ANN-Based Ground Motion and Physics-Based Broadband Models for Vertical Spectra

Abstract

This study proposes a new simplified Ground Motion Model (GMM) for vertical spectra by combining comprehensive datasets from the NESS and NGA-West2 databases. The proposed Artificial Neural Network (ANN) architecture-based model requires only 288 unknowns to predict spectral accelerations (Sa) at 33 distinct periods ranging from 0 to 4 s. Notably, this model inherently captures known physical phenomena with reduced variability using a minimum number of unknowns compared to the GMMs existing literature, thus offering a valuable addition to current hazard estimation frameworks. Furthermore, recognizing the necessity for physics-based simulations in vertical ground motion analysis, we introduce a physics-based broadband model for vertical spectra using ANN methodology. The proposed broadband model exhibits better robustness due to the comprehensiveness of the dataset utilized and the inclusion of source path and site characteristics at the input layer. Additionally, the model effectively captures the physical trends with minimal deviation. Further, we verified the predictive ability of the developed models through a comprehensive case study of the 2008 Iwate–Miyagi earthquake. The proposed models serve as essential tools for physics-based broadband simulations and hazard assessments in active shallow crustal regions.