Complete Three‐Dimensional Coseismic Deformation Field of the 2016 Central Tottori Earthquake by Integrating Left‐ and Right‐Looking InSAR Observations With the Improved SM‐VCE Method

Abstract

The three-dimensional (3-D) deformation field associated with the 2016 Central Tottori earthquake is retrieved from advanced land observing satellite 2 interferometric synthetic aperture radar (InSAR) observations with four different viewing geometries, that is, ascending/descending tracks and left-/ right-looking modes. The strain model and variance component estimation (SM, VCE, SM-VCE) method is exploited and improved to integrate the InSAR observations with different viewing geometries so that the 3-D deformation field is not affected by the inconsistent coverage of SAR footprints or the gross errors in InSAR observations. The obtained results are consistent with GNSS observations, indicating that the improved SM-VCE method, known as SM-RVCE in this paper, is capable of retrieving an accurate and spatially complete 3-D deformation field for this earthquake. In addition, the precision of the InSAR observations and the estimated 3-D deformation are quantitatively assessed by the SM-RVCE method. Finally, on the basis of the estimated 3-D coseismic deformation, the source parameters of this event are inverted, revealing an asperity with a maximum strike-parallel slip of~1.1 m concentrated at depths between 2 and 10 km. The estimated seismic moment is 2.4 × 10 18 Nm, which corresponds to a Mw 6.2 event.

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

“…T × t j ; Equation (7) can be written as: (8) where k j , v j , a j , ∆a j , s j , c j are the unknown deformation model parameters;…”

“…In recent decades, the interferometric synthetic aperture radar (SAR, InSAR) technique has been greatly developed and widely used to serve geohazard monitoring processes, such as those for mining subsidence [1][2][3], landslides [4][5][6], earthquakes [7][8][9], and volcano eruptions [10][11][12]. Especially when integrating multi-temporal SAR images with advanced time series InSAR (TS-InSAR) methods (e.g., persistent scatter (PS), small baseline subset (SBAS), and mixed PS/SBAS methods) [13][14][15][16][17], the inherent errors in a single interferogram (e.g., decorrelation noise and atmospheric delay) can be effectively mitigated, and simultaneously, the deformation time series of the study area can be obtained, which is of great significance for understanding the evolution process and mechanism of geohazards.…”

Mapping Complete Three-Dimensional Ice Velocities by Integrating Multi-Baseline and Multi-Aperture InSAR Measurements: A Case Study of the Grove Mountains Area, East Antarctic

“…Similar to [29,30], we determine the value of n by obtaining a tradeoff between the accuracies of the 3D deformation estimations and the burden of the computation from a series of simulated experiments with a window size ranging from 3 × 3 to 25 × 25. In this study, n = 225 was employed in the experiments.…”

“…In this study, we estimated the complete 3D ice velocities that are not prone to DEM impact for the Grove Mountains area by integrating available multibaseline and multiaperture InSAR measurements acquired on ascending and descending orbits. In the integration, a recently proposed method, termed strain model and variance component estimation (SM-VCE) [29,30], was introduced to provide the accurate weights of multisource InSAR measurements.…”

Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects

“…[37], demonstrating a 1.7 mm PWV interpolation accuracy across both flat and mountainous terrain, and in both summer and winter. The ITD method has been used extensively for computing tropospheric corrections for Interferometric Synthetic Aperture Radar (InSAR) studies, including from GPS stations [38] and from numerical weather models [39], and used, for example, for improving measurements of volcano deformation [40] and co-seismic deformation [41]. The ITD method is based on the assumption that PWV comprises a stratified (topography/elevation-dependent) component, and a turbulent component, representing topography-independent PWV signals (below equation) PWVi=Sfalse(Hifalse)+Tfalse(xifalse)+εi where Sfalse(Hifalse)=L0e−βHi and represents the stratified component at position vector boldxi; i is the location considered; Hi is the height above sea level; L 0 is the stratified PWV at sea level; β is an exponential coefficient; T is the turbulent PWV component; εi is the residual PWV.…”