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

Abstract

AbstractInspired 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.