Improving the stability of microseismic event detection by clustering algorithm
A key step in microseismic monitoring is the efficient and accurate picking of the first break of the mi-croseismic data.Currently,the commonly used method to pick the first break is the energy ratio algorithm,which is simple and efficient in application.However,the main weakness of this algorithm is the poor results on low signal to-noise ratio data.In this paper,the algorithm is improved by applying the clustering algorithm.The principle of the improved method is to first pick the first break through the energy ratio algorithm,and opti-mize the results by clustering algorithm to divide the low error result with false pickings.Then,the false pickings are corrected according to the distribution fitted by the low-error result.Finally,the Akaike informa-tion criteria(AIC)algorithm is used in a small window that creates from optimized results to pick the first break accurately.This algorithm combines the benefits of the energy ratio algorithm and the AIC algorithm.Actual data test results show that the improved algorithm has higher pick-up accuracy in low SNR data compared to the conventional algorithm and can effectively identify the first break of multiple seismic phases.In addition,the al-gorithm is efficient and can be applied to field processing.
microseismic monitoringfirst break pickingenergy ratio algorithmAIC(Akaike Information Crite-ria)algorithmclustering analysis
龚屹、孟庆利、蓝加达、单中强、何培、翟仁磊
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中国石化华东油气分公司勘探开发研究院,江苏南京 210000
中国石化石油工程地球物理公司华东分公司,江苏南京,210000
微地震监测 初至拾取 能量比算法 AIC(Akaike Information Criteria) 聚类分析
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