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Insight Into Earthquake Sequencing: Analysis and Interpretation of Time-series Constructed from the Directed Graph of the Markov Chain Model : Volume 2, Issue 1 (24/02/2015)

By Cavers, M. S.

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Book Id: WPLBN0004020139
Format Type: PDF Article :
File Size: Pages 26
Reproduction Date: 2015

Title: Insight Into Earthquake Sequencing: Analysis and Interpretation of Time-series Constructed from the Directed Graph of the Markov Chain Model : Volume 2, Issue 1 (24/02/2015)  
Author: Cavers, M. S.
Volume: Vol. 2, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Vasudevan, K., & Cavers, M. S. (2015). Insight Into Earthquake Sequencing: Analysis and Interpretation of Time-series Constructed from the Directed Graph of the Markov Chain Model : Volume 2, Issue 1 (24/02/2015). Retrieved from http://hawaiilibrary.net/


Description
Description: Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada. Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time-series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time-series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, the orthogonal basis set derived from the time-series using the EEMD, to a detailed analysis to draw information-content of the time-series. Also, we investigate the influence of random-noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behavior. Here, we extend the Fano factor and Allan factor analysis to the time-series of state-to state transition frequencies of a Markov chain. Our results support not only the usefulness the intrinsic mode functions in understanding the time-series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

Summary
Insight into earthquake sequencing: analysis and interpretation of time-series constructed from the directed graph of the Markov chain model

Excerpt
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