Detection of a Non-local Seismicity Pattern Prior to Larger-size Earthquakes in Taiwan

Abstract

In this paper, we estimate the varying probability of the occurrence of earthquakes with magnitudes greater than 6 by using a pattern dynamic algorithm. The pattern dynamic algorithm extracts information by constructing a pattern state vector evaluated from past seismic activities. The pattern state vector is expressed as a unit vector in a high-himensional correlation space. To reduce the bias in constructing a pattern state vector, we only use high quality data provided by the Central Weather Bureau in Taiwan. Earthquakes that occurred from 1973 to 1997 are used as a "learning catalogue" to evaluate the future seismicity patterns Using a long-likelihood function, we found that the optimized time span of 8 to 10 years is appropriate for a learning catalogue. The spatial distributions of increasing probability for each time span for the earthquakes after 1998 is estimated. Results show that most of the larger-size earthquakes that occurred inland after 1998 were located in the estimated areas. Earthquakes in the eastern, offshore Taiwan area, where the data quality is poor, could not be well located.

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