CNN-tuned spatial filters for P- and S-wave decomposition and applications in elastic imaging

Abstract

P-and S-wave decomposition is essential for imaging multi-component seismic data in elastic media. A data-driven workflow is proposed to obtain a set of spatial filters that are highly accurate and artifact-free in decomposing the P-and Swaves in 2D isotropic elastic wavefields. The filters are formulated initially by inverse Fourier transforms of the wavenumberdomain operators, and then are tuned in a convolutional neural network to improve accuracy using synthetic snapshots. Spatial filters are flexible for decomposing P-and S-waves at any time step without performing Fourier transforms over the entire wavefield snapshots, and thus are suitable for target-oriented imaging. Snapshots from synthetic data show that the network-tuned spatial filters can decompose P-and S-waves with improved decomposition accuracy compared with other space-domain PS decomposition methods. Elastic reverse-time migration using Pand S-waves decomposed from the proposed algorithm shows reduced artifacts in the presence of a high velocity contrast.

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

“…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.…”