利用HVSR方法探测四川理县西山村滑坡速度结构

HVSR(Horizontal-to-Vertical Spectral Ratio)是地震背景噪声水平分量与垂直分量的频谱比, 常被用来探测起伏地形较小的浅地表速度结构.然而, 高起伏地形下该方法的适用性还需要仔细研究.本文探讨利用HVSR方法探测四川理县西山村滑坡的速度结构.我们首先将ENZ坐标系下三分量地震数据旋转到垂直于地形自由面的LQT坐标系, 分别计算了两个坐标系下2 h地震背景噪声数据的HVSR曲线, 对比结果发现, LQT坐标系下三分量数据的HVSR峰值频率更能反映地下结构信息.然后, 本文反演了29个台站LQT坐标系的HVSR曲线, 获得了滑坡体堆积物及基岩的一维S波速度结构.西山村滑坡体堆积物的S波速度范围为50~1000 m·s^-1, 滑坡厚度23~130 m.滑坡体西侧边缘地下40 ~120 m处存在10~20 m的低速层, 表明该位置抗剪强度相对较弱, 是潜在的滑动危险区域.对数据进行坐标系转换后, HVSR方法能够利用较短的观测数据获取可靠的滑坡体速度结构, 为滑坡体积估算、脆弱位置识别、灾害评估提供重要信息.

Velocity structure of the Xishancun landslide in Li County,Sichuan Province,explored by HVSR method

The HVSR (Horizontal to Vertical Spectral Ratio) is the Fourier spectral ratio of the horizontal and vertical components of ambient seismic noises, which is often used to study near surface velocity structures for areas with small topography. However, it is necessary to evaluate this method in large topographic area. In this study, we will apply the HVSR method to obtain velocity structures of the Xishancun Landslide in Sichuan Province. We first transform three-component seismic waveforms in the ENZ coordinate system to the LQT coordinates, in which the L component is perpendicular to the landslide surface. We then calculate HVSR curves with 2-hour long seismic noises under these two coordinate systems. Comparison of the two HVSR results suggests that the peak of HVSR curves in the LQT coordinate system bears better information on underground structures. We then invert the HVSR curves to obtain one-dimensional shear wave velocity structures of the landslide and bedrock beneath 29 stations. The shear wave velocity of the landslide ranges from 50 m·s-1 to 1000 m·s-1 and the thickness ranges from 23 m to 130 m. Our results show a lower shear velocity layer with a thickness of 10~20 m lies at a depth of 40~120 m for stations on the west side, suggesting a weak shear strength and potential sliding dangerous zone. After coordinate transformation to the observed data, the HVSR method can obtain reliable velocity structures by using short-term observational data, which provide important information for landslide volume estimation, weak zone identification, and hazard assessment.