The geophysical effects are the main factor that causes the nonlinear motion of the station, and a comprehensive analysis of the relationship between the GRACE seasonal load deformation and the GPS station coordinates is helpful to study the physical mechanism that causes the nonlinear motion of the station. Aiming at the continuous GPS coordinate time series in Greenland, this paper comprehensively analyzes the correlation between GRACE seasonal load deformation and GPS station coordinates. First, in order to improve the accuracy of GPS station coordinates, the principle component analysis (PCA) method was used to eliminate the common mode error (CME) of the station coordinates. The results show that this method effectively reduces the uncertainty of the station coordinates time series. Secondly, when extracting seasonal signals, it is found that the singular spectrum (SSA) method can effectively obtain the time-varying part of seasonal signals, and its extraction effect is better than that of the least square fitting (LSF) method. Finally, the seasonal relationship between GRACE load deformation and GPS station coordinates is analyzed from the aspects of time series change, correlation, and WRMS reduction. It is found that there are differences in the amplitude and phase parts of the time series. The mean value of correlation is 0.73, the maximum reduction of WRMS is 55.20% (QAQ1 station), and the minimum is −22.69% (KMJP station), indicating that most stations mainly exhibit seasonal load deformation, while individual stations cannot effectively reflect. In addition, the influence of GRACE seasonal load deformation on the station coordinate parameters is quantitatively analyzed. The results show that the best noise model of the station is mainly WN + FN, which effectively reduces the velocity uncertainty of the station coordinate, and weakens the seasonal term oscillation.

Based on GNSS observations, the co-seismic and post-seismic slip of the 2024 Noto Peninsula earthquake and the spatio-temporal pattern of afterslip are investigated in this paper. The co-seismic slip is mainly distributed in the depth range of 2 to 15 km with the maximum value of 5.94 m. Compared with the co-seismic rupture pattern, a shallow afterslip can be observed after the earthquake, and the afterslip patch is formed northeast of the epicenter. The maximum value of afterslip during the post-seismic 180 days is 1.13 m, which is situated at the longitude of 137.53°, latitude of 37.75°, and epth of 5.43 km. The spatio-temporal evolution of afterslip indicates that the fault activity has continued throughout the post-seismic 180 days, and the coverage and magnitude of afterslip have gradually increased. As time goes on, the fault activity tends to weaken, as evidenced by a decrease in slip rate. The daily images of afterslip demonstrate that the fault activity is particularly strong in the early time period following the earthquake. The maximum value of afterslip in the first week accounts for about 18% of that in the post-seismic 180 days, and the maximum slip rate reaches 0.043 m/day. In addition, the Coulomb stress analysis indicates that afterslip and most aftershocks appear in the positive Coulomb stress region, suggesting that co-seismic Coulomb stress changes may be the driving mechanism of afterslip and aftershocks.

Based on subdaily kinematic GNSS solutions, the fault slip properties during the very early postseismic phase after the 2021 M 8.2 Chignik earthquake are investigated in this paper. The very early postseismic deformations captured by near-field GNSS sites can be well depicted by the power model. The comparison of afterslip determined by daily and subdaily GNSS solutions suggests that neglecting very early afterslip can result in the underestimation of postseismic slip. Compared with coseismic slip, the cumulative afterslip of the first 24 h is mainly focused in the southeast of the hypocenter, and the shallow updip afterslip appears after this earthquake. The spatio-temporal evolution of the afterslip reveals that the patch of afterslip is immediately generated after the earthquake, and then the postseismic slip gradually grows along the afterslip patch. The magnitude of the afterslip patch varies remarkably within the 24 h following the earthquake, especially in the first several hours. Meanwhile, the spatio-temporal patterns of aftershocks and afterslip exhibit strong similarity during the first 24 h, suggesting that very early afterslip may be a possible driving factor of aftershocks. Moreover, most of the afterslip patches and aftershocks occurring immediately after this earthquake are situated in the area covered by positive Coulomb Stress Change (CSC), which implies that the immediate afterslip and aftershock activities can be influenced by the coseismic CSC. The following afterslip process further releases coseismic CSC and then influences the spatio-temporal variations of aftershock activities. Thus, the afterslip may be a possible triggering mechanism of very early aftershocks for this earthquake, alongside the effects of the CSC generated by coseismic rupture.

In recent years, the increasing mass loss of the Greenland Ice Sheet (GrIS) has a profound impact on the global sea level and climate change. Although some studies have investigated the rate and acceleration of mass loss in GrIS, research on its seasonal variation is relatively limited. In this regard, we firstly use Gravity Recovery and Climate Experiment (GRACE) multi-sourced data to estimate the GrIS mass changes from January 2003 to December 2016. Then, we study the annual variations of ice sheet mass and its driving factors (Modèle Atmosphérique Régionale 3.9 model variables and ice discharge) through singular spectrum analysis (SSA) and wavelet transform analysis. Results show the following: (1) Based on the generalized three-cornered hat method (TCH), the ice sheet mass change obtained from the combined data from different mechanisms, the seasonal annual amplitude variation extracted by SSA is within 200 Gt. (2) In terms of the seasonality, it is also found that the dominant factor of the GrIS mass change is runoff (RU) and ice melt (ME), while snowfall (SF) and rainfall (RF) have little effect on it. Among them, the mean annual amplitudes of the RU and ME are 1.1 and 1.5 times of GRACE respectively, and are not in phase with GRACE. (3) Through the analysis of relative phase and phase approximation, GRACE-derived data has an annual oscillation period with the RU, ME and RF, which lags their phases by 5.2, 5.4 and 4.7 months, respectively, while SF takes only 1.8 months. In addition, the average phase approximation values of the ME and GRACE are larger than those of the RU and RF, while that of the SF is positive. The change trend of the SF is basically opposite to that of the ME, RU and RF, and only consistent in the period 2006–2012.