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Recurrence quantification analysis for detecting dynamical changes in earthquake magnitude time series

Author

Listed:
  • Min Lin

    (School of Mathematical Sciences, Ocean University of China, Qingdao 266100, P. R. China)

  • Gang Zhao

    (College of Civil Aviation, NanJing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China;
    Faculty of Transportation Engineering of Huaiyin Institute of Technology, Huai'an 223003, P. R. China)

  • Gang Wang

    (Key Laboratory of Data Analysis and Applications (LDAA), The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, P. R. China)

Abstract

In this study, recurrence plot (RP) and recurrence quantification analysis (RQA) techniques are applied to a magnitude time series composed of seismic events occurred in California region. Using bootstrapping techniques, we give the statistical test of the RQA for detecting dynamical transitions. From our results, we find the different patterns of RPs for magnitude time series before and after the M6.1 Joshua Tree Earthquake. RQA measurements of determinism (DET) and laminarity (LAM) quantifying the order with confidence levels also show peculiar behaviors. It is found that DET and LAM values of the recurrence-based complexity measure significantly increase to a large value at the main shock, and then gradually recovers to a small values after it. The main shock and its aftershock sequences trigger a temporary growth in order and complexity of the deterministic structure in the RP of seismic activity. It implies that the onset of the strong earthquake event is reflected in a sharp and great simultaneous change in RQA measures.

Suggested Citation

  • Min Lin & Gang Zhao & Gang Wang, 2015. "Recurrence quantification analysis for detecting dynamical changes in earthquake magnitude time series," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(07), pages 1-13.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:07:n:s0129183115500771
    DOI: 10.1142/S0129183115500771
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    Cited by:

    1. Giovanna Zimatore & Maria Chiara Gallotta & Matteo Campanella & Piotr H. Skarzynski & Giuseppe Maulucci & Cassandra Serantoni & Marco De Spirito & Davide Curzi & Laura Guidetti & Carlo Baldari & Stavr, 2022. "Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review," IJERPH, MDPI, vol. 19(19), pages 1-24, October.

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