Inversion method of NHV based on novel model parameter space acquisition strategy and its application in the Tonghai basin site in Yunnan, China.

The imaging of subsurface soil velocity structures from ambient noise inversion is a difficult problem. Few recording points and a simplified 1-D layered profile lead to important non-uniqueness. From our point of view, improving the reliability of processing methods of the observed data to obtain noise horizontal-to-vertical spectral ratio (NHV) curves and setting a complete model parameter space are important tasks to reduce the non-uniqueness of inversion. In this study, using a local site near the border of the Tonghai Basin, China, as a case study, we first demonstrate how to identify and mitigate the influence of industrial sources using surface observations to obtain more reliable NHV curves. Then, a new strategy to determine model parameter space is proposed, that is, stratifying soil layers based on the number of NHV peaks and determining the shear wave velocities, thicknesses, and their ranges based on the empirical relationship between sedimentary thickness and resonant frequency ( h - f _r ). Subsequently, combining the model parameter space acquisition strategy with the NHV inversion, a novel NHV inversion approach is developed and applied to obtain the 2-D V _S profile of the investigated Tonghai site. The inverted 2-D V _S profile aligns favorably with the frequency-depth conversion results of the measured NHV curves (NHV-profiling) and the measured borehole profiles, affirming the reliability of the proposed NHV inversion method. Finally, by comparing the empirical transfer functions from the strong-motion recordings, we validated the applicability of the inverted models for characterizing site effects. The model parameter space acquisition strategy proposed in this paper and the analysis procedure of the observed data are also applicable to other study areas, which can provide a referable approach to quickly and effectively acquire the soil layer velocity structure of the site.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2024 The Authors.)