Dongzhen Wang

Since the late Cenozoic, the reactivated Tianshan orogenic belt has accommodated crustal shortening exceeding 200 km, primarily due to the far-field effects of the India-Eurasia plate collision. However, the details of the strain partitioning in the Tianshan Mountain range remain elusive. We interpret a new compilation of GPS velocities covering the whole Tianshan range with a classic elastic block model. Compared to previous studies with a block modeling approach, the Tianshan orogenic belt is further subdivided into several blocks based on geological fault traces and a clustering analysis approach. In addition to obvious crustal shortening on the bounding thrust faults of the Tianshan, our inverted fault slip rates also reveal that faults within the Tianshan orogenic belt, such as the Nalati Fault and the southern margin of the Issyk-Kul Lake Fault, which plays a crucial role in accommodating the tectonic crustal shortening. In the 72°E−78°E region, the internal shortening rate within the mountain is approximately 5–7 mm/yr. Besides crustal shortening, strike-slip motion occurs on faults in the interior of the mountain range as well as in the foreland fold-and-thrust belts, especially in the southern margin of the Tianshan. These findings suggest that the crustal deformation in the Tianshan Mountain range is more complex than previously thought, and the oblique convergence between the Tarim Basin and the Tianshan probably results in both strike-slip and thrust motion.

The frictional properties and slip behaviors of subduction thrusts play a key role in seismic and tsunami hazard assessment, especially in weakly coupled “seismic gaps”. Here, we rely on GPS observations in the Shumagin Gap of the Aleutian subduction zone to derive the slip distribution of the 2020 Mw 7.8 Simeonof Island, Alaska earthquake and of the subsequent afterslip during the first 87-day period. Our modeling results show that the mainshock ruptured at depths of ∼30–40 km beneath Simeonof Island. Kinematic and stress-driven models indicate that the afterslip occurred both updip and downdip of the mainshock rupture. Physically plausible locking models derived from interseismic GPS velocities suggest that the 2020 Simeonof and 2021 Mw 8.2 Chignik earthquakes ruptured persistent asperities on the subduction thrust. We infer that there are several additional persistent asperities at depths of 20–50 km west ∼157°W. However, it is still uncertain whether there are additional locked asperities at shallow depths because of the current lack of geodetic observations close to the trench.

Monitoring the surface deformation is of great significance, in order to explore the activity and geophysical features of the underground deep pressure source in the volcanic regions. In this study, the time series surface deformation of the Changbaishan volcano is retrieved via Sentinel-1B SAR data, using the SBAS-InSAR method. The main results are as follows. (1) The mean surface deformation velocity in the Changbaishan volcano is uplifted as a whole, while the uplift is locally distributed, which shows a strong correlation with faults. (2) The time series surface deformation of the Changbaishan volcano indicates an apparently seasonal change. (3) The cumulative surface deformation shows a strong correlation with the maximal magnitude and number of annual earthquakes, and it is likely dominated by the maximal magnitude of the annual earthquakes. (4) The single Mogi source model is appropriate to evaluate the deep pressure source in the Changbaishan volcano, constrained by the calculated surface deformation. The optimal estimated depth of the magma chamber is about 6.2 km, and the volume is increased by about 3.2 × 10⁶ m³. According to the time series surface deformation, it is concluded that the tectonic activity and faults, related to the deep pressure source, are pretty active in the Changbaishan volcano.

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.