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 (STA/LTA) 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
applications. Each detection approach has been applied to three years of
seismic data recorded by stations in East Antarctica. Our results
emphasize that the most appropriate detection approach depends on the
data attributes and the study objectives. STA/LTA is well-suited for
applications that require rapid results even if there is a greater
likelihood for false positive detections, and correlation-based
techniques work well for identifying events with a high degree of
waveform similarity. Deep learning models offer the most adaptability if
dealing with a range of seismic sources and noise, and their performance
can be enhanced with transfer learning, if the detection parameters are
fine-tuned to ensure the accuracy and reliability of the generated
catalog. Our results in East Antarctic provide new insight into polar
seismicity, highlighting both cryospheric and tectonic events, and
demonstrate how automated event detection approaches can be optimized to
investigate seismic activity in challenging environments.