Dispersion-Guided Physics-Aware Deep Inverse Operator for Surface Wave Mode Separation
arXiv:2607.12808
The paper presents an unsupervised physics-aware deep learning framework that separates fundamental and higher surface‑wave modes directly in the time‑space domain by using adaptive Gaussian masks and physical constraints in the frequency‑phase‑velocity domain, without needing labeled training data.
Abstract
Surface-wave (SW) dispersion analysis is widely used in near-surface geophysics and seismology to determine shear-wave velocity structures by measuring SW geometric dispersion in seismic data. Among the available approaches, multichannel analysis of surface waves (MASW) and two-station methods are commonly employed to extract dispersion information for SW inversion. However, the coexistence of fundamental and higher modes in seismic data poses challenges for these methods, particularly for two-station analysis. To separate the different mode components, we propose a physics-aware unsupervised deep-learning framework. The method acts as a deep inverse operator that directly separates fundamental- and higher-mode components in the time-space domain using an adaptive Gaussian mask constructed in the frequency-phase-velocity (f-v) domain. Physical constraints are incorporated into the loss function by maximizing energy concentration within the target mask while suppressing leakage outside it. Through backpropagation, the network learns the inverse mapping from physical constraints in the f-v domain to wavefield separation in the time-space domain without requiring labeled training data. Numerical experiments on both synthetic and field data show that the framework provides a robust and automated solution for SW mode separation, facilitating more reliable dispersion-curve picking and improving the accuracy of subsequent SW inversion.