Assessing Markovian Models for Seismic Hazard and Forecasting

We propose the use of statistical measures to quantify robustness, uncertainty, and significance for Markovian models of large magnitude seismic systems, and we also propose a method for choosing the best of different models by using the normalized measures in a discriminant function. We tested the...

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Bibliographic Details
Published in:	Pure and applied geophysics Vol. 178; no. 3; pp. 847 - 863
Main Authors:	Gutierrez Peña, Q. J., Nava Pichardo, F. A., Glowacka, E., Castro Escamilla, R. R., Márquez Ramírez, V. H.
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01-03-2021 Springer Nature B.V
Subjects:	Discriminant analysis Earth and Environmental Science Earth Sciences Earthquakes Geological hazards Geophysics/Geodesy Seismic activity Seismic hazard Seismicity Statistical analysis seismic hazard Markov chains
Online Access:	Get full text
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Summary:	We propose the use of statistical measures to quantify robustness, uncertainty, and significance for Markovian models of large magnitude seismic systems, and we also propose a method for choosing the best of different models by using the normalized measures in a discriminant function. We tested the proposed methods on earthquakes occurring in an area around Japan, divided into four regions; modeling the system as having four states, where each state corresponds to the region where the latest large earthquake, larger than a given threshold moment magnitude, has occurred. Our results show that for the 7.0–7.3 threshold magnitude range the seismicity of this region does occur according to a Markovian process, with optimum results for threshold magnitude 7.1, whereas for magnitudes outside this range seismicity is less Markovian.
ISSN:	0033-4553 1420-9136
DOI:	10.1007/s00024-021-02686-2