2025 Volume 78 Pages sp16-sp32
We developed a method that spatially interpolates one-dimensional (1D) S-wave velocity (Vs) profiles to a 3D Vs model based on deep neural network to predict regional shallow three-dimensional (3D) subsurface models. The method uses dispersion curves obtained by active and passive surface wave measurements, and horizontal-to-vertical spectral ratio (H/V) obtained by single station three-component microtremor measurements. Since the number of sites with dispersion curves is smaller than those with H/V measurements, the deep neural network consists of two stages. The first stage (A) predicts Vs profiles from H/V using training data of Vs profiles obtained from dispersion curves, and the second stage (B) predicts Vs profiles from surface topography and geomorphological classification etc. using training data of Vs profiles obtained from the first stage. Estimation procedure for a 3D regional shallow Vs model can be summarized as four steps and two stage deep learnings as follows. At the first step, we estimated 1D Vs profiles by the inversion of dispersion curves at sites where both dispersion curve and H/V were observed. At the second step, the first stage deep learning (A) predicts 1D Vs profiles from H/V spectra based on the first stage network trained by H/V spectrum-1D velocity profiles together with other regional information including coordinate, surface elevation, geomorphology, and bedrock depths in community velocity model. The first stage (A) training predicted 1D Vs profiles from H/V spectra measured at sites without surface wave methods. At the third step, we used 1D Vs profiles predicted in the second step as initial profiles, and applied non-linear inversion using H/V to finalize 1D Vs profiles. At the last step, the second stage deep learning (B) predicts 1D Vs profiles in the investigation area from geological and other regional information, based on the second stage network trained by the geological information-1D Vs profile pairs. We applied the proposed method to the Eastern part of Tokyo Metropolitan area to Southeastern part of Saitama prefecture, using dispersion curves, H/V and Vs profiles open to public as digital data, and predicted Vs profiles to 90 m deep with 200 m grid intervals. The predicted Vs model was reasonably consistent with surface topography, surface geology and geomorphological classification.
