Single-station automated detection of transient deformation in GPS time series with the relative strength index: A case study of Cascadian slow slip

The discovery of slow-slip events over the past decades has changed our understanding of tectonic hazards and the earthquake cycle. Proper geodetic characterization of slow-slip events is necessary for studies of regional interseismic, coseismic and postseismic deformation, and miscalculations can affect our understanding of the regional stress field and tectonic hazard. Because of the proliferation of GPS data over the last two decades, an automated algorithm is required to analyze the signals and model the deformation on a station by station basis. Using the relative strength index (RSI), a financial momentum oscillator, we test the ability to detect events of various sizes and durations. We first determine the statistics of the RSI under different noise conditions and then use this information as the basis for the automated transient detection algorithm by testing different synthetic signals. We then apply the technique to daily GPS displacement time series from 213 stations along the Cascadia subduction zone to form a record of transient deformation between 2005 and 2016. Our estimates of the spatial extent, duration, and propagation of major episodic tremor and slip (ETS) events are consistent with previous studies. We use the automated detections to remodel the displacement time series and obtain transient deformation rates over the past decade and discuss the tectonic implications. Finally, we analyze the correlation between transient detections and tremor showing good agreement between the two at slab depths commonly associated with ETS events.