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In weather forecasting, current and past observational data are routinely assimilated into numerical simulations to produce ensemble forecasts of future events in a process termed “model steering”. Here we describe a similar approach that is motivated by analyses of previous forecasts of the Working Group on California Earthquake Probabilities (WGCEP). Our approach is adapted to the problem of earthquake forecasting using topologically realistic numerical simulations for the strike-slip fault system in California. By systematically comparing simulation data to observed paleoseismic data, a series of spatial probability density functions (PDFs) can be computed that describe the probable locations of future large earthquakes. We develop this approach and show examples of PDFs associated with magnitude M > 6.5 and M > 7.0 earthquakes in California. [ABSTRACT FROM AUTHOR]

Abstract: Topologically realistic simulations of earthquake faults systems have been constructed to understand the physics of interacting earthquake fault systems. We focus on one of these models, a simulation called Virtual California, that represents a model for the strike-slip fault system in California. In weather forecasting, current and past observations are routinely extrapolated forward to forecast future weather. The question addressed in this paper is whether a similar application of numerical simulations can be used in earthquake forecasting. Present simulation models are discussed and their ability to successfully generate earthquake recurrence statistics is demonstrated. An important question relates to how paleoseismic data can be used to constrain simulations, and whether these constrained simulations provide improved forecasts of future earthquakes. Here, we show first results from a consideration of these issues using a method of “datascoring”. The data are divided into “training intervals” and “testing intervals”. In the training intervals, the time history of paleoseismic data are used to evaluate space–time windows of simulations. Earthquakes following high-scoring space–time windows in the simulations are then used as a basis for developing waiting time statistics and used to forecast data in the testing intervals. In our present method, we focus on the problem of determining the timing of future earthquakes having magnitude m >7. Our preliminary conclusion is that the amount of paleoseismic data currently available does not as yet improve the waiting time statistics to a level significantly beyond a random (temporal) predictor. However, this conclusion is based on a set of studies that are not extensive, so further investigations may well reveal important new avenues. In particular, it may be that the true value of this approach lies in defining the probable spatial locations of future earthquakes, rather than their timing. [Copyright &y& Elsevier]

Abstract: Correlations in space and time play a fundamental role in earthquake processes. One direct manifestation of the effects of correlations is the occurrence of aftershocks due to the stress transfer in the vicinity of a main shock. Less obvious and more speculative changes in correlations may occur in the background seismicity before large earthquakes. Using statistical physics it is possible to introduce a measure of spatial correlations through a correlation length. This quantity characterizes how local fluctuations can influence the occurrence of earthquakes over distances comparable with the correlation length. In this work, the physical basis of spatial correlations of earthquakes is discussed in the context of critical phenomena and the percolation problem. The method of two-point correlation function is applied to the seismicity of California. Well defined variations in time of the correlation length are found for aftershock sequences and background seismicity. The scaling properties of our obtained distributions are analyzed with respect to changes in several scaling parameters such as lower magnitude cutoff of earthquakes, the maximum time interval between earthquakes, and the spatial size of the area considered. This scaling behavior can be described in a unified manner by utilizing the multifractal fit. Utilizing the percolation approach the time evolution of clusters of earthquakes is studied with the correlation length defined in terms of the radius of gyration of clusters. This method is applied to the seismicity of California. [Copyright &y& Elsevier]