We installed 10 continuous Global Positioning System (GPS) stations on the northeast margin of the Tibetan Plateau at the end of 2012, in order to qualitatively investigate strain accumulation across the Liupanshan Fault (LPSF). We integrated our newly built stations with 48 other existing GPS stations to provide new insights into three-dimensional tectonic deformation. We employed white plus flicker noise model as a statistical model to obtain realistic velocities and corresponding uncertainties in the ITRF2014 and Ordos-fixed reference frame. The total velocity decrease from northwest to southeast in the Longxi Block (LXB) was 5.3 mm/yr within the range of 200 km west of the LPSF on the horizontal component. The first-order characteristic of the vertical crustal deformation was uplift for the northeastern margin of the Tibetan Plateau. The uplift rates in the LXB and the Ordos Block (ORB) were 1.0 and 2.0 mm/yr, respectively. We adopted an improved spherical wavelet algorithm to invert for multiscale strain rates and rotation rates. Multiscale strain rates showed a complex crustal deformation pattern. A significant clockwise rotation of about 30 nradians/yr (10⁻⁹ radians/year) was identified around the Dingxi. Localized strain accumulation was determined around the intersectional region between the Haiyuan Fault (HYF) and the LPSF. The deformation pattern across the LFPS was similar to that of the Longmengshan Fault (LMSF) before the 2008 Wenchuan M_S 8.0 earthquake. Furthermore, according to the distributed second invariant of strain rates at different spatial scale, strain partitioning has already spatially localized along the Xiaokou–Liupanshan–Longxian–Baoji fault belt (XLLBF). The tectonic deformation and localized strain buildup together with seismicity imply a high probability for a potential earthquake in this zone.

On 22 May 2021, an $Mw$ 7.4 earthquake struck Maduo, China, within the eastern Bayan Har block of the Tibetan plateau. The eastward‐extruding Bayan Har block is marked by active seismicity along its boundary faults, including the 2008 $Mw$ 7.9 Wenchuan earthquake, but large earthquakes within the block are relatively rare. Thus, the Maduo earthquake could provide useful information about crustal deformation of the Tibetan plateau. Early reports, shortly after the earthquake, have suggested a sinistral strike‐slip fault rupture, but the fault geometry and slip distribution vary in these models due to the limited observational constraints. Here, we reconstructed a model of fault geometry and coseismic slip using Interferometric Synthetic Aperture Radar and Global Positioning System data. A nonplanar fault model was constructed based on pixel‐offset images and the optimized dip angle. The along‐strike variation of the dip angle is small, so a single optimized dip is used. Our results suggest that the Maduo earthquake ruptured ∼156 km on a northwest‐striking major fault that dips 78°, and ∼24 km on a minor southeast‐striking fault that dips 64°. Most fault slip occurred above 15 km depth, and released a moment of $∼1.65×1020N·m$⁠. Using the resolved fault source model, we calculated the change of coulomb failure stress in the region and on the neighboring faults. The Maduo earthquake highlighted intrablock deformation in the Tibetan plateau whereas numerous lithospheric blocks extrude along major strike‐slip faults.

Seismogenic fault geometry, especially for a blind fault, is usually difficult to derive, based only on the distribution of aftershocks and interference fringes of Interferometric Synthetic Aperture Radar (InSAR). To better constrain the fault geometry of the 2017 Jiuzhaigou Mw 6.5 earthquake, we first carried out a nonlinear inversion for a single fault source using multi-peak particle swarm optimization (MPSO), Monte Carlo (MC), and Markov Chain Monte Carlo (MCMC) algorithms, respectively, with constraints of InSAR data in multiple SAR viewing geometries. The fault geometry models retrieved with different methods were highly consistent and mutually verifiable, showing that a blind faulting with a strike of ~154° and a dip angle of ~77° was responsible for the Jiuzhaigou earthquake. Based on the optimal fault geometry model, the fault slip distribution jointly inverted from the InSAR and Global Positioning System (GPS) data by the steepest descent method (SDM) and the MC method showed that the slip was mainly concentrated at the depth of 1–15 km, and only one slip center appeared at the depth of 5–9 km with a maximum slip of about 1.06 m, some different from previous studies. Taking the shear modulus of μ = 32 GPa, the seismic moment derived from the distributed slip model was about 7.85 × 10¹⁸ Nm, equivalent to Mw 6.54, which was slightly larger than that from the focal mechanism solutions. The fault spatial geometry and slip distribution could be further validated with the spatial patterns of the immediate aftershocks. Most of the off-fault aftershocks with the magnitude > M2 within one year after the mainshock occurred in the stress positive stress change area, which coincided with the stress triggering theory. The static Coulomb stress, triggered by the mainshock, significantly increased at the Tazang fault (northwest to the epicenter), and at the hidden North Huya fault, and partial segments of the Minjiang fault (west of the epicenter).

