Machine Learning for Single-Station Detection of Transient Deformation in GPS Time Series With a Case Study of Cascadia Slow Slip

Inspired by recent studies using various machine learning methods on different types of time series data (e.g., seismic, sea floor pressure), this study proposes a simple machine learning method, based on the recurrent neural network approach, for transient deformation detection in Global Positioning System time series. Unlike most previous studies using a sliding window technique, our model uses a single data point of the entire time series as sequential input and directly outputs the transient probability for all points in the time series. As a case study, we apply our method to detect slow-slip events in Cascadia between 2005 and 2016. The specific model is first trained and validated using synthetic data, and then used to detect slow-slip events on real Cascadia data. Based on our detection results, the spatial extent, duration, and migration of the major events are consistent with previous studies. As a benchmark, we compared our results in detail with those based on the Relative Strength Index (RSI). In general, our ML model detects more stations likely to be associated with nearby slow-slip events than the RSI model, especially if there are data gaps in the timeseries.