Generation of synthetic catalog by using Markov chain Monte Carlo simulation and inverse Poisson distribution
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
MCMC algorithm, Synthetic catalog, Seismicity, Pattern recognition, Spatial smoothing
Kaynak
Journal of Seismology
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
25
Sayı
4
