Multicomponent C3 Green's Functions for Improved Long-Period Ground-Motion Prediction

The virtual earthquake approach to ground-motion prediction uses Green's functions (GFs) determined from the ambient seismic field to predict long-period shaking from scenario earthquakes. The method requires accurate relative GF amplitudes between stations and among components; however, the amplitudes of ambient-field GFs are known to be subject to biases from uneven source distribution. We show that multicomponent, higher order cross correlations are significantly less biased than the conventional first-order cross correlation, and we demonstrate that they provide a more reliable prediction of observed ground-motion amplitudes for a recent moderate earthquake on the San Jacinto fault in southern California.