With the increase in Global Navigation Satellite System (GNSS) observations, the requirement for objective and automated detection of slow slip event (SSE) signals hidden in displacement time series is increasing. However, machine learning for GNSS time series has rarely been attempted, primarily due to the smaller number of SSEs compared to ordinary earthquakes, which renders it difficult to prepare training data. In this study, we conducted a single-site SSE detection based on machine learning trained by real GNSS observations of southwest Japan to directly consider the complicated spatiotemporal characteristics of observational noise. Based on a catalog of 284 short-term SSEs, approximately 16,000 time series containing SSE signals or noises were extracted as training data. The signal data predominantly had an amplitude of 1.5–2.0 mm. The model architecture following the Generalized Phase Detection, which was originally proposed for seismic wave detection, was then adopted. We obtained an accuracy of 97–98% for the test data. As expected, signals with smaller amplitudes showed a higher frequency of false negatives. The true-positive ratio was highest in the western Shikoku region, which had the largest signal amplitude. In contrast, the false-positive ratio exhibited a nearly random spatial distribution. False detection appears to be caused primarily by the bends in the time series that resemble the onset or termination of the SSE signal. The analysis of this study is expected to facilitate a straightforward evaluation of the influence of noise characteristics on the detection performance, and clarify the crucial topics to improve detection precision.

Monitoring and predicting fault slip behaviors in subduction zones are essential for understanding earthquake cycles and assessing future earthquake potential. We developed a data assimilation (DA) method for fault slip monitoring and short-term prediction of slow slip events (SSEs), which was applied to the 2010 Bungo Channel SSE in southwest Japan. The observed geodetic data were quantitatively explained using a physics-based model with DA. We investigated short-term predictability by assimilating observation data with limited periods. Without prior constraint on fault slip style, observations solely during slip acceleration predicted the occurrence of a fast slip; however, the inclusion of slip deceleration data successfully predicted a slow transient slip. With prior constraint to exclude unstable slip, the assimilation of data after the SSE occurrence predicted a slow transient slip. This study provided a tool using DA for fault slip monitoring and prediction based on real observation data.