Automated Detection of Short-Term Slow Slip Events Using GNSS Data via Change-Point Analysis
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Authors
Ma, Yiming
Anastasiou, Andreas
Montiel, Fabien
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Journal Article
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Oxford University Press (OUP)
Abstract
Inferring from the occurrence pattern of slow slip events (SSEs) the probability of triggering a damaging earthquake within the nearby velocity weakening portion of the plate interface is critical for hazard mitigation. Although robust methods exist to detect long-term SSEs consistently and efficiently, detecting short-term SSEs remains a challenge. In this study, we propose a novel statistical approach, called singular spectrum analysis isolate-detect (SSAID), for automatically estimating the start and end times of short-term SSEs in GNSS data. The method recasts the problem of detecting SSEs as that of identifying change-points in a piecewise non-linear signal. This is achieved by obscuring the deviation from piecewise-linearity in the underlying SSE signals using added noise. We verify its effectiveness on a range of synthetic SSE data with different noise levels, and demonstrate its superior performance compared to two existing methods. We illustrate its capability in detecting short-term SSEs in observed GNSS data from 36 stations in southwest Japan via the co-occurrence of non-volcanic tremors, hypothesis tests and fault estimation.Description
Keywords
37 Earth Sciences, 40 Engineering, 3706 Geophysics, 0403 Geology, 0404 Geophysics, 0909 Geomatic Engineering, Geochemistry & Geophysics, 3705 Geology, 4013 Geomatic engineering, Satellite geodesy, Transient deformation, Time-series analysis, Earthquake ground motions, Episodic tremor and slip
Source
Geophysical Journal International, ISSN: 0956-540X (Print); 1365-246X (Online), Oxford University Press (OUP), 244(3), ggaf517-. doi: 10.1093/gji/ggaf517
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© The Author(s) 2025. Published by Oxford University Press on behalf of The Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.