Objectives Slow slip events (SSEs) are slow dislocation that occur in weak zones in the crust, and they may cause surface deformations and lead to slow earthquakes. However, the mechanism of SSEs and whether they will trigger earthquakes are still in the stage of discussion and speculation for developing the new principles and methods. To further study the characteristics of SSEs and the relationship with earthquakes, we use global positioning system (GPS) coordinate time series to invert SSEs. The spatiotemporal characteristics and evolution laws of SSEs are analyzed, and the relationship between SSEs and earthquakes is discussed.

Methods The proposed method is executed based on standard linear trajectory model (SLTM) and network inversion filter (NIF). First, the slow slip coordinate time series are obtained by modeling GPS continuous coordinate time series using SLTM. The steps are repaired, and the gross errors, the constant, the steady state velocity, and the annual and semi-annual periodic season terms are removed. Then, NIF is used to construct the fault grid, and the elastic Green's function is introduced to describe the relationship between fault slip and surface displacement. Using the slow slip coordinate time series and NIF, the slip vectors on the fault grid and surface displacement vectors are inverted. Finally, the magnitude and frequency of the earthquakes before and after SSEs are calculated, and the possible relationship between SSEs and earthquakes is analyzed.

Results Taking the 2018 SSE in Boso Peninsula of Japan as an example, the results show that the active period of the slow slip ranges from the 156th to the 169th days. The maximum cumulative slip is about 11.3 cm, and the maximum slip rate is about 2.7 cm/d. The central area of the slow slip is located in the southeast of Boso Peninsula (140.2°E-140.9°E,34.8°N-35.6°N). The SSE spreads southward slightly, and the depth of the slow slip changes from deep (about 23 km) to shallow (about 15 km). The frequency of regional earthquakes increases significantly during the SSE, and gradually recovers to normal in the following months. Therefore, the danger of approaching potential earthquake swarm can be identified from dynamic evolution of SSE in Boso Peninsula.

Conclusions GPS plays an important role in the detection of SSEs, but it is difficult to detect short period SSEs within a few days due to the influence of various long period tectonic movement information, and the uneven distribution of GPS stations on land and sea leads to the decrease of spatial resolution. Hence, in the future work, we will further study the physical mechanism of SSEs in combination with other monitoring means such as strain gauge, tilt-meter and submarine pressure gauge.