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The Green's functions constructed from 17 years of ambient seismic noise recorded at ten stations of the German Regional Seismic Network

机译:Green的功能是根据在德国区域地震台网十个站点记录的17年的环境地震噪声构建的

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摘要

The characteristics of the signal-to-noise ratio (S/N) of Green's functions constructed from continuous data of ambient seismic noise stacked over a 17-yr time series are shown. We used data recorded by 10 stations of the German Regional Seismic Network (GRSN). For 45 different interstation distances between 80 and 500 km, the root mean square (rms) of ambient noise in different frequency bands in the range from 0.015 to 2 Hz is used to observe seasonal as well as daily variations of noise amplitude. It is shown that rms values depend on the station location and the filter frequency. We construct the Green's functions from noise using a window selection method (WSM) described in this paper. All data processing steps in this method are linear, avoiding nonlinear one-bit normalization. We show that the S/N of the Green's function increases proportionally to the square root of stacking time if noise is measured at the end of the correlogram. On the other hand, S/Ns are not improved as a result of stacking the data for long time series for noise defined at the beginning of the correlogram. Therefore, stacking the Green's function for long time series does not improve the S/N near the first arrival of the retrieved Green's function. We interpret this observation to be the result of an inhomogeneous distribution of noise sources. The correlation functions do not perfectly converge to the Green's functions even for stacking long time series such as a 17-yr time series.
机译:显示了格林函数的信噪比(S / N)的特性,该函数是通过在17年的时间序列上堆叠的环境地震噪声的连续数据构建的。我们使用了德国区域地震台网(GRSN)的10个台站记录的数据。对于80至500 km之间的45个不同的站间距离,在0.015至2 Hz范围内的不同频带中,环境噪声的均方根(rms)用于观察噪声幅度的季节性和每日变化。结果表明,均方根值取决于测站位置和滤波器频率。我们使用本文描述的窗口选择方法(WSM)从噪声构造Green函数。此方法中的所有数据处理步骤都是线性的,避免了非线性一位标准化。我们显示,如果在相关图的末尾测量噪声,则格林函数的S / N与堆积时间的平方根成比例地增加。另一方面,由于在相关图的开头定义了长时间序列的噪声数据,因此无法改善S / N。因此,将Green函数进行长时间序列的堆叠并不会提高检索到的Green函数的首次到达附近的信噪比。我们将此观察解释为噪声源分布不均匀的结果。即使堆叠诸如17年时间序列之类的较长时间序列,相关函数也无法完美地收敛于Green函数。

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