Seismic Phase

Seismic phase analysis focuses on identifying and interpreting different wave arrivals (e.g., P-waves, S-waves) in seismograms to understand earthquake characteristics and subsurface structures. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), U-Nets, graph neural networks (GNNs), and transformers to automate tasks such as phase picking, association, and location estimation, often integrating multiple tasks into a single model. These advancements improve the accuracy and efficiency of earthquake monitoring, enabling more precise hazard assessments and a deeper understanding of seismic phenomena, particularly in high-rate seismicity regimes. The resulting improvements in automated analysis are crucial for expanding our knowledge of earthquake processes and improving early warning systems.