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本文从数理统计入手,首先计算了倾斜仪资料均值序列分布的偏态系数和两种时间序列的振幅谱及均方差σ,并将均值序列的振幅谱与一阶差分序列的振幅谱作了比较,而后者的年振幅及其均方差σ比前者要削减90％以上,表明差分序列宜作为统计分析的基本量。 文中选定十二个区间,对16个台站的倾斜日差(时间窗为一年)发生频数进行了统计分析,并在此基础上,按:p(d≤r)=95％(d为日差绝对值),求得十六个台站三十二台倾斜仪的正常倾斜复现域r值。该值为寻找定点形变观测仪器的背景值提供了依据。在此基础上得出三十二台倾斜仪的辨别异常限M的初步值,作为初步区分正常倾斜和异常倾斜的依据。经震例检验,我国Ⅰ、Ⅱ类倾斜仪台站己具有辨别10~(-7)异常倾斜信息的能力,而泰安(东西分量)等优质台站则达10~(-8)。 本文所列各项定量技术指标,可供研究观测精度和台址条件时参考;本法也适用于其它定点形变观测仪器。
Starting with the mathematical statistics, this paper first calculates the skewness coefficient of the mean value sequence distribution of the inclinometer data and the amplitude spectrum and the mean square error σ of the two time series, and compares the amplitude spectrum of the mean sequence with the amplitude spectrum of the first-order differential sequence , While the latter’s annual amplitude and its mean square deviation σ are more than 90% lower than the former, indicating that the differential sequence should be used as the basic statistical analysis. Twelve intervals are selected in this paper, and statistical analysis is made on the frequency of tilting of 16 stations (time window is one year). On the basis of this, For the absolute value of the day difference), obtained r value of the normal tilt recurrence domain of thirty-two inclinometers at sixteen stations. This value provides the basis for finding the background value of the fixed point deformation observation instrument. Based on this, thirty-two inclinometers are used to identify the initial value of the abnormality limit M, which serves as the basis for the initial distinction between normal tilt and anomalous tilt. According to the test of earthquake cases, the tilt stations of China Ⅰ and Ⅱ have the capability of discriminating the 10 ~ (-7) anomalous tilt information, while the good stations of Tai’an (east-west component) reach 10 ~ (-8). The quantitative technical indicators listed in this article can be used for reference when studying observation accuracy and site conditions. This Law also applies to other fixed-point deformation observation instruments.