Abstract
Slow slip events (SSEs), a type of slow earthquakes, are thought to play, an important role in releasing strain in subduction zones. We may be able to infer from their occurrence pattern the probability of triggering a damaging earthquake within the nearby velocity weakening portion of the plate interface, although the underlying geophysical mechanism governing SSEs remains elusive. In this thesis, we investigate the conditions under which recurrent SSEs can spontaneously occur and develop a new method to detect automatically short-term SSEs in GPS data. For the first of the two works, we conduct an extensive sensitivity analysis on four parameters to investigate the conditions for forming SSEs in a simplified Cascadia-like subduction model, within the modelling framework of rate- and state-dependent friction (RSF) laws. For the second of the two works, we propose a new detection method, called singular spectrum analysis isolate-detect (SSAID), which recasts the problem of detecting SSEs as that of detecting change-points in a piecewise-linear signal. This is achieved by obscuring the deviation from piecewise-linearity in the underlying SSE signals using added noise. We demonstrate its effectiveness using both simulated and observed SSE data. To bring together the two works in the process, we conduct a Bayesian inversion on observed SSE data, which estimates a finite rectangular fault model for each detected SSE candidate. This helps us to evaluate the probability of the occurrence of an actual SSE for
each detected SSE candidate. Finally, we illustrate the general applicability of SSAID using both synthetic piecewise-non-linear signals with known structures and real data sets from various disciplines including the number of COVID-19 daily confirmed cases in the United States and the monthly S&P 500 close price index.