Sensitivity Kernels for Earthquake Coda-Correlation Wavefield Features and Applications to Global Seismology

Summary

Seismic coda-correlation methods have revealed numerous body-wave-like features in globally stacked correlograms, offering new opportunities to constrain the structure of Earth’s deep interior. Unlike the common assumptions about the diffuse ambient noise field that enable reconstruction of surface waves, earthquake coda-correlation features arise from cross-terms in reverberating body waves of common slowness that share a subset of propagation legs. However, quantitative interpretation of these features for tomographic imaging has been hindered by the lack of distributed sensitivity kernels relating observed correlation signals to Earth heterogeneities. Here, we develop a forward modeling framework for the earthquake coda-correlation wavefield that bypasses conventional cross-correlation of late-coda waveforms. Our approach simulates correlation features directly by treating the time-reversed generating wavefield as a distributed source throughout Earth’s interior. The forward-modeled correlograms reproduce features obtained through conventional stacking with a significantly improved signal-to-noise ratio. Building on this framework, we derive finite-frequency traveltime banana-doughnut sensitivity kernels using adjoint methods, which quantify how correlation time measurements of correlation features depend on perturbations in P-wave velocity, S-wave velocity, and density. We compute sensitivity kernels for prominent correlation features (P*, ScS*, K*, and others) at station separations ranging from 30° to 180°. The kernels reveal extensive sampling of Earth’s mantle, outer core, and inner core, with spatial-sensitivity patterns fundamentally different from those of classical body-wave tomography. For antipodal station pairs, correlation features exhibit strong sensitivity throughout the deep interior, including regions poorly sampled by traditional seismic phases. Our results confirm that correlation features form through constructive interference of body-wave pairs with similar slowness, rather than representing Green’s functions between station pairs. This work establishes the theoretical foundation for coda-correlation tomography, enabling future three-dimensional imaging of Earth’s internal structure with unprecedented sampling of the deep interior. The sensitivity kernels provide a pathway to exploit the wealth of information contained in earthquake coda for high-resolution mantle and core tomography.