Time-Dependent Earthquake Forecasts on a Global Scale

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. An example is the Working Group on California Earthquake Probabilities, which has been using fault-based models to compute conditional probabilities in California since 1988. An example of a forecast is the Epidemic-Type Aftershock Sequence (ETAS), which is based on the Gutenberg-Richter (GR) magnitude-frequency law, the Omori aftershock law, and Poisson statistics. The method discussed in this talk 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 research, we used conditional Weibull statistics to convert event counts into a temporal probability for a given fixed region. In the present paper, we move belyond 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. We develop and implement these methods on a modern web-service computing platform, which can be found at www.openhazards.com and www.quakesim.org. We also discuss constraints on the User Interface (UI) that follow from practical considerations of site usability.