Construction of Empirical Green’s Functions from Direct Waves, Coda Waves, and Ambient Noise in Se Tibet

Empirical Green’s functions (EGFs) between receivers can be obtained from seismic interferometry through cross-correlation of pairs of ground motion records. Full reconstruction of the Green’s function requires diffuse wavefields or a uniform distribution of (noise) sources. In practice, EGFs differ from actual Green’s functions because wavefields are not diffuse and the source-distribution not uniform. This difference, which may depend on medium heterogeneity, complicates (stochastic) medium characterization as well as imaging and tomographic velocity analysis with EGFs. We investigate how source distribution and scale lengths of medium heterogeneity influence Green’s function reconstruction in the period band of primary microseisms (t = 10− 20 s). With data from a broadband seismograph array in SE Tibet we analyze the symmetry and travel-time properties of EGFs from correlation of data in different windows: ambient noise, direct minor or major arc surface waves, and surface wave coda. The EGFs from these different windows show similar dispersion characteristics, which demonstrates that the Green’s function can be recovered from direct wavefields (e.g., ambient noise or earthquakes) or from wavefields scattered by heterogeneity on a regional scale. Late surface wave coda is more diffuse than the early surface wave coda and is generally expected to produce a more symmetric EGF. We show, however, that directional bias is also manifest in EGFs from late coda and that this bias is similar to that in EGFs from ambient noise. This suggests that (in the period band studied) late coda is dominated not by signal from multiple (local) scattering of surface waves but by ambient noise (for instance from oceans). Directional bias and signal-to-noise ratio of EGFs can be understood better with (plane wave) beamforming of the energy contributing to EGF construction. Beamforming also demonstrates that seasonal variations in cross-correlation functions correlate with changes in ocean activity.