Stochastic characterization of mesoscale seismic velocity heterogeneity in Long Beach, California

Nori Nakata, & Greg C. Beroza

Published September 23, 2015, SCEC Contribution #6012

Earth's seismic velocity structure is heterogeneous at all scales, and mapping that heterogeneity provides insight into the processes that create it. At large scale lengths, seismic tomography is used to map Earth structure deterministically. At small scale lengths, structure can be imaged deterministically, but because it is impractical to image short-wavelength heterogeneity everywhere, we often resort to statistical methods to depict its variability. In this study, we develop random-field model representations of a 3D P-wave velocity model at Long Beach, California, estimated from dense-array recordings of the ambient seismic wavefield. We focus on heterogeneity at the mesoscale, which is smaller than 10+ km scale of regional tomography but larger than the \textit{micro} scale of borehole measurement. We explore four ellipsoidally anisotropic heterogeneity models, including von Karman, Gaussian, self-affine and Kummer models, based on their autocorrelation functions. We find that the von Karman model fits the imaged velocity model best among these options with a correlation length in the horizontal direction about five times greater than in the vertical direction, and with strong small-scale length variations. We validate our results by showing that our model accurately predicts the observed decay of scattered waves in the coda of a nearby earthquake, suggesting that quantitative measures of velocity variability will be useful for predicting high frequency ground motion in earthquakes.

Key Words
spatial analysis, fractals and multifractals, probability distributions, earthquake ground motions, coda waves, statistical seismology

Citation
Nakata, N., & Beroza, G. C. (2015). Stochastic characterization of mesoscale seismic velocity heterogeneity in Long Beach, California. Geophysical Journal International, 203(3), 2049-2054. doi: 10.1093/gji/ggv421.

Related Projects & Working Groups
Ground-Motion Prediction