微地震事件初至拾取SLPEA算法

Arrival picking of microseismic events using the SLPEA algorithm

微地震事件初至拾取是微地震数据处理的关键步骤之一.实际微地震监测资料中存在大量低信噪比事件,而传统方法对这些事件的应用效果并不理想.为了克服传统方法抗噪性弱的缺点,本文通过综合地震信号与环境噪声在振幅、偏振以及统计特征等方面的存在的差异,设计了一种针对低信噪比微地震事件的初至拾取方法——SLPEA算法.为了检验本文方法的可行性和有效性,分别对模型数据和实际资料进行了处理,并将处理结果与传统方法及手工拾取的结果进行了对比.分析表明,利用本文方法得到的初至到时与手工拾取结果的绝对误差平均值仅为1.33×10^(-3)s,小于3个采样点;方差为3.21×10^(-6)s^2;初至到时在手工拾取结果±0.005s误差范围内的个数占总数的95.8%.这些参数值均优于传统方法的同类参数,证明了本文方法的可靠性.

Arrival picking of microseismic events is a crucial step in microseismic data processing.There are a lot of microseismic events with low signal-to-noise ratio（SNR）in field datasets,and the arrival times of these events picked using conventional approaches are usually not satisfying.To overcome the weakness of low noise resistibility of the conventional approaches,a new method,called the SLPEA algorithm,is developed based on the differences in amplitudes,polarization characteristics and statistic properties between seismic signal and ambient noise.To examine its feasibility and effectiveness,the proposed method is applied to both synthetic and field datasets,and the results are compared with those from conventional approaches and handpicking.Analysis of these results demonstrates that the average of the absolute errors between the results of the proposed method and hand-picking are only 1.33×10^-3 s,which is less than 3samples,and the variance is 3.21×10^-6s^2.The percentage of the arrival times which is within±0.005 sof the hand-picking results is 95.8%.All these parameters are superior to those of the conventional approaches,which proves the reliability of the proposed method.