Attention Mechanism-Based Improvement of Stacked Surface Wave Cross-Correlation From High-Frequency Ambient Noise

The cross-correlation of high-frequency ambient noise (>1 Hz) is usually interpreted as the empirical Green’s function between two stations and used for imaging the near surface. However, high-frequency ambient noise mainly originates from human activities with nonuniform distributions, which may lead to spurious arrival in cross-correlation and bias the analysis of surface waves. Here, we develop an algorithm for improving high-frequency surface wave cross-correlation using an attention mechanism-based neural network, CCformer. The CCformer takes two-station cross-correlations of different time segments as input. Instead of directly producing an improved cross-correlation, the CCformer integrates the process of stacking individual cross-correlations to enhance its explainability. By identifying coherent information between each segment and generating stacking weights, the CCformer improves the desired coherent signals and attenuates spurious and incoherent noises, ultimately resulting in a well-stacked cross-correlation. After training with a synthetic dataset of 200000 labeled samples, the CCformer presents a good ability to improve the quality of stacked cross-correlation for a synthetic noise-added test dataset with dispersion, source distribution, and acquisition parameters different from the training dataset. The dispersion spectrum of the improved cross-correlation is more continuous than the results of linear stack (LS) and phase-weighted stack, and the spectral maxima agree with the theoretical dispersion curve. Moreover, a real dataset acquired from a test site also indicates the generalizability of CCformer for laterally varying media according to the symmetry of improved cross-correlation, dispersion spectrum maxima consistent with that of active data, and inversion results validated by known targets. Therefore, the proposed algorithm provides a practical solution for automatically extracting effective surface wave signals from high-frequency ambient noise.