Structures of the Xishancun landslide in Lixian County, Sichuan, inferred from seismic ambient noise autocorrelations
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摘要:
地震背景噪声自相关法不需要人工震源或天然地震即可获得地下速度界面的反射响应, 广泛应用于不同尺度的结构探测中.然而, 由于多数土质滑坡体的厚度介于数米至百米之间且结构复杂, 尽管其风化层和底部基岩界面上下的物性存在显著不同, 地震背景噪声自相关方法在探测滑坡体结构的应用研究并不常见.为此, 我们以四川理县西山村滑坡为例, 尝试利用水平分量地震背景噪声自相关方法探测该滑坡体结构.首先, 我们将连续噪声数据截取为长3600 s的时窗, 再对每段数据进行时域归一化和谱白化, 接着计算自相关函数并带通滤波到1~15 Hz, 最终提取了台站下方滑坡体与基岩分界面的一次及多次反射S波响应.结果表明, 西山村滑坡上22个台站的E分量自相关函数提取到的界面反射S波双程走时介于0.14~0.25 s, 获得的滑坡体厚度为57~102 m.因为西山村滑坡体较薄, P波在自由地表和地下界面之间的双程走时小于0.1 s, 导致P波反射响应无法从自相关函数的本征值中分辨出来.本研究对西山村滑坡部分台站的沉积层厚度进行了补充, 结果表明西山村滑坡第四系沉积物呈北厚南薄的特征, 且综合电法剖面与接收函数结果, 滑坡内部100 m深度范围内有大面积低电阻率特征、高泊松比的含水层, 抗滑能力差, 若有降雨与地震的影响, 北部的碎石堆积物容易脱落, 对南部村落聚集地造成较大损失.
Abstract:The seismic ambient noise autocorrelation method is widely used to detect interface structures at different scales because it can obtain shallow underground reflection responses without artificial sources or natural earthquakes. However, the thickness of most soil landslides ranges from a few meters to a hundred meters with complex internal structures, despite significant difference in physical properties between the weathered layer and the bottom bedrock, the application of ambient noise autocorrelation is less common in detecting landslide structures. Therefore, we take the Xishancun landslide in Lixian County, Sichuan Province as an example, and try to apply the autocorrelation method to horizontal component seismic ambient noise to detect the landslide structures. Firstly, we cut the continuous noise data into segments with a time window of 3600 s, and adopt time domain normalization and spectral whitening to each segment. We then calculate autocorrelation functions and bandpass filter them to the frequency range of 1~15 Hz. Next, we extract the first and multiple S-wave reflection responses of the interface between the landslide and the bedrock directly beneath each station. The results show that the two-way travel time of the interface reflected S-wave extracted from the autocorrelation function of the E component of 22 stations in the Xishancun landslide is between 0.14~0.25 s. The thickness of the Xishancun landslide is 57~102 m. Because the landslide is relatively thin, the two-way P-wave travel times between the free surface and underground interface are less than 0.1 s, which makes it impossible to distinguish P-wave reflection responses from eigenvalues of the autocorrelation functions. This study provides more thickness constraints on the sediment layer at some monitoring stations in the Xishancun landslide. The results of this article indicate that the the Quaternary deposits of Xishancun landslide are thick in the north and thin in the south. Based on the result of electrical profile and receiver function, there is a large area of low resistivity and high Poisson's ratio aquifer within a depth range of 100 m inside the landslide, which has poor anti-slide ability. If there are heavy rainfalls or earthquakes, the gravel deposits in the north are prone to detachment, which will cause significant losses to the gathering areas of the southern villages.
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图 1
理县西山村滑坡台站分布、S13台站高频近震接收函数和地震背景噪声HVSR结果
Figure 1.
Seismic stations in the Xishancun landslide, high-frequency receiver function from local earthquakes and HVSR results for seismic station S13
图 3
噪声源、一维二层速度模型、合成噪声数据及对应的自相关函数
Figure 3.
