Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica
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 evaluate...
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2024
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crwinnower:10.22541/essoar.171033153.33947266/v1 2024-06-02T07:55:28+00:00 Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica Ho, Long Minh Walter, Jacob I. Hansen, Samantha E Sánchez-Roldán, José Luis Peng, Zhigang 2024 http://dx.doi.org/10.22541/essoar.171033153.33947266/v1 unknown Authorea, Inc. posted-content 2024 crwinnower https://doi.org/10.22541/essoar.171033153.33947266/v1 2024-05-07T14:19:23Z 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. Other/Unknown Material Antarc* Antarctic Antarctica East Antarctica Victoria Land The Winnower Antarctic East Antarctica Victoria Land |
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description |
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. |
format |
Other/Unknown Material |
author |
Ho, Long Minh Walter, Jacob I. Hansen, Samantha E Sánchez-Roldán, José Luis Peng, Zhigang |
spellingShingle |
Ho, Long Minh Walter, Jacob I. Hansen, Samantha E Sánchez-Roldán, José Luis Peng, Zhigang Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica |
author_facet |
Ho, Long Minh Walter, Jacob I. Hansen, Samantha E Sánchez-Roldán, José Luis Peng, Zhigang |
author_sort |
Ho, Long Minh |
title |
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica |
title_short |
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica |
title_full |
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica |
title_fullStr |
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica |
title_full_unstemmed |
Evaluating Automated Seismic Event Detection Approaches: An Application to Victoria Land, East Antarctica |
title_sort |
evaluating automated seismic event detection approaches: an application to victoria land, east antarctica |
publisher |
Authorea, Inc. |
publishDate |
2024 |
url |
http://dx.doi.org/10.22541/essoar.171033153.33947266/v1 |
geographic |
Antarctic East Antarctica Victoria Land |
geographic_facet |
Antarctic East Antarctica Victoria Land |
genre |
Antarc* Antarctic Antarctica East Antarctica Victoria Land |
genre_facet |
Antarc* Antarctic Antarctica East Antarctica Victoria Land |
op_doi |
https://doi.org/10.22541/essoar.171033153.33947266/v1 |
_version_ |
1800748946627231744 |