Coherent Source Subsampling: A Data-Driven Strategy for Restoring Causal-Acausal Symmetry in Ambient Seismic Wavefield Correlations

TL;DR

The paper introduces Coherent Source Subsampling (CSS), a data-driven method that improves ambient seismic noise correlation estimates by selecting source-related windows, effectively restoring symmetry and enhancing imaging in heterogeneous noise conditions.

Contribution

CSS is a novel data-driven technique that selectively averages seismic cross-correlations to mitigate source heterogeneity effects in ambient noise tomography.

Findings

CSS stabilizes surface-wave dispersion measurements.

CSS-derived tomograms reveal known geological features.

CSS outperforms full-ensemble averaging in heterogeneous conditions.

Abstract

Ambient noise tomography relies on the assumption that the seismic wavefield is equipartitioned. In practice, ambient noise sources are spatially and temporally heterogeneous, producing biased estimates of the Green's function between stations. We introduce a data-driven method, Coherent Source Subsampling (CSS), which selects and averages only cross-correlation time windows associated with excitation of sources in the stationary zone. By restricting the ensemble average to these windows, CSS mitigates the effects of nonuniform source distribution and restores causal-acausal symmetry in the retrieved interstation response. Applications to regional ambient-noise datasets show that CSS stabilizes surface-wave dispersion measurements even when source statistics violate the assumptions of standard seismic interferometry. For the central California dataset, CSS-derived group-velocity tomograms consistently image a high-velocity block between the Rinconada and San Andreas faults across multiple periods. In comparison, the full-ensemble (linear) average does not capture this block, which is well established. Our approach is particularly useful for short-duration passive surveys.