Distribution of seismic noise sources, one-dimensional two-layer velocity model, synthetic noise data and corresponding autocorrelation function
图 4
二层(a)和三层(b)一维速度模型及对应的E分量噪声自相关函数
Figure 4.
One-dimensional velocity models and corresponding autocorrelation functions of the E-component seismic noises for two-layer (a) and three-layer (b) velocity models
图 5
频谱白噪化及不同滤波频段对噪声自相关函数的影响
Figure 5.
Influence of spectral whitening and different filtering frequency bands
图 6
(a) 不同数据长度下D13台站E分量噪声自相关函数的叠加结果及其信噪比；红色实线为计算信噪比选的随机噪声.(b)D13台站Z分量自相关函数
Figure 6.
(a) Stacked autocorrelation functions and their signal-to-noise ratio of the E-component seismic noises with different durations for station D13. The red curve marks the random noises selected for calculating the signal-to- noise ratio. (b) The Z-component autocorrelation function for station D13
图 7
E分量与L分量的噪声自相关函数
Figure 7.
Autocorrelation functions for the E and L component noises
图 8
22个台站E分量噪声自相关函数. 黑色圆点为一次反射S波双程走时，灰色圆点为多次反射S波双程走时
Figure 8.
Autocorrelation functions of E-component noises for 22 stations. Black dots represent two-way travel times of reflected S waves while gray dots are two-way travel times for multiple reflected S waves
图 9
台站D15 (a)、D16 (b)、S13 (c)、S30(d)得到的地震背景噪声互相关函数(黑色)与理论自相关函数(灰色)对比，其中理论自相关函数由HVSR反演得到的一维速度模型(灰色)计算得到
Figure 9.
Comparison of seismic ambient noise autocorrelation functions from observed data (black) and synthetics for stations D15 (a), D16 (b), S13 (c), and S30 (d). The synthetic autocorrelation functions are calculated using one-dimensional velocity models (gray) obtained from the HVSR method
图 10
地震噪声自相关(蓝色方框)、HVSR(黑色方框)、近震高频P波接收函数(灰色方框)以及钻孔(红色圆点)测得的滑坡体厚度对比，竖直黑色线段是噪声自相关估算的滑坡体厚度误差棒
Figure 10.
Comparison of landslide thicknesses obtained from seismic noise autocorrelation (blue squares), HVSR (black squares), high frequency P-wave receiver function of local earthquakes (gray squares) and borehole measurements (red dots), respectively. Vertical black solid lines represent error bars estimated from seismic noise autocorrelations
表 1
测井得到的西山村滑坡垂直于地面与滑坡面的沉积层厚度
Table 1.
Sedimentary layer thickness of Xishancun landslide perpendicular to the ground and landslide surface obtained from logging
测井 经度(°E) 纬度(°N) 沉积层厚度(Z方向) (m) 沉积层厚度(Q方向) (m) BZK2 103.42408 31.57163 37.9 34.3 BZK3 103.42072 31.57483 64.3 62.3 BZK4 103.42433 31.57463 57.4 52 BZK5 103.42647 31.57513 ≥69 ≥61.5 BZK6 103.42361 31.57727 62.9 54.5 BZK7 103.42627 31.58219 45.5 39.4 BZK8 103.42459 31.58499 46.5 42.8 BZK9 103.42886 31.58316 50.1 43.4 BZK10 103.43011 31.58202 64.7 58.2 BZK11 103.42886 31.58741 >84.9 >73.5 BZK12 103.43089 31.59124 >62.4 >54 BZK13 103.43194 31.59269 77.1 66.7 BZK14 103.42994 31.58924 ≥60.6 ≥52.5 BZK15 103.42916 31.57844 50.5 43.7 BZK16 103.42227 31.58213 54.4 47.11 BZK17 103.42397 31.56988 23.7 20.5 
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