A clustering-based repeating earthquakes identification method and its application
Events with highly similar waveforms and rupturing of the same fault patch are interpreted as re-peating earthquakes,which can be applied in detecting deep fault deformation,characterizing fault behavior,and assess seismic hazards.In this study,we develop a clustering-based repeating earthquakes identification method by using the hierarchical clustering algorithm in machine learning.First,the parallel waveform cross-correlation meth-od is adopted to calculate the cross-correlation coefficient(CC).Then,the S-P differential time is used to measure the inter distance of events.Finally,the hierarchical clustering is applied to obtain repeating clusters.We utilize this method to investigate the seismicity around the Ganzi-Yushu fault(GYF)and the eastern Kunlun fault(EKLF).We identify 6 repeating clusters along the GYF,with an average fault slip rate of 7.4 mm/a.Around the EKLF,we identify 3 repeating clusters,with an average fault slip rate of 6.9 mm/a.Along the EKLF,the fault slip rates gradu-ally decreases from the west to the east along the strike,indicating a complex dynamic process.Our results agree with geology observation and GPS data.Based on real data testing,our results show that the method to identify re-peating earthquakes is automatic,efficient and convenient and provides basic information for accurate identifica-tion of repeating earthquakes and places constraints on fault activity.
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