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A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

机译:基于手势传感器和极化分析的三组分VSP数据新的初次采摘

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摘要

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data.
机译:提出了一种新的针对三分量(3C)垂直地震剖面(VSP)数据的初次拾取算法,以提高初次到达的估计精度,该方法采用手势检测校准和基于协方差矩阵特征值的极化分析。这项研究旨在解决使用检波器的方位角和倾角对VSP数据进行校准的问题,这是因为在钻孔中使用检波器的方向是随机的,这将进一步导致首次中断拾取可能不可靠。最初,将手势测量模块集成到地震仪中,以快速获取高精度手势数据(包括方位角和倾角信息)。通过使用较早的手势数据进行重新旋转和重新投影,每个组件的地震数据集都将被校准到与振动子发射方向一致的方向。当将每个分量波形校准为相同的虚拟参考分量时,它将提高原始数据的可靠性,并且相应的第一次中断也将得到适当调整。在实现3C数据校准后,采用基于自回归-Akaike信息准则(AR-AIC)的自动第一次断裂挑选算法来评估第一次断裂。此外,为了提高第一次中断拾取的准确性,使用3C VSP记录的极化属性来约束AR-AIC拾取器的扫描段,该过程使用协方差矩阵的最大特征值计算。使用现场数据进行的预校准和后校准之间的对比结果表明,它可以进一步提高3C VSP波形的质量,这有利于后续拾取。与获得的短期平均值至长期平均值(STA / LTA)和AR-AIC算法相比,该方法与极化分析相结合,可以显着降低拾取错误。实际现场实验的应用也已经证实,所提出的方法可能更适合于3C VSP的首次中断选择。使用具有低SNR的合成3C地震数据进行的测试表明,选择第一个中断时的误差在0.75毫秒至1.5毫秒之间。因此,所提出的方法可以减少3C VSP数据的拾取错误。

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