On 22ed May 2021 (local time), an earthquake of Ms7.4 struck Maduo county in Qinghai Province, China. This was the largest earthquake in China since the 2008 Wenchuan earthquake. In this study, ascending/descending Sentinel-1 and advanced land observation satellite-2 (ALOS-2) synthetic aperture radar (SAR) images were used to derive the three-dimensional (3-D) coseismic displacements of this earthquake. We used the differential interferometric SAR (InSAR, DInSAR), pixel offset-tracking (POT), multiple aperture InSAR (MAI), and burst overlap interferometry (BOI) methods to derive the displacement observations along the line-of-sight (LOS) and azimuth directions. To accurately mitigate the effect of ionospheric delay on the ALOS-2 DInSAR observations, a polynomial fitting method was proposed to optimize range-spectrum-split-derived ionospheric phases. In addition, the 3-D displacement field was obtained by a strain model and variance component estimation (SM-VCE) method based on the high-quality SAR displacement observations. Results indicated that a left-lateral fault slip with the largest horizontal displacement of up to 2.4 m dominated this earthquake, and the small-magnitude vertical displacement with an alternating uplift/subsidence pattern along the fault trace was more concentrated in the near-fault regions. Comparison with the global navigation satellite system data indicated that the SM-VCE method can significantly improve the accuracy of the displacements compared to the classical weighted least squares method, and the incorporation of the BOI displacements can substantially benefit the accuracy of north–south displacement. In addition to the displacements, three coseismic strain invariants calculated based on the strain model parameters were also investigated. It was found that the eastern and western parts of the faults suffered more significant strains compared with the epicenter region.

We present a new time series PU approach to improve the unwrapping accuracy in this article. The rationale behind is to first improve the sparse unwrapping by mitigating the phase gradient in a 2D network and then correcting the unwrapping errors in time based on the triplet phase closure. Rather than commonly-used Delaunay network, we employ All-Pairs-Shortest-Path (APSP) algorithm in graph theory to maximize the temporal coherence of all edges and to approach the phase continuity assumption in the 2D spatial domain. Next, we formulate the PU error correction in the 1D temporal domain as a compressed sensing (CS) problem, according to the sparsity rule of remaining phase ambiguity cycles. We finally estimate phase ambiguity cycles by means of Integer Linear Programming. The comprehensive comparisons on synthetic and real Sentinel-1 data covering Lost Hills, California confirm the validity of the proposed 2D+1D unwrapping approach.

Fault geometric complexity plays a critical role in earthquake rupture dimension. Fault bifurcations are commonly observed in earthquake geology, yet, robust kinematic rupture processes on bifurcated fault branches are largely missing, limiting our understanding of rupture dynamics and seismic hazard. Here, we holistically study the fault geometry and bilateral rupture of the 2021 Mw7.4 Maduo, China earthquake, that shows clear fault bifurcation near its eastern terminal. We integrate space geodesy imaging, back-projection of high-frequency teleseismic array waveforms, multiple point source and finite fault inversions, and constrain in detail the rupture process, in particular, through its fault bifurcation. Our models reveal a stable rupture speed of ~2.5 km/s throughout the entire rupture and a simultaneous rupture through fault branches bifurcated at 20°. The rupture on bifurcated faults radiated more high-frequency waves, especially from the stopping phases. The stopping phase on the southern branch likely stopped the rupture on the northern branch.

Interferometric Synthetic Aperture Radar (InSAR) phase unwrapping error is a major limiting factor on the InSAR-derived tectonic deformation velocity. This is particularly the case when atmospheric turbulence, large deformation gradient and strong phase noise exist. To address limitations of existing phase unwrapping error correction methods which are not supported for multi-looked InSAR data, here we present a new algorithm that integrates decorrelation phase correction, triplet phase closure (TPC) test and integer linear programming (ILP) to overcome this limit. The rationale behind is that we mitigate the phase inconsistence using decorrelation correction and then detect the phase unwrapping error magnitude using TPC. Next we borrow the ILP from Compressed Sensing that converts the phase unwrapping error correction to a sparse signal recovery problem. We demonstrated the validity of our method by using synthetic data and 5-years Sentinel-1 real data covering the Central San Andreas Fault creep section, where exists obvious tectonic deformation, strong atmospheric disturbance and decorrelated scatterers, and the inverted long-term creep model constrained by InSAR velocity after correction shows a lower uncertainty than that constrained by the uncorrected one.