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Automatic pickings in earthquake real-time monitoring systems often contain noise bursts and/or phases of different event(s)
occurring almost simultaneously. Typically, a locator uses these picks as P and S waves arrival times coming from a single
event and, therefore, should be complemented by a distinctive phase association logic. The method we propose manages to automatically
associate data related to different events and eliminates the influence of spoiled data from single events. The method is
based on “network beamforming”, a robust and stable algorithm, which utilizes a hypocenter grid search for the stack maximum
of a set of complex exponents applied to the P phase readings. The algorithm separates the residual outliers and then uses
them for location. If successful, a hypocenter is established for the interfering event. The solutions obtained are overall
robust and independent from the estimate of origin times. The preliminary epicenter for the grid search is provided by the
intersection of perpendicular bisectors in the modified “arrival order algorithm” or by the modified “Tnow” algorithm, which uses non-arrival information. We applied this method to automatic first arrival phase readings of 915 events
registered by the Hi-net Japan seismic network and our results are statistically promising. Here, we present two interesting
and complicated examples. 相似文献
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快速准确地从微震监测数据中提取微地震事件是微地震监测技术的关键。采用理论模拟数据分析了STA/LTA方法的可行性,选择了更能反映微地震信号变化的特征函数代替原始信号,结合实际数据对时窗长度、长短时窗比、阈值等重要参数进行了对比分析。研究结果表明,STA/LTA方法能够从海量微地震监测数据中快速准确地自动识别微地震有效信号,去除冗余信息,大幅减少微地震监测数据的传输量,从而为微地震监测数据的无线实时传输提供了可能,同时也减少了数据存储所需要的磁盘空间,取得了较好的应用效果。 相似文献
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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. 相似文献
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