Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
Volume 22, issue 5
Nonlin. Processes Geophys., 22, 589–599, 2015
https://doi.org/10.5194/npg-22-589-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 22, 589–599, 2015
https://doi.org/10.5194/npg-22-589-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Brief communication 09 Oct 2015

Brief communication | 09 Oct 2015

Brief Communication: Earthquake sequencing: analysis of time series constructed from the Markov chain model

M. S. Cavers1 and K. Vasudevan1,2 M. S. Cavers and K. Vasudevan
  • 1Department of Mathematics and Statistics, University of Calgary, Calgary, AB T2N 1N4, Canada
  • 2Department of Geoscience, University of Calgary, Calgary, AB T2N 1N4, Canada

Abstract. 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, 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 behaviour. 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 of 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.

Publications Copernicus
Download
Short summary
We introduced a new modified Markov chain model to generate a time series of the earthquake sequences from a global catalogue with an optimum time sampling of 9 days. Here, we subject the time series to a known analysis method namely an ensemble empirical mode decomposition to study the state-to-state fluctuations in each of the intrinsic mode functions. Also, we establish the power-law behaviour of the time series with the Fano factor and Allan factor used in time-correlative behaviour studies
We introduced a new modified Markov chain model to generate a time series of the earthquake...
Citation