VSP wavefield separation method based on sparse representation and SVT

Gao, Lei; Chen, Wenchao; Wang, Baoli; Gao, Jinghuai; Bao, Qianzong

doi:10.1190/segam2013-0774.1

Cited by 2 publications

(2 citation statements)

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“…To separate wave modes correctly in the recorded data, various approaches have been employed, the prior polarization rotation (Greenhalgh et al, 1990;Cho and Spencer, 1992), domain transform (F-K or τ-p) methods (Tatham and Goolsbee, 1984;Dankbaar, 1985Dankbaar, , 1987, wave-equation based methods (Dellinger and Etgen, 1990;Sun et al, 2004;Huang and Milkereit, 2007), and statistical methods (Wang et al, 2018;Gao et al, 2013;van Der Baan, 2006;Muijs et al, 2004). However, the whole process of wave separation is time-consuming.…”

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confidence: 99%

Multi-task learning based P/S wave separation and reverse time migration for VSP

Wei

Yang

et al. 2020

SEG Technical Program Expanded Abstracts 2020

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P/S wave mode separation is an essential tool for single-mode analysis from multi-component seismic data. Wave separation methods in recorded data require expert knowledge to choose parameters in different shots of data. To make this process automatic, we propose a machine learning-based method to separate P/S waves. This method employs a multi-task neural network that extracts P-and S-potential simultaneously from multi-component VSP data. Targeting at a specific testing dataset, we derive an efficient building strategy to construct training datasets. Synthetic data experiment shows NN trained on our training dataset performs well both in training and testing datasets. We also make further verifications from the view of acoustic reverse time migration. The separated waves by using NN trained on our training datasets have a considerable high-resolution PP and PS imaging.

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mentioning

confidence: 99%

Multi-task learning based P/S wave separation and reverse time migration for VSP

Wei

Yang

et al. 2020

SEG Technical Program Expanded Abstracts 2020

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show abstract

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confidence: 99%

Multi-task learning based P/S wave separation and reverse time migration for VSP

Wei¹,

Li²,

Zong³

et al. 2020

Preprint

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P/S wave mode separation is an essential tool for single-mode analysis from multi-component seismic data. Wave separation methods in recorded data require expert knowledge to choose parameters in different shots of data. To make this process automatic, we propose a machine learning-based method to separate P/S waves. This method employs a multi-task neural network that extracts P- and S-potential simultaneously from multi-component VSP data. Targeting at a specific testing dataset, we derive an efficient building strategy to construct training datasets. Synthetic data experiment shows NN trained on our training dataset performs well both in training and testing datasets. We also make further verifications from the view of acoustic reverse time migration. The separated waves by using NN trained on our training datasets have a considerable high-resolution PP and PS imaging.

show abstract

VSP wavefield separation method based on sparse representation and SVT

Cited by 2 publications

References 8 publications

Multi-task learning based P/S wave separation and reverse time migration for VSP

Multi-task learning based P/S wave separation and reverse time migration for VSP

Multi-task learning based P/S wave separation and reverse time migration for VSP

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