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Ornstein–Uhlenbeck processes for geophysical data analysis

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  • Habtemicael, Semere
  • SenGupta, Indranil

Abstract

In this work a three parameter stochastic process, termed the Gamma-Ornstein–Uhlenbeck process, has been implemented to analyze geophysical data. Such non-Gaussian Ornstein–Uhlenbeck processes offer the possibility of capturing important distributional deviations from Gaussianity and make the model flexible of dependence structures. It is shown that the Gamma-Ornstein–Uhlenbeck process is a possible candidate for earthquake data modeling. Rigorous regression analysis is provided and based on that the first-passage times are computed for different sets of data. It is shown that this model may be used to estimate parameters related to some major events—namely major earthquakes.

Suggested Citation

  • Habtemicael, Semere & SenGupta, Indranil, 2014. "Ornstein–Uhlenbeck processes for geophysical data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 147-156.
    • Handle: RePEc:eee:phsmap:v:399:y:2014:i:c:p:147-156
    DOI: 10.1016/j.physa.2013.12.050
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    References listed on IDEAS

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    3. Maria P. Beccar-Varela & Md Al Masum Bhuiyan & Maria C. Mariani & Osei K. Tweneboah, 2019. "Analytic Methods for Solving Higher Order Ordinary Differential Equations," Mathematics, MDPI, vol. 7(9), pages 1-17, September.

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