A HVSR inversion method based on the multi-modal forest optimization algorithm

Abstract The exploration of urban underground spaces is of great significance to urban planning, geological disaster prevention, resource exploration, and environmental monitoring. However, due to severe interferences, conventional seismic methods cannot adapt to the complex urban environment well. Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal, the microtremor horizontal to vertical spectral ratio (HVSR) method can effectively avoid the strong interference problems caused by the complex urban environment, which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve. Nevertheless, HVSR curve inversion is a multi-parameter curve fitting process. Conventional inversion methods can easily converge to the local minimum, directly affecting the inversion results’ reliability. Thus, we propose a HVSR inversion method based on the multimodal forest optimization algorithm, which uses the efficient clustering technique and can locate the global optimum quickly. Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model. Both the adaption and stability to the abnormal layer velocity model are also demonstrated. The results of the real field data are also verified by the drilling information.