Methods of determining weight scaling factors for geodetic–geophysical joint inversion

Xu, Caijun; Ding, Kaihua; Cai, Jianqing; Grafarend, Erik W.

doi:10.1016/j.jog.2008.06.005

Cited by 57 publications

(27 citation statements)

References 25 publications

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“…First, we determined the optimal values for smoothness of the model and relative weighting between GPS and InSAR LOS data using the HVCE method as described by Xu et al [8]. In the HVCE method, the relative weight ratio is obtained according to the posterior misfit information from each dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, this region is the locus of other moderate-sized damaging earthquakes that struck Central Italy in modern times: the Mw 5. 8,1979 Norcia to the west, the Mw 6.0, 1997 Umbria-Marche earthquake sequence to the north, and the Mw 6.1, 2009 L'Aquila sequence to the south. The L'Aquila earthquake resulted in significant landslides in the local area, and was also followed by a vigorous aftershock sequence.…”

Section: Tectonic Backgroundmentioning

confidence: 97%

“…Although the interferogram empirical errors derived from the 1-D covariance function can be used to weight the different datasets directly, Xu et al [8] suggested that the HVCE method has various advantages, especially when the number of redundant observations is relatively large in a joint inversion. In this study, the HVCE method was employed to weight the different observations, and derive the coseismic slip distribution of the four main Central Italy earthquakes.…”

Section: Data Reduction and Weightingmentioning

confidence: 99%

“…The fault geometry and slip distribution were retrieved from a combination of nonlinear and linear inversions together with the Helmert variance component estimation (HVCE) weighting method [8]. The interaction between the earthquakes was also investigated through Coulomb failure stress analysis.…”

Section: Introductionmentioning

confidence: 99%

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Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data

Wen

et al. 2017

Remote Sensing

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Abstract:In this study, joint inversions of Synthetic Aperture Radar (SAR) and Global Position System (GPS) measurements are used to investigate the source parameters of four Mw > 5 events of the 2016-2017 Central Italy earthquake sequence. The results show that the four events are all associated with a normal fault striking northwest-southeast and dipping southwest. The observations, in all cases, are consistent with slip on a rupture plane, with strike in the range of 157 • to 164 • and dip in the range of 39 • to 44 • that penetrates the uppermost crust to a depth of 0 to 8 km.

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Section: Methodsmentioning

confidence: 99%

Section: Tectonic Backgroundmentioning

confidence: 97%

Section: Data Reduction and Weightingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data

Wen

et al. 2017

Remote Sensing

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“…After we construct the constraints, we use the Helmert variance component estimation [32,33] to determine the appropriate weights of the observations and constraints, and then estimate the tomography solution by Equation (9). According to Equations (4)- (8), we obtained five kinds of equations, so the equations for HVCE in this study can be written as: (4)- (8) are very few and not sufficient to determine all five weights, we chose to determine some weights before we ran HVCE.…”

Section: Helmert Variance Component Estimationmentioning

confidence: 99%

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

et al. 2017

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Using the Global Navigation Satellite System (GNSS) to sense three-dimensional water vapor (WV) has been intensively investigated. However, this technique still heavily relies on the a priori information. In this study, we propose an improved tomography approach based on adaptive Laplacian smoothing (ALS) and ground meteorological observations. By using the proposed approach, the troposphere tomography is less dependent on a priori information and the ALS constraints match better with the actual situation than the constant constraints. Tomography experiments in Hong Kong during a heavy rainy period and a rainless period show that the ALS method gets superior results compared with the constant Laplacian smoothing (CLS) method. By validation with radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) data, we found that the introduction of ground meteorological observations into tomography can solve the perennial problem of resolving the wet refractivity in the lower troposphere and thus significantly improve the tomography results. However, bad data quality and incompatibility of the ground meteorological observations may introduce errors into tomography results.

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Coseismic slip distribution of the 2015 M_w7.8 Gorkha, Nepal, earthquake from joint inversion of GPS and InSAR data for slip within a 3‐D heterogeneous Domain

Tung

Masterlark

2016

JGR Solid Earth

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We derive a coseismic slip model of the 2015 Mw7.8 Gorkha earthquake on the basis of GPS and line‐of‐sight displacements from ALOS‐2 descending interferograms, using Green's functions calculated with a 3‐D finite element model (FEM). The FEM simulates a nonuniform distribution of elastic material properties and a precise geometric configuration of the irregular topographical surface. The rupturing fault is modeled as a low‐angle and north dipping surface within the Main Frontal Thrust along the convergent margin of the Himalayas. The optimal model that inherits heterogeneous material properties provides a significantly better solution than that in a homogenous domain at the 95% confidence interval. The best fit solution for the domain having a nonuniform distribution of material properties reveals a rhombus‐shaped slip zone of three composite asperities. Slip is primarily concentrated at a depth of 15 km with both dip‐slip (maximum 6.54 m) and strike‐slip (maximum 2.0 m) components, giving rise to a geodetic‐based moment of 1.09 × 1021 Nm in general agreement with the seismological estimate. The optimal relative weights among GPS and interferometric synthetic aperture radar (InSAR) are deduced from a new method, MC‐HVCE which combines a Monte Carlo search and a Helmert Method of Variance Components Estimation. This method determines the relative weights in a systemic approach which preserves the intrinsic solution smoothness. The joint solution is significantly better than those inverted from each individual data set. This methodology allows us to integrate multiple data sets of geodetic observations with seismic tomography, in an effort to achieve a better understanding of seismic ruptures within crustal heterogeneity.

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Methods of determining weight scaling factors for geodetic–geophysical joint inversion

Cited by 57 publications

References 25 publications

Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data

Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

Coseismic slip distribution of the 2015 M_w7.8 Gorkha, Nepal, earthquake from joint inversion of GPS and InSAR data for slip within a 3‐D heterogeneous Domain

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Methods of determining weight scaling factors for geodetic–geophysical joint inversion

Cited by 57 publications

References 25 publications

Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data

Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data

An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations

Coseismic slip distribution of the 2015 Mw7.8 Gorkha, Nepal, earthquake from joint inversion of GPS and InSAR data for slip within a 3‐D heterogeneous Domain

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Coseismic slip distribution of the 2015 M_w7.8 Gorkha, Nepal, earthquake from joint inversion of GPS and InSAR data for slip within a 3‐D heterogeneous Domain