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Megathrust earthquakes release and transfer stress that has accumulated over hundreds of years, leading to large aftershocks that can be highly destructive. Understanding the spatiotemporal pattern of megathrust aftershocks is key to mitigating the seismic hazard. However, conflicting observations show aftershocks concentrated either along the rupture surface itself, along its periphery or well beyond it, and they can persist for a few years to decades. Here we present aftershock data following the four largest megathrust earthquakes since 1960, focusing on the change in seismicity rate following the best-recorded 2011 Tohoku earthquake, which shows an initially high aftershock rate on the rupture surface that quickly shuts down, while a zone up to ten times larger forms a ring of enhanced seismicity around it. We find that the aftershock pattern of Tohoku and the three other megathrusts can be explained by rate and state Coulomb stress transfer. We suggest that the shutdown in seismicity in the rupture zone may persist for centuries, leaving seismicity gaps that can be used to identify prehistoric megathrust events. In contrast, the seismicity of the surrounding area decays over 4-6 decades, increasing the seismic hazard after a megathrust earthquake.
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Online Few-Shot Time Series Classification for Aftershock Detection
Seismic monitoring systems sift through seismograms in real-time, searching for target events, such as underground explosions. In this monitoring system, a burst of aftershocks (minor earthquakes occur after a major earthquake over days or even years) can be a source of confounding signals. Such a burst of aftershock signals can overload the human analysts of the monitoring system. To alleviate this burden at the onset of a sequence of events (e.g., aftershocks), a human analyst can label the first few of these events and start an online classifier to filter out subsequent aftershock events. We propose an online few-shot classification model FewSig for time series data for the above use case. The framework of FewSig consists of a selective model to identify the high-confidence positive events which are used for updating the models and a general classifier to label the remaining events. Our specific technique uses a %two-level decision tree selective model based on sliding DTW distance and a general classifier model based on distance metric learning with Neighborhood Component Analysis (NCA). The algorithm demonstrates surprising robustness when tested on univariate datasets from the UEA/UCR archive. Furthermore, we show two real-world earthquake events where the FewSig reduces the human effort in monitoring applications by filtering out the aftershock events.
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- Award ID(s):
- 2104537
- PAR ID:
- 10526417
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400701030
- Page Range / eLocation ID:
- 5707 to 5716
- Format(s):
- Medium: X
- Location:
- Long Beach CA USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3580305.3599879
