4 papers
TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
Baris Yilmaz, Bevan Deniz Cilgin, Erdem Akagündüz +1
Effective earthquake risk reduction relies on accurate site-specific evaluations, which require models capable of representing the influence of local site conditions on ground moti…
Variational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms
Turkan Simge Ispak, Salih Tileylioglu, Erdem Akagunduz
Accurate P-wave detection is critical for earthquake early warning, yet strong-motion records pose challenges due to high noise levels, limited labeled data, and complex waveform c…
Exploring Challenges in Deep Learning of Single-Station Ground Motion Records
Ãmit Mert ÃaÄlar, Baris Yilmaz, Melek Türkmen +2
Contemporary deep learning models have demonstrated promising results across various applications within seismology and earthquake engineering. These models rely primarily on utili…
Deep Sequence Models for Predicting Average Shear Wave Velocity from Strong Motion Records
Baris Yilmaz, Erdem Akagündüz, Salih Tileylioglu
This study explores the use of deep learning for predicting the time averaged shear wave velocity in the top 30 m of the subsurface () at strong motion recording stations…