Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning

Slow slip events are typically associated with seismic activity, but the internal interactions and their relationship with earthquakes are still not well understood. With the rapid increase in Global Navigation Satellite System (GNSS) data, it has become possible to study the subtle slow slip signals in GNSS displacement time series using deep learning. In this study, GNSS displacement time series data from the Cascadia subduction zone are preprocessed using the Variational Bayesian Independent Component Analysis method to effectively remove non-slow-slip signals. Additionally, we developed a deep learning model that includes a multi-layer bidirectional Long Short-Term Memory  neural network and an attention mechanism, which can effectively detect slow slip events from complex data. Through this deep learning model, we successfully detected 56 slow slip events in the Cascadia region from 2012 to 2022. The start times, durations, spatial distribution, and propagation patterns of these 56 events were consistent with earthquake catalogs, providing new insights into the slow slip behavior of the Cascadia subduction zone. Overall, our work offers an effective framework for extracting subtle signals hidden in GNSS time series.