Predicting the seismic ground-motion parameters: 3D physics-based numerical simulations combined with artificial neural networks

Typically, it is challenging to incorporate near-surface soils into 3D physics-based numerical simulations (PBSs) for ground-motion prediction. The low shear wave speed of near-surface soils, coupled with the complexity of the soil seismic response, poses significant difficulties. To overcome these limitations, a hybrid approach was proposed in this study, combining PBSs with artificial neural networks (ANNs). The essence of the hybrid method can be summarized as follows: (1) development of ANN models, establishing a strong-motion database, training the ANNs on it to predict the ground-motion parameters for East-West (EW), North-South (NS), and Vertical (UD) directions afterward; (2) establishment of 3D PBS model, obtaining the ground-motion parameters of the bedrock face corresponding to a certain shear wave speed; (3) application of the trained ANNs to predict the ground-motion parameters on the ground surface, taking the simulated results and related site parameters as inputs, and the outputs are peak ground acceleration (PGA) and 5% damped spectral accelerations (Sa) at different periods on the ground surface. In this study, ANN models were trained on a strong-motion database based on Kiban-Kyoshin Network (KiK-net). After several verifications of the ANN predictions, a case study of the 21 October 2016 Mw6.2 Central Tottori earthquake was conducted. In addition to the comparison with observations, the broadband (0.1-10 Hz) results of the hybrid method were also compared with the results that obtained by transfer function based on recorded data and Next Generation Attenuation (NGA)-West2 ground-motion prediction equations (GMPEs) to demonstrate the effectiveness and applicability of the proposed method. In addition, the distribution of Sa for four periods in simulated area was presented. The performance of the hybrid method for predicting broadband ground-motion characteristics was generally satisfactory.