2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Deep Learning-based Average Shear Wave Velocity Prediction using Accelerometer Records
Barış Yılmaz, Melek Türkmen, Sanem Meral +2
Assessing seismic hazards and thereby designing earthquake-resilient structures or evaluating structural damage that has been incurred after an earthquake are important objectives…
Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records
Melek Türkmen, Sanem Meral, Baris Yilmaz +3
This paper explores the application of deep learning (DL) techniques to strong motion records for single-station epicenter localization. Often underutilized in seismology-related s…
Exploring Challenges in Deep Learning of Single-Station Ground Motion Records
Ümit Mert Çağlar, 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…