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A first arrival picking method of microseismic data based on single time window with window length independent

机译:基于单时间窗口与窗口长度独立的单时间窗口的第一到达拣选方法

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摘要

First arrival picking is a key factor which affects the precision of microseismic data analysis. Here, we propose a new method, which employs the maximum eigenvalue to constraint the Maeda-Akaike Information Criterion (Maeda-AIC) algorithm. First, aims at addressing the pick result affected by signal-to-noise ratio (SNR) of microseismic data, maximum eigenvalue method based on polarization analysis is applied, and the maximum eigenvalue is calculated firstly, as for three component (3C) microseismic data, the maximum eigenvalue is calculated with corresponding covariance matrix, a time window need to be set in the process of building the covariance matrix, and it is the only time window set in the method proposed in this paper, so the method is called single window Maeda-AIC (SWM-AIC), to the single component (1C) microseismic data, the variance of the data is taken as the maximum eigenvalue. Then, to reduce the effect of time window and increase the automation of the algorithm, Maeda-AIC method which is a non-window-based first arrival picking method is applied. Maeda-AIC values in preliminary window are calculated, and the preliminary window is the sequence before the largest eigenvalue of the 3C or 1C data. We validate the developed method with both synthetic and field microseismic data, using a range of signal-to-noise ratios. The developed method is compared with some basic methods, specifically STA/LTA, Maeda-AIC, and the maximum eigenvalue method. The results demonstrate that the new method is much better at identifying first arrival times than basic methods when the data have a low signal-to-noise ratio, and is even faster than the STA/LTA method with 1C data. In contrast to other improved methods, threshold value is not required for this method, and the only time window used in this method is just for maximum eigenvalue calculation, through test in the paper, its length has almost no effect on the first arrival picking.
机译:首先到达挑选是影响微震数据分析精度的关键因素。在这里,我们提出了一种新方法,该方法采用了最大的特征值来限制Maeda-akaike信息标准(Maeda-AIC)算法。首先,旨在解决受微震数据的信噪比(SNR)影响的挑选结果,施加了基于极化分析的基于极化分析的最大特征值方法,并且首先计算最大特征值,如三个组分(3C)微震数据计算,使用相应的协方差矩阵计算最大特征值,需要在构建协方差矩阵的过程中设置时间窗口,并且是本文提出的方法中唯一的时间窗口,因此该方法称为单个窗口Maeda-AIC(SWM-AIC),到单个组分(1C)微震数据,数据的方差被视为最大特征值。然后,为了减少时间窗口的效果并增加算法的自动化,应用是基于非窗口的第一到达拣选方法的Maeda-AIC方法。计算初步窗口中的Maeda-AIC值,并且初步窗口是3C或1C数据的最大特征值之前的序列。我们使用一系列信噪比来验证合成和现场微震数据的开发方法。将开发方法与一些基本方法进行比较,特别是STA / LTA,Maeda-AIC和最大特征值方法。结果表明,当数据具有低信噪比时,新方法在识别第一到达时间比基本方法更好,并且甚至比具有1C数据的STA / LTA方法更快。与其他改进的方法相比,此方法不需要阈值,并且该方法中使用的唯一时间窗口仅用于最大的特征值计算,通过对纸张的测试,其长度几乎没有对第一个到达拣选的影响。

著录项

  • 来源
    《Journal of seismology》 |2018年第6期|共15页
  • 作者单位

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China;

    Chengdu Univ Technol State Key Lab Geohazard Prevent &

    Geoenvironm Pro Chengdu 610059 Sichuan Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地震学;
  • 关键词

    Microseismic; Automatic picking; Maeda-AIC; Polarization; Eigenvalue;

    机译:微震;自动采摘;Maeda-AIC;极化;特征值;

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