Machine learning and feature extraction for detecting transient signals in GNSS time series

Advances in the processing of Global Navigation Satellite System (GNSS) positioning data and the increasing densification of geodetic networks have provided an unprecedented opportunity to detect and analyse transient deformation signals, including Slow Slip Events (SSE). These events, characterised by very slow rupture and durations of days to months, are often associated with areas of low coupling and sometimes show clear recurrence patterns. Despite their importance in subduction zones, reliable detection of SSEs remains an ongoing challenge. The sheer volume of GNSS data, combined with high noise levels and the subtle nature of these signals, requires efficient and robust methods capable of rapidly processing large datasets.

To overcome these challenges, we propose a method that relies on feature extraction techniques and machine learning to improve the detection and analysis of possible SSEs. Specifically, we use the TSFRESH algorithm to extract relevant features from GNSS time series, coupled with supervised machine learning classification techniques. Preliminary results of our current model, trained on synthetic data and validated through various performance tests, demonstrate high detection capabilities and accuracy. We further validated the model using a collection of GNSS time series from the Cascadia subduction zone with a single-station method scaled to the entire network, where the model showed satisfactory performance in detecting possible SSEs compared to similar work. Future efforts will focus on improving the robustness and generalisation of the model to new data, and refining methods for estimating the slip and duration of each possible SSE.