  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 offer a
potential wealth of information about seismic events. 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. Through extensive experimentation, we demonstrate the efficacy of DL
approaches in extracting meaningful insights from these records, showcasing
their potential for enhancing seismic event analysis and localization accuracy.
Notably, 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.
