Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records

TL;DR

This study applies deep learning models to strong motion records for single-station epicenter localization, demonstrating improved accuracy and potential for seismic analysis using a large dataset from Turkey.

Contribution

Introduces neural network architectures tailored for strong motion data and shows their effectiveness in seismic epicenter localization, especially when filtering low-quality records.

Findings

Deep learning models significantly reduce localization errors.

Filtering low signal-to-noise ratio records improves accuracy.

Models perform well in both nationwide and regional transfer scenarios.

Abstract

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.