Generation and Analysis of Strong Motion Signals via Diffusion Model

Abstract

This study examines the potential of Denoising Diffusion Probabilistic Models in earthquake engineering to generate seismic signals and learn deep representations. The complex nature of seismic data and its noise are major obstacles that hinder the extraction of meaningful features. Traditional supervised learning methods are limited in their generalization capacity due to their dependence on labeled data. Diffusion models, however, promise to overcome these limitations by generating conditional seismic signals and enhancing the reliability of early warning systems. This study aims to demonstrate how diffusion-based methods contribute to earthquake engineering and propose an approach for seismic data analysis and detection of the P-wave arrival time. The obtained results show that the model can grasp certain patterns; however, larger-scale datasets are needed for more realistic signal generation and a deeper understanding of seismic features.