Improving the stability of microseismic event detection by clustering algorithm
GONG Yi1, MENG Qingli1, LAN Jiada1, SHAN Zhongqiang1, HE Pei2, ZHAI Renlei2
1. Exploration and Development Research Institute, SINOPEC East China Oil & Gas Company, Nanjing, Jiangsu 210000, China; 2. Huadong Branch, SINOPEC Geophysical Corporation, Nanjing, Jiangsu 210000, China
摘要 高效且准确地拾取有效信号的初至是微地震监测技术的关键。目前常用的微地震初至拾取算法是能量比算法，该算法应用简单且拾取效率高。但是能量比算法存在的主要问题是算法的抗噪性较差，拾取误差较大。为此，将聚类算法应用于微地震信号初至拾取，改进现有拾取算法。首先通过能量比算法对微地震初至进行一次拾取；然后通过聚类算法对一次拾取结果进行优化，提取其中的小误差初至；再对提取出的小误差初至的分布进行拟合，根据分布规律校正误差较大的初至；最后以优化后的初至为中心开时窗并利用AIC（Akaike Information Criteria）算法对微地震信号进行精细拾取。该算法结合了能量比算法和AIC算法的优点。实际数据测试结果表明，与传统算法相比该算法具有较高的拾取精度和抗噪性，而且可以有效识别多震相初至。此外，该算法的运算效率很高，适用于现场实时处理。
Abstract：A key step in microseismic monitoring is the efficient and accurate picking of the first break of the microseismic 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 optimize 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 information 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 algorithm is efficient and can be applied to field processing.
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