SCEC Award Number 15213 View PDF
Proposal Category Individual Proposal (Integration and Theory)
Proposal Title Characterizing Spatial Variability of Ground Motion Using Very Dense Arrays
Investigator(s)
Name Organization
Gregory Beroza Stanford University
Other Participants Nori Nakata (postdoctoral associate)
SCEC Priorities 6c, 6d, 6e SCEC Groups Seismology, USR, GMP
Report Due Date 03/15/2016 Date Report Submitted 03/14/2016
Project Abstract
 Knowledge of small-scale heterogeneities of Earth’s structure is essential for high-frequency ground motion prediction. Seismic tomography is useful for mapping the heterogeneity of seismic velocities, but due to the limitation of wavelengths used, the resolution of obtained images tends to be kilometers in scale. In this study, we develop random-field 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 micro scale of borehole measurements. We explore four ellipsoidally anisotropic heterogeneity models based on their autocorrelation functions and find that the von Ka ́rma ́n model fits the deterministically 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.
Intellectual Merit We estimate the statistical characteristics of the P-wave velocity heterogeneity at Long Beach, California. The potential impact of this study is that we extend the limit of the scale of heterogeneity estimation based on seismic waves (e.g., Nolet and Dahlen, 2000) by using stochastic characterization. With this approach, we can obtain data-driven random heterogeneity parameters, and these parameters are necessary for more accurate high-frequency ground motion prediction. For example, we can use these parameters for numerical simulation of wave propagation. The very dense array has an important role in this approach because we have more data points at larger wave-lengths, which means we can accurately estimate the small-scale stochastic heterogeneities.
Broader Impacts Thanks to technological development, cheap and user-friendly sensors are available, such dense arrays are becoming more common, and they represent an important new tool to improve our understanding of high frequency strong ground motion. We can deploy these arrays where we are interested in and use our developed technique to predict high frequency ground motion.
Exemplary Figure Figure 3: (a) An example of waveforms generated by a nearby earthquake (4.5 km east and 11.1 km deep) at one station (0.5–16 Hz) and (b– c) mean-square envelope of the earthquake waveforms compared with the synthetic envelopes computed by radiative transfer theory with the estimated von Ka ́rma ́n model at the frequency ranges of
(b) 8–16 Hz and (c) 4–8 Hz. Radiative transfer theory is based on scalar waves and the envelopes for P and S waves (red and blue lines, respectively) are computed separately (after Nakata and Beroza, 2015)