Earthquake Counting Method for Spatially Localized Probabilities: Challenges in Real-Time Information Delivery

Abstract

We develop and implement a new type of global earthquake forecast. Our forecast is a perturbation on a smoothed seismicity (Relative Intensity) spatial forecast combined with a temporal time-averaged (Poisson) forecast. A variety of statistical and fault-system models have been discussed for use in computing forecast probabilities. Our paper takes a new approach. The idea is based on the observation that GR statistics characterize seismicity for all space and time. Small magnitude event counts (quake counts) are used as markers for the approach of large events. More specifically, if the GR b-value = 1, then for every 1000 M>3 earthquakes, one expects 1 M>6 earthquake. So if ~1000 M>3 events have occurred in a spatial region since the last M>6 earthquake, another M>6 earthquake should be expected soon. In physics, event count models have been called natural time models, since counts of small events represent a physical or natural time scale characterizing the system dynamics. In a previous paper, we used conditional Weibull statistics to convert event counts into a temporal probability for a given fixed region. In the present paper, we dispense with a fixed region, and develop a method to compute these Natural Time Weibull (NTW) forecasts on a global scale, using an internally consistent method, in regions of arbitrary shape and size. Among the results we find that the Japan region is at serious risk for a major (M>8) earthquake over the next year or two, a result that also follows from considering completeness of the Gutenberg-Richter relation.