Seismic spatiotemporal characteristics in the Alpide Himalayan Seismic Belt

Due to the extremely complex regional geological tectonic movements in the Alpide Himalayan seismic belt with the dense population and rapid economic development, the frequent seismic activity has caused huge concern. In geology, the research on the spatiotemporal characteristics of seismic activity in different areas at different scales has always been valuable to explore the mechanism of seismic activity for further simulation and prediction of the seismic activity. Using the spatial autocorrelation analysis in this study, we analyzed the spatiotemporal dynamic characteristics of seismic activity in the Alpide Himalayan seismic belt. According to the historical earthquake list (from 1973 to 2017), the data are divided into five periods. Compared with the commonality and diversity of the spatiotemporal characteristics of seismic activity in different periods, it shows that: there is a positive spatial autocorrelation in each period, which proves the spatial aggregation of the seismic activity in the Alpide Himalayan seismic belt. Nevertheless, over time, the spatial aggregation distribution is gradually diminishing from 1973 to 2017; From the Local Spatial Autocorrelation analysis, the spatial aggregation characteristics of seismic activity change with time and have a significant differentiation among the regions. According to the comparison of Local Spatial Autocorrelation characteristics in different periods, seismic activities in the Alpide Himalayan seismic belt were consistent with the periodic characteristics. Assuming the alternating seismic calm period and active period, we could notice that it turned from the active period to the calm period around 2008, which is the most recent turning point between the active and calm periods. At last, the spatio-temporal dynamic characteristics of the seismic activity in the Alpide Himalayan seismic belt is not only consistent with the formation mechanism of seismic activity but also represents the spatial disparity of the geological tectonic movements. It is the feasibility and effectiveness to explore the seismic mechanism based on spatio-temporal dynamic characteristics of the seismic activity.

[1]  D. McKenzie Active tectonics of the Alpine—Himalayan belt: the Aegean Sea and surrounding regions , 1978 .

[2]  Beniamino Murgante,et al.  Geostatistics in Historical Macroseismic Data Analysis , 2009, Trans. Comput. Sci..

[3]  Y. Ogata,et al.  Analysis of temporal and spatial heterogeneity of magnitude frequency distribution inferred from earthquake catalogues , 1993 .

[4]  Yu Zhou,et al.  Wavelet analysis of the temporal-spatial distribution in the Eurasia seismic belt , 2017, Int. J. Wavelets Multiresolution Inf. Process..

[5]  Beniamino Murgante,et al.  Integrated Geological, Geomorphological and Geostatistical Analysis to Study Macroseismic Effects of 1980 Irpinian Earthquake in Urban Areas (Southern Italy) , 2009, ICCSA.

[6]  K. Abe Magnitudes of large shallow earthquakes from 1904 to 1980 , 1981 .

[7]  Rodolfo Console,et al.  Refining earthquake clustering models , 2003 .

[8]  David Vere-Jones,et al.  Some models and procedures for space-time point processes , 2009, Environmental and Ecological Statistics.

[9]  Michael Tiefelsdorf,et al.  Global and local spatial autocorrelation in bounded regular tessellations , 2000, J. Geogr. Syst..

[10]  Khalid Al-Ahmadi,et al.  Spatial Autocorrelation of Cancer Incidence in Saudi Arabia , 2013, International journal of environmental research and public health.

[11]  L. Telesca,et al.  Long-range correlations in two-dimensional spatio-temporal seismic fluctuations , 2007 .

[12]  Jürgen Kurths,et al.  Recurrence networks—a novel paradigm for nonlinear time series analysis , 2009, 0908.3447.

[13]  L. Telesca A non‐extensive approach in investigating the seismicity of L’Aquila area (central Italy), struck by the 6 April 2009 earthquake (ML = 5.8) , 2010 .

[14]  K. Kaila,et al.  Model of earthquake-energy periodicity in the Alpide-Himalayan seismotectonic belt , 1986 .

[15]  Dan Wang,et al.  Predicting seismicity trend in southwest of China based on wavelet analysis , 2015, Int. J. Wavelets Multiresolution Inf. Process..

[16]  Lirong Yin,et al.  Spatiotemporal heterogeneity of urban air pollution in China based on spatial analysis , 2016, Rendiconti Lincei.

[17]  Xiaolu Li,et al.  Fractal dimension analysis for seismicity spatial and temporal distribution in the circum-Pacific seismic belt , 2019, Journal of Earth System Science.

[18]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[19]  Yan Y. Kagan,et al.  Statistical distributions of earthquake numbers: consequence of branching process , 2009, 0909.2372.

[20]  N. Lam,et al.  Applications of integrated geophysical method in archaeological surveys of the ancient Shu ruins , 2013 .

[21]  Benjamin Edwards,et al.  Development of a Response Spectral Ground‐Motion Prediction Equation (GMPE) for Seismic‐Hazard Analysis from Empirical Fourier Spectral and Duration Models , 2015 .

[22]  P. Bird An updated digital model of plate boundaries , 2003 .

[23]  Shui-Beih Yu,et al.  Spatial and Temporal Distribution of Slip for the 1999 Chi-Chi, Taiwan, Earthquake , 2004 .

[24]  Linda See,et al.  A spatial statistical analysis of the occurrence of earthquakes along the Red Sea floor spreading: clusters of seismicity , 2014, Arabian Journal of Geosciences.