Abstract
Based on the complete seismic data of the Tianshan area in the past 118 years, the co-occurrence rate of earthquakes in different fault zones is calculated, the fault zone with the highest associative degree in the Tianshan area is determined as the research object, extracted the earthquake time series information of the strong associative fault zone and the commensurability method is used to obtain the commensurability expression, the year of strongest earthquake signal is calculated and predicted, then the butterfly structure diagram and commensurability structure system are drawn. Combined the butterfly structure diagram and commensurability structure system to analyze the seismic spatiotemporal structure characteristics of the Tianshan area, judge the trend of future earthquakes, and predict the time of future earthquakes. The results demonstrate that Ms≥5.6 earthquake signals are strong in 2020 and 2021. In order to determine the location of future earthquakes, the historical epicenter migration law of the strongly correlated fault zone in the Tianshan area is analyzed. It is revealed that the epicenter distribution is symmetric in both the longitude and latitude directions. Analyzing the spatial distribution of epicenters, the five successive earthquakes in the strong associative fault zone in the Tianshan area present a pentagonal symmetrical structure in space. It is judged that the next earthquake will migrate toward the northeast. Additionally, the sunspot has a strong seismic correlation with the strong associative fault zone in the Tianshan area. Specifically, on the 11-year cycle scale of sunspot activity, 64.71% of earthquakes occurred in the fluctuation descending range; on the monthly scale of sunspot activity. Hence, it has been verified that the proposed disaster prediction method based on commensurability theory is scientific and has a broad application prospect.
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This work was supported by the National Natural Science Fund of China (grant number 41877519)
Wan Jia is a Ph.D. student at the School of Geography and Tourism, Shaanxi Normal University, Xi’an. She obtained a master’s degree from Shaanxi Normal University in 2013. Her research interests are regional sustainable development and disaster prevention.
Yan Jun-Ping is a professor and doctoral supervisor working at the School of Geography and Tourism, Shaanxi Normal University, Xi’an. He obtained his doctor’s degree from Xi’an University of Technology in 2003. He has led a number of projects funded by the National Natural Science Foundation of China. At present, his research interests are global change and natural disaster prevention.
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Wan, J., Yan, JP., Liu, ZQ. et al. Spatiotemporal characteristics and trend assessment of Ms ≥ 5.6 earthquakes in the Tianshan area of China based on co-occurrence analysis and commensurability. Appl. Geophys. 18, 396–407 (2021). https://doi.org/10.1007/s11770-021-0847-9
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DOI: https://doi.org/10.1007/s11770-021-0847-9