can be effectively processed by standard methods. Recently, deep learning methods based on the deep neural network have been applied to the deblending task with promising results, in particular using an iterative approach. We propose an enhancement to deblending with an iterative deep neural network, whereby we modify the training stage of the deep neural network in order to achieve better performance through the iterations. We refer to the method that only uses the blended data as the input data as the general training method. Our new multi-data training method allows the deep neural network to be trained by the data set with the input patches composed of blended data, noisy data with low amplitude crosstalk noise, and unblended data, which can improve the ability of the deep neural network to remove crosstalk noise and protect weak signal. Based on such an extended training data set, the multi-data training method embedded in the iterative separation framework can result in different outputs at different iterations and converge to the best result in a shorter iteration number. Transfer learning can further improve the generalization and separation efficacy of our proposed method to deblend the simultaneous-source data. Our proposed method is tested on two synthetic data and two field data to prove the effectiveness and superiority in the deblending of the simultaneous-source data compared with the general training method, generic noise attenuation network and low-rank matrix factorization methods.

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