Deep Learning-Based Data-Driven P-/S-Wave Vector Decomposition for Multicomponent Seismic Data

The precise decomposition of P/S waves in multicomponent seismic data is critical for seismic imaging. Inaccuracies in this decomposition can result in migration images with biased amplitudes and undesirable crosstalk artifacts. Although vector decomposition (VD) methods are effective, they rely on the availability of elastic parameters at the acquisition surface. Therefore, we propose a data-driven deep-learning (DL)-P/S-VD method that eliminates the need for prior elastic parameter information. We used publicly available elastic models to generate training datasets by simulating multicomponent data and their amplitude-preserving P-/S-wave components using the decoupled elastic wave equation. Our analysis explores the impact of the loss function type, output channel quantity, and direct wave removal on the generalization ability of DL-PSVD. The critical insights from the numerical experiments include the superior generalization ability of DL-PSVD using two channels when the P-/S-wave energy distribution is highly unbalanced. Moreover, DL-PSVD exhibits improved generalization ability using four channels for observed data with removed direct waves. Finally, the L1 loss function is more effective for DL-PSVD’s generalization ability than the L2 loss function. Overall, the proposed DL-PSVD is a promising method for automatic P-/S-wave VD without requiring prior elastic parameter information.