Multimode dispersion measurement of surface waves extracted by multicomponent ambient noise cross-correlation functions
Multimode dispersion measurement of surface waves extracted by multicomponent ambient noise cross-correlation functions
复制标题
通过多分量环境噪声互相关函数提取的表面波的多模色散测量
DOI：
10.1093/gji/ggac225
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发表时间：
2022
影响因子：
2.8
通讯作者：
Nishida Kiwamu
中科院分区：
文献类型：
--
作者：
Takagi Ryota;Nishida Kiwamu
背景地震噪声的互相关函数有时表现出面波的多模态特征，特别是在沉积区和海洋区的观测中。多模色散曲线对于提高地下成像的深度分辨率是有用的，然而，多模色散曲线的测量是不容易的。面波的多模干扰使得即使地下结构没有横向非均匀性，互相关函数也变得复杂，并且复杂的波形可能导致非物理色散测量。我们开发了一种方法来确定多模相速度频散曲线的基础上的拟合的合成互谱观测。合成互谱中的相速度被建模为一维速度结构的函数，从而实现物理上可实现的频散曲线的测量。1-D结构不一定直接代表地球结构，但作为站对内的频散曲线的模型参数。互谱拟合分为两步，即基于阵列的拟合和单对拟合。第一步从阵列内的互谱数据估计每个表面波模式和参考1-D结构的振幅。第二步利用第一步估计的模态振幅和参考结构，从单个站对的互谱估计依赖于对的频散曲线。基于互谱拟合的色散测量即使在时域互相关函数中多模干扰显著的短距离下也可以工作。我们将这种方法应用于海底观测的合成和现场数据。综合和现场应用表明，同时使用多分量互相关函数确定多模频散曲线是有效的。在互相关函数信噪比不高的情况下，仍能稳定地估计海洋区域的多模相速度频散曲线。由本方法估计的对相关的多模色散曲线可以作为高分辨率表面波层析成像的鲁棒输入数据。
Cross-correlation functions of ambient seismic noise sometimes show multimode characteristics of surface waves, especially in observations in sedimentary areas and ocean areas. Multimode dispersion curves are useful for improving the depth resolution of subsurface imaging; nevertheless, measuring the multimode dispersion curves is not easy. Multimode interference of surface waves makes the cross-correlation functions complicated even without lateral heterogeneity of the subsurface structure, and the complex waveforms may result in unphysical dispersion measurement. We developed a method to determine multimode phase velocity dispersion curves based on the fitting of the synthetic cross-spectra to observed ones. The phase velocity in the synthetic cross-spectra is modelled as the function of a 1-D velocity structure, which achieves the measurement of physically realizable dispersion curves. The 1-D structures do not necessarily represent the Earth structure directly but act as model parameters of the dispersion curves within station pairs. The cross-spectral fitting has two steps, that is, array-based and single-pair fittings. The first step estimates the amplitude of each surface wave mode and the reference 1-D structure from the cross-spectral data within an array. The second step estimates the pair-dependent dispersion curves from the cross-spectra of a single station pair using the modal amplitudes and the reference structure estimated by the first step. The dispersion measurement based on the cross-spectral fitting can work even at short distances where the multimode inference is significant in the time-domain cross-correlation functions. We applied this method to synthetic and field data in seafloor observations. The synthetic and field applications show that the simultaneous use of multicomponent cross-correlation functions is effective to determine multimode dispersion curves. The multimode phase velocity dispersion curves in the ocean area are estimated stably even though the signal-to-noise ratio of cross-correlation functions is not high. The pair-dependent multimode dispersion curves estimated by the present method can serve as robust input data for high-resolution surface wave tomography.