In this paper, an investigation has been made to detect the self-similarity and stationarity nature of magnitude of occurred Earthquake by exploring the fractal pattern and the variation nature of frequency of the essential parameter, Magnitude of occurred earthquake across the different place of the world. The time series of magnitude (19.04.1997 to 07.11.2017), of occurred earthquakes, collected from U.S.G.S. have been analysed for exposing the nature of scaling (fractality) and stationary behaviour using different statistical methodologies. Four conventional methods namely Visibility Graph Analysis (VGA), Wavelet Variance Analysis (WVA) Higuchi’s Fractal Dimension (HFD) and Detrended Fluctuation Analysis (DFA) are being used for computing the value of Hurst parameter. In addition, Artificial Neural Network, a pre-trained fully connected 3-layer has finally been used to compute the Hurst parameter. It has been perceived that the specified dataset reveals the anti-persistency and Short-Range Dependency (SRD) behaviour. Binary based ADF, KPSS test and Time Frequency Representation based Smoothed Pseudo Wigner-Ville Distribution (SPWVD) test is incorporated in order to explore the nature of stationarity/non-stationarity of the specified profile, which here displays the non-stationary character of the magnitude of earthquake.

Earthquake; Hurst Parameter (H); Fractality; Stationarity; Artificial Neural Network (ANN)

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