Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica

Abstract

As seismic data collection continues to grow, advanced automated processing techniques for robust phase identification and event detection are becoming increasingly important. However, the performance, benefits, and limitations of different automated detection approaches have not been fully evaluated. Our study examines how the performance of conventional techniques, including the Short‐Term Average/Long‐Term Average method and cross‐correlation approaches, compares to that of various deep learning models. We also evaluate the added benefits that transfer learning may provide to machine learning‐based seismic applications. Each detection approach has been applied to 3 years of data recorded by stations in East Antarctica, which contain both cryospheric and tectonic‐related seismic events. Our results demonstrate that by integrating different event detection approaches with transfer learning, the strengths of each approach can maximize seismic detection accuracy and reliability while also enhancing the completeness of associated event catalogs. That said, model performance can vary, depending on the quality of the data set as well as the data augmentation schemes and training strategies employed. Our results in East Antarctica provide new insight into polar seismicity and demonstrate how automated event detection approaches can be optimized to investigate seismic activity in challenging environments.