Browsing by Author "Cikis, Melis"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    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
  • Thumbnail Image
    Conference Object
    Representing Earthquake Accelerogram Records for Cnn Utilization
    (IEEE, 2020) Cikis, Melis; Tileyoğlu, Salih; Akagündüz, Erdem
    In this study, a spectrogram based false color representation of earthquake accelergrams is proposed and its usability for both human investigation and its application in convolutional networks are discussed. By using more than forty two thousand earthquake records open to the public, an epicenter clustering algorithm was employed, and it was observed that earthquakes in similar clusters produce similar representations. The prospective purpose of the proposed representation is to estimate the epicenter of an earthquake by processing the accelerograms recorded in a single station using convolutional networks.