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Generation of synthetic catalog by using Markov chain Monte Carlo simulation and inverse Poisson distribution

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Abstract

A synthetic seismic catalog assists not only in reducing the uncertainties in computations of seismic hazard, but also in simulating the future seismic events, which, if modeled accordingly, provides a forecast model. The seismicity forecast provides additional time-dependent information that may complement the seismic hazard. Within this context, in an attempt to generate a synthetic catalog and simulate future seismicity at the same time, Markov chain Monte Carlo (MCMC) simulation techniques are employed. The temporal distribution of earthquakes is modeled through hidden Markov model (HMM) and periods with different inter-event time distributions are determined, which are then assigned with different states. Along with the global magnitude and spatial distribution, the inter-event time distribution for each state is used to simulate future events with magnitude, occurrence location, and time assigned accordingly. In the end, a synthetic catalog is generated which indeed is a detailed forecast as well.

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  1. Department of Architecture, Nigde Ömer Halisdemir University, Nigde, Turkey

    Hakan Karaca

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  1. Hakan Karaca
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Correspondence to Hakan Karaca.

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Article highlights

• As one of the most crucial patterns, the temporal distribution patterns of earthquake rates has to be identified and modeled with the right methods before projecting to the future.

• If the temporal distribution pattern of earthquake rates does not follow stationary character and the Poisson distribution, then a Markov chain technique has to be introduced for the identification of the overlapping Poisson models that make up the overall temporal distribution pattern.

• The overall magnitude distribution characteristics of a region should not be accepted as valid for the entire area; hence, any simulation study must take the spatial distribution of magnitude-frequency relationship into consideration.

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Karaca, H. Generation of synthetic catalog by using Markov chain Monte Carlo simulation and inverse Poisson distribution. J Seismol 25, 1103–1114 (2021). https://doi.org/10.1007/s10950-021-10018-z

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