SITE-SPECIFIC STRONG MOTION GENERATION AND LATENT SPACE ANALYSIS AT SEISMIC STATIONS

Date

2025-11-3

Author

Bevan Deniz , Çılğın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Modeling the strong motion data is crucial for seismic hazard assessment. In our study, we aim to unveil the nature of strong motion data and soil characteristics using generative AI. We created a generative model that efficiently extracts underlying physical patterns from historical events with site-specific properties. To achieve this, we trained a Conditional Convolutional Variational Autoencoder model on amplitude and phase spectrograms of earthquake waveforms to generate strong-motion data that accurately represents the soil characteristics. We performed training in two steps: unsupervised learning on 31,249 records and fine-tuning (after minor architectural changes to create a conditional model) on 348 records for five stations. Ultimately, we compared the relevance of the generated signals by calculating the fundamental site frequency distributions of both the generated and original samples, linking the simulation results to a physical property of their sites. The proposed method employs a data-driven approach using recorded time waveforms and relies solely on station identifiers for conditional generation, differing from existing methods in the literature. Moreover, it uses latent space conditioning techniques to make the model class-aware without requiring a large amount of station-specific data. Unlike traditional methods, it doesn't need theoretical assumptions, extensive parameter tuning, or computational power. We also introduce an evaluation framework to numerically analyze the correctness of the generated waveforms from simulations by comparing fundamental site frequencies calculated from HVSR curves, which are calculated using earthquake recordings. Our model achieves an alignment score of 0.84 with the ideal situation. Generated waveforms accurately represent their fundamental site frequency characteristics and differentiate from the frequency characteristics of other sites.

Subject Keywords

Strong motion data generation, Variational autoencoder, Generative artificial intelligence, Conditional model, Latent Space Reshaping

URI

https://hdl.handle.net/11511/116639

Collections

Graduate School of Informatics, Thesis