With the development of interferometric synthetic aperture radar (InSAR), the seismic deformation observation density increases sharply. Data down-sampling can effectively reduce the observation density and the computational cost for subsequent researches. Considering the saliency of the deformation field, we introduce a saliency-based quadtree algorithm for down-sampling (SQS). Three simulation experiments show that SQS can effectively distinguish the near-field and far-field deformation, as well as reduce the amount of observation, while keeping the detailed information of the main deformation near the fault. SQS can avoid the interference of far-field local deformation better than the traditional quadtree sampling algorithm (QS), thus obtaining better inversion results. We took the Dingri earthquake on 20 March 2020 as a case study to verify the advantages of SQS in dealing with real earthquake deformation. We obtained the co-seismic deformation from the ascending and descending Sentinel-1 for the Dingri earthquake, using QS and SQS for sampling and inversion separately. The results show the advantages of SQS in data volume reduction, observation distribution, anti-interference of local deformation, and inversion accuracy. Our preferred solution based on SQS shows that the Dingri earthquake was caused by a normal fault slip. The main slip area is 2–5.5 km deep with a maximum slip of 0.68 m. The estimated geodetic moment is 3.14 × 1017 Nm, corresponding to a magnitude of Mw5.63.
DETECTION OF STICK-SLIP OCCURRENCES THROUGH NEAR FIELD NOISE MEASUREMENTS
