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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 earthquakes and other types of seismic events such as icequakes or firnquakes, 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 is not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluates the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The performance of these different event detection techniques applied to continuous seismic data from a polar environment will be highlighted. We will also identify potential limitations and necessary adjustments for deep learning algorithm training, which is essential for their reliable application to specific datasets.
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The Matrix Profile in Seismology: Template Matching of Everything With Everything
Abstract Template matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the ‘Matrix Profile’ (MP), a “correlate everything with everything” calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research.
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- PAR ID:
- 10492553
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 129
- Issue:
- 2
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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