scholarly journals Identification of temporal patterns in the seismicity of Sumatra using Poisson Hidden Markov models

2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Katerina Orfanogiannaki ◽  
Dimitris Karlis ◽  
Gerassimos A. Papadopoulos

On 26 December 2004 and 28 March 2005 two large earthquakes occurred between the Indo-Australian and the southeastern Eurasian plates with moment magnitudes Mw=9.1 and Mw=8.6, respectively. Complete data (<em>mb</em>≥4.2) of the post-1993 time interval have been used to apply Poisson Hidden Markov models (PHMMs) for identifying temporal patterns in the time series of the two earthquake sequences. Each time series consists of earthquake counts, in given and constant time units, in the regions determined by the aftershock zones of the two mainshocks. In PHMMs each count is generated by one of <em>m</em> different Poisson processes that are called states. The series of states is unobserved and is in fact a Markov chain. The model incorporates a varying seismicity rate, it assigns a different rate to each state and it detects the changes on the rate over time. In PHMMs unobserved factors, related to the local properties of the region are considered affecting the earthquake occurrence rate. Estimation and interpretation of the unobserved sequence of states that underlie the data contribute to better understanding of the geophysical processes that take place in the region. We applied PHMMs to the time series of the two mainshocks and we estimated the unobserved sequences of states that underlie the data. The results obtained showed that the region of the 26 December 2004 earthquake was in state of low seismicity during almost the entire observation period. On the contrary, in the region of the 28 March 2005 earthquake the seismic activity is attributed to triggered seismicity, due to stress transfer from the region of the 2004 mainshock.

2018 ◽  
Vol 40 (3) ◽  
pp. 1199
Author(s):  
K. Orfanogiannaki ◽  
D. Karlis ◽  
G. A. Papadopoulos

On 26 December 2004 and 28 March 2005 occurred two of the largest earthquakes of the last 40 years between the Indo-Australian and the southeastern Eurasian plates, with moment magnitudes Mw=9.1 and Mw= 8.6 respectively. Complete data (mb > 4.2) of the post-1993 time interval (Fig. 1) have been used to apply Poisson Hidden Markov Models (PHMM in identifying temporal patterns in the time series of the two main shocks. Each time series consists of earthquake counts, in given and constant time units, in the regions determined by the aftershock zones of the two main shocks. In PHMM each count is generated by one of m Poisson processes, that are called states. The series of states is unobserved and is, in fact a Markov chain. The model incorporates a varying seismicity rate; it assigns a different rate to each state, and detects the changes of the rate over time. In PHMM, unobserved factors related to the local properties of the region, affect the earthquake occurrence rate. Estimation and interpretation of the unobserved sequence of states that underlie the data contribute to a better understanding of the geophysical processes that take place in the region. We applied PHMM to the time series of earthquakes preceding the two main shocks, and we estimated the unobserved sequences of states that underlie the data. The results showed that the region of the 26 December 2004 earthquake was in state of low seismicity during about 400 days before the earthquake occurrence. On the contrary, in the region of the 28 March 2005 earthquake a transition from a state of low seismicity to a state of high seismicity was observed immediately after the occurrence of the big earthquake of 26 December 2004.


2019 ◽  
Vol 24 (1) ◽  
pp. 14 ◽  
Author(s):  
Luis Acedo

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.


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