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

Abstract

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.

Delayed Poroelastic Triggering of the 2016 October Visso Earthquake by the August Amatrice Earthquake, Italy

“…We calculate the postseismic transients of Δ p and ΔCFS at the VE hypocenter under a fully coupled poroelastic scheme, which is governed by a volumetric strain equation derived from mass conservation and Darcy's law (Wang, ): where α is the Biot‐Willis coefficient; t indicates the elapsed time since the loading event (i.e., the AE); ϵ kk = ∂ u k /∂ x k is the volumetric strain and subscript k cycles through the orthogonal axis 1, 2, and 3; S ϵ is the constrained storage coefficient; and k is the intrinsic rock permeability; μ f is the pore fluid viscosity (Table S1 in the supporting information). It is also constrained by a force equilibrium equation (Detournay & Cheng, ; Tung & Masterlark, ; Wang & Kümpel, ): where G and v respectively denote the shear modulus and Poisson's ratio which are determined by a local velocity model (Cirella et al, ). The Cartesian coordinates and corresponding displacements are denoted as x and u , respectively.…”

“…where α is the Biot-Willis coefficient; t indicates the elapsed time since the loading event (i.e., the AE); ϵ kk = ∂u k /∂x k is the volumetric strain and subscript k cycles through the orthogonal axis 1, 2, and 3; S ϵ is the constrained storage coefficient; and k is the intrinsic rock permeability; μ f is the pore fluid viscosity ( Table S1 in the supporting information). It is also constrained by a force equilibrium equation (Detournay & Cheng, 1993;Tung & Masterlark, 2016;Wang & Kümpel, 2003):…”

Rapid Geodetic Analysis of Subduction Zone Earthquakes Leveraging a 3‐D Elastic Green's Function Library

“…Finite element models (FEMs) are well suited to simulate both of the above tectonic complexities (Hughes et al, ; Kyriakopoulos et al, ; Figure ) and assimilate existing crustal information such as CRUST2.0 that cannot be well accommodated by rectangular fault patches in an analytical half‐space (Okada, ). The advantages of using FEMs over customary half‐space solutions have been exemplified in recent earthquakes and volcanic eruptions (Hughes et al, ; Kyriakopoulos et al, ; Masterlark, ; Masterlark et al, ; Masterlark et al, ; Masterlark & Hughes, ; Trasatti et al, ; Tung & Masterlark, , , ; Williams & Wallace, ), contributing to significantly more accurate source information. Yet, the lengthy processes of building FEMs and the corresponding Green's function (GF) matrix are the bottlenecks of real‐time analyses (c.f.…”

Sensitivities of Near‐field Tsunami Forecasts to Megathrust Deformation Predictions

“…While such analytical solutions are computationally efficient, they overly simplify the mechanically complex structure of upper crust along plate margins (Bjarnason et al, ; Gutscher et al, )—a problem which translates to prediction errors for the seafloor deformation that drives tsunami genesis (Wei et al, ). A wide range of studies have already revealed the influence of subsurface rock heterogeneity in deriving coseismic rupture characteristics (Eleonora et al, ; Fernandez et al, ; He & Peltzer, ; He et al, ; Hughes et al, ; Jovanovich et al, ; Kyriakopoulos et al, ; Masterlark et al, ; Masterlark et al, ; Pan, ; Sato, ; Savage, , ; Trasatti et al, ; Tung & Masterlark, , ; Wang et al, ), when inverting the earthquake deformation recorded by GPS and interferometric synthetic aperture radar data. Therefore, we propose to investigate whether the distributed rock properties in deformational models might become significant enough to alter the predictions of seafloor deformation and subsequent tsunami behavior.…”