Browsing by Author "Yilmaz, Baris"

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    Article
    Deep Learning-Based Epicenter Localization Using Single-Station Strong Motion Records
    (Springer, 2025) Turkmen, Melek; Meral, Sanem; Yilmaz, Baris; Cikis, Melis; Akagunduz, Erdem; Tileylioglu, Salih
    This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related studies, strong motion records contain rich information for source parameter inference. We investigate whether DL-based methods can effectively leverage this data for accurate epicenter localization. Our study introduces AFAD-1218, a collection comprising more than 36,000 strong motion records sourced from Turkey. To utilize the strong motion records represented in either the time or the frequency domain, we propose two neural network architectures: deep residual network and temporal convolutional networks. Our findings highlight significant reductions in prediction error achieved through the exclusion of low signal-to-noise ratio records, both in nationwide experiments and regional transfer-learning scenarios. Overall, this research underscores the promise of DL techniques in harnessing strong motion records for improved seismic event characterization and localization. Our codes are available via this repo: https://github.com/melekturkmen/EarthQuakeLocalization
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    Conference Object
    Generation and Analysis of Strong Motion Signals via Diffusion Model
    (IEEE, 2025) Dogrusoz, Rem; Yilmaz, Baris; Tileylioglu, Salih; Akagunduz, Erdem
    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.