Ornstein–Uhlenbeck processes for geophysical data analysis

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.