A bivariate compound Poisson model for the occurrence of foreshock and aftershock sequences in Turkey

Özel, Gamze

doi:10.1002/env.1098

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…The simultaneous solution of (20) and (21) gives the maximum likelihood estimates (MLEs) of and . However, since the likelihood equations contain nonlinear functions, there is no explicit form for the MLEs of the parameters of PQX distribution.…”

Section: Maximum Likelihoodmentioning

confidence: 99%

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A new approach to model the counts of earthquakes: INARPQX(1) process

2021

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This paper introduces a first-order integer-valued autoregressive process with a new innovation distribution, shortly INARPQX(1) process. A new innovation distribution is obtained by mixing Poisson distribution with quasi-xgamma distribution. The statistical properties and estimation procedure of a new distribution are studied in detail. The parameter estimation of INARPQX(1) process is discussed with two estimation methods: conditional maximum likelihood and Yule-Walker. The proposed INARPQX(1) process is applied to time series of the monthly counts of earthquakes. The empirical results show that INARPQX(1) process is an important process to model over-dispersed time series of counts and can be used to predict the number of earthquakes with a magnitude greater than four.

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Section: Maximum Likelihoodmentioning

confidence: 99%

“…Gospodinov and Rotondi [ 9 ] and Aktas et al [ 1 ] used the compound Poisson process to model the cumulative energy release of mainshocks and the expected value of the earthquake, respectively. Ozel [ 21 ] introduced a bivariate compound Poisson model to predict the number of foreshocks and aftershocks.…”

Section: Introductionmentioning

confidence: 99%

A new approach to model the counts of earthquakes: INARPQX(1) process

2021

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“…In Turkey, the most of earthquakes happen on the North Anatolian Fault Zone (NAFZ) which can damage to many constructions (Sengor et al 1985). It is an active right-lateral system about 1500 km long which bounds to the North Anatolian block (Ozel 2011). It represents a transform margin that mainly follows a pre-existing zone of crustal weakness: a suture zone inherited from an earlier collisional phase (Batuk 2005).…”

Section: Application On the Natural Disasters In Turkeymentioning

confidence: 99%

“…The non-central, central moments, and cumulants of the NTA distribution are computed from (9), (10), and (11) respectively. The results are presented in Table 3.…”

Section: Condition 2: Fit Of the Poisson Distribution To The Number Omentioning

confidence: 99%

Efficacy of Data Fusion Using Convolved Multi-Output Gaussian Processes

Vasudevan¹,

Melkumyan²,

Scheding³

2021

Journal of Data Science

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The statistical modeling of natural disasters is an indispensable tool for extracting information for prevention and risk reduction casualties. The Poisson distribution can reveal the characteristics of 1 a natural disaster. However, this distribution is insufficient for the clustering of natural events and related casualties. The best approach is to use a Neyman type A (NTA) distribution which has the feature that two or more events occur in a short time. We obtain some properties of the NTA distribution and suggest that it could provide a suitable description to analyze the natural disaster distribution and casualties. We support this argument using disaster events, including earthquakes, floods, landslides, forest fires, avalanches, and rock falls in Turkey between 1900 and 2013. The data strongly supports that the NTA distribution represents the main tool for handling disaster data. The findings indicate that approximately three earthquakes, fifteen landslides, five floods, six rock falls, six avalanches, and twenty nine forest fires are expected in a year. The results from this model suggest that the probability of the total number of casualties is the highest for the earthquakes and the lowest for the rock falls. This study also finds that the expected number of natural disasters approximately equals to 64 per year and inter-event time between two successive earthquakes is approximately four months. The inter-event time for the natural disasters is approximately six days in Turkey.

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“…The probability assessment has become feasible on the basis of seismicity models, developed to represent spatiotemporal distribution of earthquakes. Statistical and physical models on earthquake interactions (Ogata 1988(Ogata , 1998Gospodinov and Rotondi 2006;Console et al 2006aConsole et al , 2007Ö zel 2011) have begun to capture many features of natural seismicity, such as aftershock triggering and clustering.…”

Section: Introductionmentioning

confidence: 99%

Seismicity rate modeling for prospective stochastic forecasting: the case of 2014 Kefalonia, Greece, seismic excitation

2015

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We examined the January-February 2014 earthquake doublet (M w = 6.1 and M w = 6.0) and the associated aftershocks which form a seismic excitation adequately well recorded by a dense local seismological network. It started on January 26 with the main shock, causing a lot of panic and followed by numerous aftershocks. The second main shock with M w = 6.0 occurred 7 days later on an along-strike adjacent fault segment. The close proximity of the two main shocks, in both space and time and the intense aftershock sequence, triggered the investigation of the occurrence probability evolution for the stronger aftershocks and possibly a third main shock in the seismic excitation. This purpose was further motivated by the potential of the area for hosting a stronger (M w C 6.0) earthquake based upon both historical information and instrumental data. Aftershock rate modeling was done on subsequent data samples by the restricted epidemic-type aftershock sequence stochastic model, and probabilities for the occurrence of strong (M w C 5.0) earthquakes were calculated during the progress of the aftershock sequence. We executed daily model simulations and probability forecasts for 30 days focusing in more detail on the impact of some model parameters on the prospective forecasting. Trying to be near to a real-time case, all forecasts were done on data up to the moment of forecasting.

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A bivariate compound Poisson model for the occurrence of foreshock and aftershock sequences in Turkey

Cited by 13 publications

References 27 publications

A new approach to model the counts of earthquakes: INARPQX(1) process

A new approach to model the counts of earthquakes: INARPQX(1) process

Efficacy of Data Fusion Using Convolved Multi-Output Gaussian Processes

Seismicity rate modeling for prospective stochastic forecasting: the case of 2014 Kefalonia, Greece, seismic excitation

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