Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)

Can, Ceren Eda; Ergun, Gul; Gökçeoğlu, Candan

doi:10.2478/s13533-012-0180-1

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…The seismicity data commonly used in seismic activity studies is the earthquake frequencies with a certain magnitude threshold and time interval [15][16][17]21,22]. In other words, the characteristics of the seismicity data are categorized as discrete variables.…”

Section: Introductionmentioning

confidence: 99%

Seismic activity analysis of five major earthquake source segments in the Sumatra megathrust zone

Rizal

Gunawan

Indratno

et al. 2023

BNZSEE

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The Sumatra megathrust zone has five major earthquake sources, namely the Aceh-Andaman, Nias-Simeulue, Mentawai-Siberut, Mentawai-Pagai, and Enggano segments. This paper provides seismic activity analysis in these five segments via an unobserved process study of tectonic plate movements, which is conducted in two cases: each of the five segments independently (Case 1), and a pair of two adjacent segments (Case 2). To do this, two specific types of Hidden Markov Models (HMMs), i.e., Poisson-HMMs and Copula-HMMs, dealing with unobserved process issues, are applied. In practice, the data used is the annual frequency of mainshock earthquakes with a magnitude of >4.6 that occurred from 1971 to 2018. This data is obtained by working out the declustering process and estimating the magnitude of completeness from a particular earthquake catalogue. Due to the incompleteness of the data sets, the parameters of the two HMMs are estimated using the Expectation-Maximization algorithm. Results show that for Case 1, the model that fits the data for each of the five segments is the 3-state Poisson-HMM. The three states, in this instance, stand for the rates of seismic activity that correspond to the dynamic level of tectonic plate movements. Furthermore, in Case 2, the selected model for the Aceh-Andaman with Nias-Simeulue is the 2-state Gumbel Copula-HMM. Meanwhile, for the three groups remaining, namely Nias-Simeulue with Mentawai-Siberut, Mentawai-Siberut with Mentawai-Pagai, and Mentawai-Pagai with Enggano, the appropriate models are Gaussian, Gumbel, and Frank Copulas, respectively. In this case, the number of states represents the seismic activity association of two adjacent segments that corresponds to the association level of two adjacent tectonic plate dynamics.

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Section: Introductionmentioning

confidence: 99%

Seismic activity analysis of five major earthquake source segments in the Sumatra megathrust zone

Rizal

Gunawan

Indratno

et al. 2023

BNZSEE

View full text Add to dashboard Cite

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“…The earliest applications of Poisson-HMMs date back to the early 1900s with scholars such as Albert 45 , Leroux 46 , Le et al 47 , Leroux and Puterman 48 developing it to study time series of epileptic seizure counts. In more recent times, Can et al 49 used Poisson-HMMs to estimate earthquake (magnitude four or higher on the Richter scale) hazards over the period 2013-2047 in north-western Turkey. Orfanogiannaki et al 50 applied the Poisson-HMM to model earthquake frequencies in the seismogenic area of Killini, Ionian Sea, Greece, between the period 1990 and 2006.…”

Section: Methodsmentioning

confidence: 99%

On modifications to the Poisson-triggered hidden Markov paradigm through partitioned empirical recurrence rates ratios and its applications to natural hazards monitoring

Bhaduri

2020

Sci Rep

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Hidden Markov models (HMMs), especially those with a Poisson density governing the latent state-dependent emission probabilities, have enjoyed substantial and undeniable success in modeling natural hazards. Classifications among these hazards, induced through quantifiable properties such as varying intensities or geographic proximities, often exist, enabling the creation of an empirical recurrence rates ratio (ERRR), a smoothing statistic that is gradually gaining currency in modeling literature due to its demonstrated ability in unearthing interactions. Embracing these tools, this study puts forth a refreshing monitoring alternative where the unobserved state transition probability matrix in the likelihood of the Poisson based HMM is replaced by the observed transition probabilities of a discretized ERRR. Analyzing examples from Hawaiian volcanic and West Atlantic hurricane interactions, this work illustrates how the discretized ERRR may be interpreted as an observed version of the unobserved hidden Markov chain that generates one of the two interacting processes. Surveying different facets of traditional inference such as global state decoding, hidden state predictions, one-out conditional distributions, and implementing related computational algorithms, we find that the latest proposal estimates the chances of observing a high-risk period, one threatening several hazards, more accurately than its established counterpart. Strongly intuitive and devoid of forbidding technicalities, the new prescription launches a vision of surer forecasts and stands versatile enough to be applicable to other types of hazard monitoring (such as landslides, earthquakes, floods), especially those with meager occurrence probabilities.

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“…This assumption is similar to the assumption of localized likelihoods and is called the 1st order Markov Property with respect to data. HMM has found applications in computational biology (e.g., Krogh et al 1994;Liang et al 2007;Pachter et al 2002;Shih et al 2015;Yoon 2009), natural language processing (e.g., Collins 2002;Nivre 2002;Sun et al 2012), speech recognition (e.g., Dymarski 2011; Rabiner 1989), computer vision (e.g., Li et al 2000;Othman & Aboulnasr 2003;Baumgartner et al 2013), earthquake seismology (e.g., Alasonati et al 2006;Beyreuther & Wassermann 2008;Can et al 2014), petroleum geoscience (e.g., Eidsvik et al 2004;Lindberg & Omre & 2015) and many other fields of research. all of these approaches for facies inversion is that they are based on inference from full posterior distribution which must be explored through simulation (sampling) based inference, e.g., using McMC methods which suffer from the convergence and bias problems described earlier.…”

Section: Hidden Markov Chain (1d-hmm)mentioning

confidence: 99%

Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model

Nawaz¹,

Curtis²

2016

Geophys. J. Int.

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Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of two-dimensional spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P-and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell-facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations-an assumption referred to as localized likelihoods. The hidden state (facies) at a location cannot be determined solely by the observation at that location as it also depends on prior information concerning the spatial distribution of other hidden states elsewhere. The prior information is included in the inversion in the form of a training image which represents a conceptual depiction of local geologies that might be expected, but other forms of prior information can be used in the method as desired. The method provides direct estimates of posterior marginal probability distributions over each

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Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)

Cited by 5 publications

References 39 publications

Seismic activity analysis of five major earthquake source segments in the Sumatra megathrust zone

Seismic activity analysis of five major earthquake source segments in the Sumatra megathrust zone

On modifications to the Poisson-triggered hidden Markov paradigm through partitioned empirical recurrence rates ratios and its applications to natural hazards monitoring

Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model

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