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Identifying anomalous signals in GPS data using HMMs: An increased likelihood of earthquakes?

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  • Wang, Ting
  • Bebbington, Mark

Abstract

A way of combining a hidden Markov model (HMM) and mutual information analysis is proposed to detect possible precursory signals for earthquakes from Global Positioning System (GPS) data. A non-linear filter, which measures the short-term deformation rate ranges, is introduced to extract anomalous signals from the GPS measurements of ground deformation. An HMM fitted to the filtered GPS measurements can classify the deformation data into different states which form proxies for elements of the earthquake cycle. Mutual information is then used to examine whether any of these states possesses any precursory characteristics. The class of GPS measurements identified by the HMM as having the largest variation of deformation rate shows some precursory information and is hence considered as a “precursory state”. The performance of possible earthquake forecasts is assessed by comparing a decision rule (based on model characteristics) with the actual outcome.

Suggested Citation

  • Wang, Ting & Bebbington, Mark, 2013. "Identifying anomalous signals in GPS data using HMMs: An increased likelihood of earthquakes?," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 27-44.
    • Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:27-44
    DOI: 10.1016/j.csda.2011.09.019
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    References listed on IDEAS

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    Cited by:

    1. Ting Wang & Jiancang Zhuang & Kazushige Obara & Hiroshi Tsuruoka, 2017. "Hidden Markov modelling of sparse time series from non-volcanic tremor observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 691-715, August.
    2. Amina Shahzadi & Ting Wang & Mark Bebbington & Matthew Parry, 2023. "Inhomogeneous hidden semi-Markov models for incompletely observed point processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 253-280, April.

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