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Wavelet methods for detecting slow slip events in GNSS recordings
  • Ariane Ducellier,
  • Kenneth Creager,
  • David Schmidt
Ariane Ducellier
University of Washington

Corresponding Author:[email protected]

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Kenneth Creager
Univ Washington
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David Schmidt
University of Washington
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Slow slip events were discovered in many subduction zones during the last two decades thanks to recordings of the displacement of Earth’s surface by dense GNSS networks. Slow slip can last from a few days to several years and have a relatively short recurrence time (months to years), compared to the recurrence time of regular earthquakes (up to several hundreds of years), allowing scientists to observe and study many complete event cycles. In many places, tectonic tremor is also observed in relation to slow slip and can be used as a proxy to study slow slip events of moderate magnitude where surface deformation is hidden in GNSS noise. However, in subduction zones where no clear relationship between tremor and slow slip occurrence is observed, these methods cannot be applied, and we need other methods to be able to better detect and quantify slow slip. Wavelets methods such as the Discrete Wavelet Transform (DWT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) are mathematical tools for analyzing time series simultaneously in the time and the frequency domain by observing how weighted averages of a time series vary from one averaging period to the next. In this study, we use wavelet methods to analyze GPS time series and seismic recordings of slow slip events in Cascadia. We use detrended GPS data, apply the MODWT transform and stack the wavelet details over several nearby GPS stations. As an independent check on the timing of slow slip events, we also compute the cumulative number of tremors in the vicinity of the GPS stations, detrend this signal, and apply the MODWT transform. In both time series, we can then see simultaneous waveforms whose timing corresponds to the timing of slow slip events. We assume that there is a slow slip event whenever there is a peak in the wavelet signal. We verify that there is a good correlation between slow slip events detected with only GPS data, and slow slip events detected with only seismic data. The wavelet-based detection method detects all events of magnitude higher than 6 as determined by independent event catalogs (e.g. Michel et al., 2019, Pure Appli. Geophys.).