Most earthquakes are considered to be caused by stress accumulating in and subsequently releasing from the crust. To extract non-linear and non-stationary earthquake-induced signals associated with stress accumulation, the Hilbert–Huang transform was utilized to filter long-term movements, short-term noise, and frequency-dependent (annual and semi-annual) variations from surface displacements measured by the global positioning system (GPS) in Taiwan. Earthquake-related surface displacements were expressed as horizontal directions (i.e., GPS azimuths) using the north–south and east–west components of residual GPS data to bypass influences resulted from the inhomogeneous nature of the crust. Analytical results showed that the relationships between earthquake occurrence and the aligned GPS azimuth passed the statistical test of the Molchan’s error diagram. Aligned GPS azimuths were in agreement with direction of earthquake-related P axes for 81% (26/32) studied events. Areas with the highest paralleling orientations of GPS azimuths appeared around epicenters several days to weeks before earthquake occurrence. Durations from aligned GPS azimuths to earthquake occurrence are roughly proportional to earthquake magnitude. Similar variations of the GPS azimuths were observed in GPS data containing or excluding co-seismic dislocation (i.e., one day before) in the temporal and spatial domain. These suggest that the aligned GPS azimuth could be a promising anomalous phenomenon for studying crustal deformation before earthquakes.

Using global positioning system (GPS) observations of northeastern China and the southeast of the Russian Far East over the period 2012–2017, we derived an ITRF2014-referenced velocity field by fitting GPS time series with a functional model incorporating yearly and semiannual signals, linear trends, and offsets. We subsequently rotated the velocity field into a Eurasia-fixed velocity field and analyzed its spatial characteristics. Taking an improved multiscale spherical wavelet algorithm, we computed strain rate tensors and analyzed their spatial distribution at multiple scales. The derived Eurasia-referenced velocity field shows that northeastern China generally moved southeastward. Extensional deformation was identified at the Yilan–Yitong Fault (YYF) and the Dunhua–Mishan Fault (DMF), with negligible strike–slip rates. The principal strain rates were characterized by NE–SW compression and NW–SE extension. The dilation rates show compressional deformation in the southern segment of the YYF, northern end of the Nenjiang Fault (NJF), and southeast of the Russian Far East. We also investigated the impact of the 2011 Tohoku M_w 9.0 earthquake on the crustal deformation of northeastern China, generated by its post-seismic viscoelastic relaxation. The velocities generated by the post-seismic viscoelastic relaxation of the giant earthquake are generally orientated southeast, with magnitudes inversely proportional with the epicentral distances. The principal strain rates caused by the viscoelastic relaxation were also characterized by NW–SE stretching and NE–SW compression. The dilation rates show that compressional deformation appeared in the southern segment of the DMF and the YYF and southeast of the Russian Far East. Significant maximum shear rates were identified around the southern borderland between northeastern China and the southeast of the Russian Far East. Finally, we compared the multiple strain rates and the seismicity of northeastern China after the 2011 Tohoku earthquake. Our finding shows that the M_L ≥ 4.0 earthquakes were mostly concentrated around the zones of high areal strain rates and shear rates at scales of 4 and 5, in particular, at the DMF and YYF fault zones.

The Crustal Movement Observation Network of China (CMONOC) has begun receiving BeiDou Navigation Satellite System (BDS) observations since 2015, and accumulated more than 2.5 years of data. BDS observations has been widely applied in many fields, and long-term continuous data provide a new strategy for the study of crustal deformation in China. This paper focuses on the evaluation of BDS positioning performance and its potential application on crustal deformation in CMONOC. According to the comparative analysis on multipath delay (MPD) and signal to noise ratio (SNR) between BDS and GPS data, the data quality of BDS is at the same level with GPS measurements in COMONC. The spatial distribution of BDS positioning accuracy evaluated as the root mean square (RMS) of daily residual position time series on horizontal component is latitude-dependent, declining with the increasing of station latitude, while the vertical one is randomly distributed in China. The mean RMS of BDS position residual time series is 7 mm and 22 mm on horizontal and vertical components, respectively, and annual periodicity in position time series can be identified by BDS data. In view of the accuracy of BDS positioning, there are no systematic differences between GPS and BDS results. Based on time series analysis with data volume being 2.5 years, the noise characteristics of BDS daily position time series is time-correlated and corresponding noise is white plus flicker noise model, and the derived mean RMS of the BDS velocities is 1.2, 1.5, and 4.1 mm/year on north, east, and up components, respectively. The imperfect performance of BDS positioning relative to GPS is likely attributed to the relatively low accuracy of BDS ephemeris, and the sparse amount of MEO satellites distribution in the BDS constellation. It is expectable to study crustal deformation in CMONOC by BDS with the gradual maturity of its constellation and the accumulation of observations.