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Techniques for Producing Spatially Correlated Conditional Random Realizations of Ground Motion Fields

For both scenario and real earthquakes, it is important to estimate the resulting shaking intensity in the near-epicentral region. These estimates can be constrained with ground motion recordings from real earthquakes, as is currently done in near-real-time by the USGS’s ShakeMap software. For scenario earthquakes, the ground motions are only estimated by the application of empirical models. The ground motion estimates are expressed as maps of the mean and standard deviation of the estimated intensity, but the consequences of the shaking on society and the built environment depend critically on the spatially correlated aleatory variability of the ground motions. This variability has been accounted for by generating random, spatially correlated realizations of the ground motions. Methods for generating these realizations, however, are computationally demanding, especially when the estimates are conditioned on numerous observed intensity values. In this presentation, a new and approximate conditional simulation approach is applied for use in ShakeMap. This approach, termed circulant embedding (CE), is a fast and exact method for simulating Gaussian processes that takes advantage of the Fast Fourier Transform for computing eigenvalues and eigenvectors. However, traditional CE is restricted to simulating stationary Gaussian processes (possibly anisotropic) on regularly spaced grids. It is also known that if the range parameter of a spatial process is large relative to the domain, this method fails. In this work we explore two new algorithms that adapt CE for (a) irregularly spaced data points, (b) methods for working with large range parameters in order for CE to be widely applicable. We also illustrate the computational efficiency of this approach relative to previous methods. These ideas are illustrated with ground motion intensity measures and also validated using Monte Carlo simulation.

Presenting Author: Maggie Bailey

Student Presenter: Yes

Day: 4/23/2021

Time: 3:45 PM - 4:45 PM Pacific

Additional Authors