Three-dimensional surface displacement field associated with the 25 April 2015 Gorkha, Nepal earthquake is derived from an integration of Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) measurements, with an extended SISTEM (Simultaneous and Integrated Strain Tensor Estimation From Geodetic and Satellite Deformation Measurements) approach (ESISTEM) proposed in this study. In ESISTEM approach, both surrounding InSAR and GPS measurements can be used as constraints in deriving surface displacements; while only surrounding GPS measurements are used in SISTEM approach. Besides the constraints from surrounding GPS measurements, the ESISTEM approach makes surrounding InSAR measurements available for constraining the derived deformations based on surface elastic theory for the first time. From the north to the south, derived surface displacement field shows prevailing southward horizontal deformations, and gradually varied vertical deformations ranging from −0.95 to 1.40 m within 120 km to the north of Kathmandu. This reveals that ruptures of Main Himalayan thrust (MHT) system were confined in subsurface and did not propagate to the Main Frontal Thrust (MFT) fault, in accordance with field investigation as well as geodetic and seismic studies. Relation between vertical deformations and earthquake-induced landslides is briefly discussed.

SUMMARY: The Bayesian slip inversion offers a powerful tool for modelling the earthquake source mechanism. It can provide a fully probabilistic result and thus permits us to quantitatively assess the inversion uncertainty. The Bayesian problem is usually solved with Monte Carlo methods, but they are computationally expensive and are inapplicable for high-dimensional and large-scale problems. Variational inference is an alternative solver to the Bayesian problem. It turns Bayesian inference into an optimization task and thus enjoys better computational performances. In this study, we introduce a general variational inference algorithm, automatic differentiation variational inference (ADVI), to the Bayesian slip inversion and compare it with the classic Metropolis–Hastings (MH) sampling method. The synthetic test shows that the two methods generate nearly identical mean slip distributions and standard deviation maps. In the real case study, the two methods produce highly consistent mean slip distributions, but the ADVI-derived standard deviation map differs from that produced by the MH method, possibly because of the limitation of the Gaussian approximation in the ADVI method. In both cases, ADVI can give comparable results to the MH method but with a significantly lower computational cost. Our results show that ADVI is a promising and competitive method for the Bayesian slip inversion.

Owing to the rapid development of unmanned aerial vehicle (UAV) technology and various photogrammetric software, UAV photogrammetry projects are becoming increasingly automated. However, marking ground control points (GCPs) in current UAV surveys still generally needs to be manually completed, which brings the problem of inefficiency and human error. Based on the characteristics of UAV photogrammetry, a novel type of circular coded target with its identification and decoding algorithm is proposed to realize an automatic and accurate approach for marking GCPs. UAV survey experiments validate the feasibility of the proposed method, which has comparative advantages in efficiency, robustness, and accuracy over traditional targets. Additionally, we conducted experiments to discuss the effects of projection size and viewing angle, number of coded bits, and environmental conditions on the proposed method. The results show that it can achieve robust identification and accurate positioning even under challenging conditions, and a smaller number of coded bits is recommended for better robustness.

The 2016 Kumamoto earthquake, including two large (Mw ≥ 6.0) foreshocks and an Mw 7.0 mainshock, occurred in the Hinagu and Futagawa fault zones in the middle of Kyushu island, Japan. Here, we obtain the complex coseismic deformation field associated with this earthquake from Advanced Land Observation Satellite-2 (ALOS-2) and Sentinel-1A Interferometric Synthetic Aperture Radar (InSAR) data. These InSAR data, in combination with available Global Positioning System (GPS) data, are then used to determine an optimal four-segment fault geometry with the jRi method, which considers both data misfit and the perturbation error from data noise. Our preferred slip distribution model indicates that the rupture is dominated by right-lateral strike-slip, with a significant normal slip component. The largest asperity is located on the northern segment of the Futagawa fault, with a maximum slip of 5.6 m at a 5–6 km depth. The estimated shallow slips along the Futagawa fault and northern Hinagu fault are consistent with the displacements of surface ruptures from the field investigation, suggesting a shallow slip deficit. The total geodetic moment release is estimated to be 4.89 × 10¹⁹ Nm (Mw 7.09), which is slightly larger than seismological estimates. The calculated static Coulomb stress changes induced by the preferred slip distribution model cannot completely explain the spatial distribution of aftershocks. Sensitivity analysis of Coulomb stress change implies that aftershocks in the stress shadow area may be driven by aseismic creep or triggered by dynamic stress transfer, requiring further investigation.