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Wavelet methods for the analysis of GPS recordings of slow earthquakes
  • Ariane Ducellier,
  • Kenneth Creager
Ariane Ducellier
University of Washington

Corresponding Author:ariane.ducellier.pro@gmail.com

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Kenneth Creager
Univ 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 GPS networks. They 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. Moreover, whereas regular earthquakes occur along the shallow part of the dipping plate boundary (in the seismogenic / locked zone), slow-slip events often occur on the plate boundary downdip of the locked zone. Slow-slip events could potentially trigger large earthquakes. This phenomenon provides a potential opportunity to further our understanding of subduction zone processes, and evaluate the time-varying seismic hazard. 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 recordings of slow-slip events in New Zealand. An important application of the DWT and the MODWT is the estimation of a signal hidden by noise within an observed time series. We used synthetic time series with slow slip events of different durations, to which a Gaussian noise has been added, and denoised the signal using a wavelet-based method, and a low-pass filter. Although the signal was barely visible behind the noise, we could see a unique ramp-like signal in the data denoised with the wavelet-based method, whereas the low-pass filtered signal showed several ramp-like features which were not present in the original synthetic data. Eventually, we aim to be able to detect possible smaller (magnitude 5) slow-slip events that may be currently undetected with standard methods, detect longer (months to years) slow-slip events that are more difficult to detect than slow-slip events with a short duration (days to weeks), and determine the vertical displacement of the ground surface during a slow-slip event.