Seismic Hazard Evaluation Using Markov Chains: Application to the Japan Area

Abstract

Seismogenic regions within some geographic area are interrelated through tectonics and seismic history, although this relation is usually complex, so that seismicity in a given region cannot be predicted in a straightforward manner from the activity in other region(s). We present a new statistical method for seismic hazard evaluation based on modeling the transition probabilities of seismicity patterns in the regions of a geographic area during a time interval, as a Markov chain. Application of the method to the Japan area renders good results, considering the occurrence of a high probability transition as a successful forecast. For magnitudes M≥5.5 and time intervals Δ t=0.10 year, the method yields a 78% aftcast (forecast of data already used to evaluate the hazard) success rate for the entire catalog, and an indicative 80% forecast success rate for the last 10 transitions in the catalog. A byproduct of the method, regional occurrence probabilities determined from the transition probabilities, also provides good results; aftcasts of regional activity have a 98% success rate, and those of activity in the highest probability region about 80.5% success rate. All results are superior to those from the null hypotheses (a memory-less Poissonian, fixed-rate, or uniform system) and have vanishingly small probabilities of resulting from purely random guessing.