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为探索气枪震源在探测区域性地壳结构中的应用, 2015年10月10~20日在长江马鞍山-安庆段开展气枪流动激发实验(安徽实验),在20个固定点定点激发2973炮,中间流动激发1872炮,由周边109个固定台、700个流动台(包括11条测线)进行接收。为获取郯庐断裂带南端较为精细的地壳速度结构,本文利用安徽气枪实验中采集到的固定台数据进行初至P波震相拾取,对20个气枪源、52个台站、335个震相进行体波层析成像,验证了利用气枪震源进行体波层析成像的可行性,并得到了P波速度结构的一些初步结果:①利用大容量气枪震源可进行三维体波层析成像;②15km深度的成像结果显示出大区域高低速异常区的清晰轮廓,即从研究区中心向外整体呈现出低-超低-低-高的分布特征,与地质构造背景相关,具有显著的横向不均匀性;③秦岭-大别造山带显示出高速异常,与其深部超高压变质岩相对应,而长江中下游地区整体呈现低速异常,与其特殊的成矿背景相对应。  相似文献   
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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   
3.
A long-standing problem in operational seismology is that of reliable focal depth estimation. Standard analyst practice is to pick and identify a ‘phase’ in the P-coda. This picking will always produce a depth estimate but without any validation it cannot be trusted. In this article we ‘hunt’ for standard depth phases like pP, sP and/or PmP but unlike the analyst we use Bayes statistics for classifying the probability that polarization characteristics of pickings belong to one of the mentioned depth phases given preliminary epicenter information. In this regard we describe a general-purpose PC implementation of the Bayesian methodology that can deal with complex nonlinear models in a flexible way. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical-programming environment. An analytic signal representation is used with the imaginary part being the Hilbert transform of the signal itself. The pickings are in terms of a plot of posterior probabilities as a function of time for pP, Sp or PmP being within the presumed azimuth and incident angle sectors for given preliminary epicenter locations. We have tested this novel focal depth estimation procedure on explosion and earthquake recordings from Cossack Ranger II stations in Karelia, NW Russia, and with encouraging results. For example, pickings deviating more than 5° off ‘true’ azimuth are rejected while Pn-incident angle estimate exhibit considerable scatter. A comprehensive test of our approach is not quite easy as recordings from so-called Ground Truth events are elusive.  相似文献   
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流动地震台阵观测初至震相的自动检测   总被引:3,自引:0,他引:3  
震相到时的自动精确检测对实现海量波形数据自动处理有重要意义.针对流动地震台阵观测,本文综合利用单台Akaike信息准则(AIC)和多台最小二乘互相关方法,发展了震相自动精确检测技术.检测结果表明,在长短时平均比值方法(STA/LTA)检测地震事件的基础上,利用单台AIC方法,近震初至震相检测精度小于0.3 s;利用多台最小二乘互相关方法,能够可靠地检测高信噪比地震的初至震相到时,当信噪比较低时,能够有效地避免初至震相的错误判别.  相似文献   
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Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the proporty of dense seismic array observations. In our method, the Akaike's information criterion (AIC) for the single channel observation and the least-squares cross-correlation for the multi-channel observation are combined together. The tests by the seismic array observation data after triggering with the short-term average/long-term average (STA/LTA) technique show that the phase picking error is less than 0.3 s for local events by using the single channel AIC algorithm. In terms of multi-channel least-squares cross-correlation technique, the clear teleseismic P onset can be detected reliably. Even for the teleseismic records with high noise level, our algorithm is also able to effectually avoid manual misdetections.  相似文献   
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