Improving PhaseNet and Investigating Loss Functions in Seismic Wave Detection

Abstract

Recent advancements have introduced deep learning techniques into seismic wave detection, particularly for identifying P-waves and S-waves and estimating their arrival times. However, most CNN-based models currently employed—such as the simple U-Net structures exemplified by PhaseNet — rely heavily on Cross Entropy loss functions, leaving considerable room for improvement. In this study, we propose a novel model architecture based on PhaseNet that is better suited to seismic waveforms, alongside a loss function specifically tailored for seismic wave detection. We tested our proposed method on a subset of the metropolitan seismic waveform dataset provided by GeoSciAI2024, achieving an average residual sum of squares of 0.5613. These results demonstrate the potential of our approach to enhance the accuracy of seismic wave detection.