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

Wei, Yanwen; Li, Yunyue Elita; Yang, Jizhong; Zong, Jingjing; Fang, Jinwei; Fu, Haohuan

doi:10.1190/segam2020-3426539.1

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…In recent years, many achievements have emerged in the research of wave field separation [18][19][20], and different methods have been adopted according to different data types and application directions. For example, the P-wave and S-wave separation of multi-wave seismic data are generally based on the kinematic characteristics (wave velocity, vibration direction) and dynamic characteristics (wave amplitude, frequency, and phase) of seismic waves, mainly the F-K filtering τ-P transform, Radon transform, polarization filtering, full waveform inversion, divergence, and curl methods [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Research and application of Rayleigh wave extraction method based on microtremors signal analysis

Pan²,

Xin³

2023

Front. Phys.

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This study investigates the method and application of single-channel, three-component microtremor signal co-directional Rayleigh surface-wave extraction. The research focuses on filtering linear polarization waves using polarizability wave and phase-difference filtering, which were analyzed based on both simulated data and real microtremor signals. Additionally, the study examines the use of time-frequency analysis to analyze microtremor signals and identify Rayleigh wave propagation direction. The combination of these methods leads to a set of procedures for extracting high-SNR co-directional Rayleigh surface waves from microtremor signals, which was applied to the elliptical polarizability imaging method. Results indicate that the proposed data processing process effectively filters linear polarization waves and accurately determines the propagation direction of the Rayleigh wave, leading to significant improvement in the accuracy of elliptical polarizability exploration results. This provides a reference for obtaining high signal-to-noise ratio data in microtremor Rayleigh wave seismic exploration.

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Section: Introductionmentioning

confidence: 99%

Research and application of Rayleigh wave extraction method based on microtremors signal analysis

Pan²,

Xin³

2023

Front. Phys.

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“…Many researchers have applied MTL to solve practical geophysical problems such as seismic image processing (Wu, Liang, Shi, Geng, et al, 2019), P/S wave separation and reverse time migration for vertical seismic profiling (VSP) (Y. Wei et al, 2020), super-resolution of seismic velocity model (Li et al, 2020), seismic structural curvature volume extraction (Ao et al, 2020) and seismic inversion (Meng et al, 2022;Zheng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, training data are insufficient to train an accurate network, and the shared training data and parameters of MTL play a role in data augmentation (Y. Zhang & Yang, 2021). Many researchers have applied MTL to solve practical geophysical problems such as seismic image processing (Wu, Liang, Shi, Geng, et al., 2019), P/S wave separation and reverse time migration for vertical seismic profiling (VSP) (Y. Wei et al., 2020), super‐resolution of seismic velocity model (Li et al., 2020), seismic structural curvature volume extraction (Ao et al., 2020) and seismic inversion (Meng et al., 2022; Wang, Wang, et al., 2021; Zheng et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Deep carbonate fault–karst reservoir characterization by multi‐task learning

Zhang,

Li,

Yan

et al. 2023

Geophysical Prospecting

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The carbonate fault–karst reservoir is a special and significant reservoir in the Shunbei area, and the development of the cave has been controlled by strike‐slip faults. Due to the complex subsurface structures, fault–karst reservoir characterization is generally divided into fault and cave detection tasks. The potential spatial relationships between faults and caves might be neglected by using the separate detection scheme. The multi‐task learning network can perform multiple tasks simultaneously and exploit the potential features of training data by using a deep neural network. In this study, we built fault–karst models based on the geological background of the Shunbei area and synthesized fault–karst training data using three‐dimensional point spread function convolution. Then, we developed a multi‐task learning network to learn fault–karst features and detect faults and caves simultaneously. The test result demonstrates that the multi‐task learning network trained by synthetic fault–karst data can effectively identify the faults and caves in field seismic data. The comparisons of the multi‐task learning network, single‐task learning networks and conventional methods demonstrate the importance of spatial relationships between faults and caves and show the superiority of the multi‐task learning network. This technique could significantly assist in the exploration, development and well deployment for an ultra‐deep carbonate reservoir.

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“…In recent years, the application of deep learning methods [21] in the fields of geophysics and applied geophysics has received great attention and promising achievements, such as detecting faults [22,23], classifying facies [24,25], attenuating noise [26,27], picking first arrivals [28,29], building velocity models [30,31] and reconstructing seismic data [32,33]. Inspired by deep learning methods, some scholars have proposed many effective separation and decomposition methods of P-and S-wave modes from the coupled elastic seismic wavefields based on different neural networks, such as multi-task learning [34], convolutional neural networks (CNNs) [35,36], generative adversarial networks (GANs) [37,38] and deep convolutional neural networks (DCNNs) [39], and these methods are intelligent datadriven algorithms for the separation and decomposition of P-and S-wave modes which are not dependent on elastic model parameters and certain prior conditions. However, these above methods mainly use the corresponding neural networks to separate two decoupled scalar P-and S-wave modes from the coupled elastic seismic wavefields, and therefore cannot obtain all the horizontal and vertical components of the decomposed vector P-and S-wave modes.…”

Section: Introductionmentioning

confidence: 99%

Vector Decomposition of Elastic Seismic Wavefields Using Self-Attention Deep Convolutional Generative Adversarial Networks

Liu,

Cao,

You

et al. 2023

Applied Sciences

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Vector decomposition of P- and S-wave modes from elastic seismic wavefields is a key step in elastic reverse-time migration (ERTM) to effectively improve the multi-wave imaging accuracy. Most previously developed methods based on the apparent velocities or the polarization characteristics of different wave modes are unable to accurately achieve the vector decomposition of P- and S-wave modes. To effectively overcome the shortcomings of conventional methods, we develop a vector decomposition method of P- and S-wave modes using self-attention deep convolutional generative adversarial networks (SADCGANs) to effectively separate the horizontal and vertical components of P- and S-wave modes from elastic seismic wavefields and accurately preserve their amplitude and phase characteristics for isotropic elastic media. For an elastic model, we use many time slices for a given source position to train the neural network, and use other time slices not in this training dataset to test the neural network. Numerical examples of different models demonstrate the effectiveness and feasibility of our developed method and indicate that it provides an effective intelligent data-driven vector decomposition method of P- and S-wave modes.

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

Cited by 8 publications

References 19 publications

Research and application of Rayleigh wave extraction method based on microtremors signal analysis

Research and application of Rayleigh wave extraction method based on microtremors signal analysis

Deep carbonate fault–karst reservoir characterization by multi‐task learning

Vector Decomposition of Elastic Seismic Wavefields Using Self-Attention Deep Convolutional Generative Adversarial Networks

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