Coherent Source Subsampling: A Data-Driven Strategy for Restoring Causal-Acausal Symmetry in Ambient Seismic Wavefield Correlations
Authors
Sanket Narayan Bajad
Pawan Bharadwaj
Abstract
Ambient noise tomography relies on the assumption that the seismic wavefield is equipartitioned, meaning that energy is uniformly distributed among all directions. However, in practice, ambient noise sources are highly non-uniform in both spatial and temporal dimensions, resulting in biased estimation of the Green's function between stations. We introduce a data-driven method, Coherent Source Subsampling (CSS), which selects and averages only those cross-correlation time windows that are associated with the excitation of sources in the stationary-zone. By confining the ensemble average to these coherent subsets, CSS effectively mitigates the influence of anisotropic or intermittent sources and restores causal-acausal symmetry in the retrieved Green's functions. Applications to regional-scale ambient noise datasets demonstrate that CSS boosts inter-station coherence and enhances the reliability of surface-wave dispersion measurements, providing a physically interpretable bridge between source statistics and noise correlation theory.
