Extending the Frequency Band of Surface‐Wave Dispersion Curves by Combining Ambient Noise and Earthquake Data and Self‐Adaptive Normalization
Extending the Frequency Band of Surface‐Wave Dispersion Curves by Combining Ambient Noise and Earthquake Data and Self‐Adaptive Normalization
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DOI：
10.1029/2022jb026040
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发表时间：
2023-05
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影响因子：
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通讯作者：
Jie Zhou;Zhengbo Li;Xiaofei Chen
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文献类型：
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作者：
Jie Zhou;Zhengbo Li;Xiaofei Chen
面波频散曲线在地下结构反演中有着广泛的应用。然而，在数据有限的情况下，反演中的非唯一性问题是不可避免的。在频率上扩展色散曲线有助于提供更好的约束。从环境噪声和地震数据中提取的色散谱不重叠，可以组合在一起提供互补信息。此外，由于在能量高度不均匀的色散谱中的同一频率上可能存在多个模，能量较低的色散曲线被较高能量的色散曲线隐藏，因此提取的色散谱往往不能通过简单的归一化来充分利用。我们提出了一种自适应归一化技术来发现隐藏的信息，并在频率上扩展色散曲线。对USArray可移动阵列记录的环境噪声和地震数据进行简单的自适应归一化处理，并进行马尔可夫链蒙特卡罗贝叶斯反演来估计速度结构。反演结果揭示了扩展频散曲线的重要性，环境噪声和地震联合数据集以及自适应归一化技术的显著改善。
Dispersion curves of surface waves are widely used in subsurface structural inversions. However, the non‐uniqueness problem in inversion is inevitable with limited data. Extending the dispersion curves in frequency is helpful for providing better constraints. The dispersion spectra extracted from ambient noise and earthquake data do not overlap and can be combined to provide complementary information. Besides, because multiple modes can be present at the same frequencies in the dispersion spectrum with highly non‐uniform energies, dispersion curves with lower energies are hidden by those with higher energies, thus the extracted dispersion spectrum is often not fully exploited by simple normalization. We propose to use a self‐adaptive normalization technique to uncover the hidden information and extend the dispersion curves in frequency. Both ambient noise and earthquake data recorded by the USArray Transportable Array are processed by simple and self‐adaptive normalizations, and a Markov chain Monte Carlo Bayesian inversion is conducted to estimate the velocity structure. The inversion results reveal the importance of extending dispersion curves and a significant improvement by the combined ambient noise and earthquake datasets and the self‐adaptive normalization technique.