Browsing by Author "Yilmaz, B."

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Exploring Challenges in Deep Learning of Single-Station Ground Motion Records
(Springer Science and Business Media Deutschland GmbH, 2025) Çaǧlar, Ü.M.; Yilmaz, B.; Türkmen, M.; Akagündüz, E.; Tileylioglu, S.
Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utilizing ground motion records for tasks such as earthquake event classification, localization, earthquake early warning systems, and structural health monitoring. However, the extent to which these models truly extract “deep” patterns from these complex time-series signals remains underexplored. In this study, our objective is to evaluate the degree to which auxiliary information, such as seismic phase arrival times or seismic station distribution within a network, dominates the process of deep learning from ground motion records, potentially hindering its effectiveness. Our experimental results reveal a strong dependence on the highly correlated Primary (P) and Secondary (S) phase arrival times. These findings expose a critical gap in the current research landscape, highlighting the lack of robust methodologies for deep learning from single-station ground motion recordings that do not rely on auxiliary inputs. © 2025 Elsevier B.V., All rights reserved.
Generation And Analysis Of Strong Motion Signals Via Diffusion Model
(Institute of Electrical and Electronics Engineers Inc., 2025) Dogrusoz, I.; Yilmaz, B.; Tileylioglu, S.; Akagündüz, E.
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. © 2025 Elsevier B.V., All rights reserved.