East Antarctic Seismicity from different Automated Event Detection Algorithms ...

• Main Authors: Hansen, Samantha, Ho, Long, Walter, Jacob
• Format: Dataset
• Language: English
• Published: U.S. Antarctic Program (USAP) Data Center 2024
• Subjects: Seismometer; GeoscientificInformation; Cryosphere; Seismology; Machine Learning; Seismic Event Detection; Antarctic; East Antarctica; Antarc*; Antarctica
• Online Access: https://dx.doi.org/10.15784/601762; https://www.usap-dc.org/view/dataset/601762

As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude events and phenomena such as icequakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal was not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different ...