scholarly journals A Feasibility Study on Monitoring Crustal Structure Variations by Direct Comparison of Surface Wave Dispersion Curves from Ambient Seismic Noise

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
K. Muhumuza
Keyword(s):  
Surface Wave ◽  
Rayleigh Wave ◽  
Crustal Structure ◽  
Upper Mantle ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Wave Group ◽  
Surface Wave Dispersion

This work assesses the feasibility of the direct use of surface-wave dispersion curves from seismic ambient noise to gain insight into the crustal structure of Bransfield Strait and detect seasonal seismic velocity changes. We cross-correlated four years of vertical component ambient noise data recorded by a seismic array in West Antarctica. To estimate fundamental mode Rayleigh wave Green’s functions, the correlations are computed in 4-hr segments, stacked over 1-year time windows and moving windows of 3 months. Rayleigh wave group dispersion curves are then measured on two spectral bands—primary (10–30 s) and secondary (5–10 s) microseisms—using frequency-time analysis. We analyze the temporal evolution of seismic velocity by comparing dispersion curves for the successive annual and 3-month correlation stacks. Our main assumption was that the Green’s functions from the cross-correlations, and thus the dispersion curves, remain invariant if the crustal structure remains unchanged. Maximum amplitudes of secondary microseisms were observed during local winter when the Southern Ocean experiences winter storms. The Rayleigh wave group velocity ranges between 2.1 and 3.7 km/s, considering our period range studied. Interannual velocity variations are not much evident. We observe a slight velocity decrease in summer and increase in winter, which could be attributed to the pressure melting of ice and an increase in ice mass, respectively. The velocity anomalies observed within the crust and upper mantle structure correlate with the major crustal and upper mantle features known from previous studies in the area. Our results demonstrate that the direct comparison of surface wave dispersion curves extracted from ambient noise might be a useful tool in monitoring crustal structure variations.

scholarly journals Geodynamic tomography: constraining upper-mantle deformation patterns from Bayesian inversion of surface waves

2020 ◽  
Vol 224 (3) ◽  
pp. 2077-2099
Author(s):  
J K Magali ◽  
T Bodin ◽  
N Hedjazian ◽  
H Samuel ◽  
S Atkins
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Upper Mantle ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Bayesian Inversion ◽  
Model Parameters ◽  
Mantle Flow ◽  
Surface Wave Dispersion ◽  
Elastic Tensor

SUMMARY In the Earth’s upper mantle, seismic anisotropy mainly originates from the crystallographic preferred orientation (CPO) of olivine due to mantle deformation. Large-scale observation of anisotropy in surface wave tomography models provides unique constraints on present-day mantle flow. However, surface waves are not sensitive to the 21 coefficients of the elastic tensor, and therefore the complete anisotropic tensor cannot be resolved independently at every location. This large number of parameters may be reduced by imposing spatial smoothness and symmetry constraints to the elastic tensor. In this work, we propose to regularize the tomographic problem by using constraints from geodynamic modelling to reduce the number of model parameters. Instead of inverting for seismic velocities, we parametrize our inverse problem directly in terms of physical quantities governing mantle flow: a temperature field, and a temperature-dependent viscosity. The forward problem consists of three steps: (1) calculation of mantle flow induced by thermal anomalies, (2) calculation of the induced CPO and elastic properties using a micromechanical model, and (3) computation of azimuthally varying surface wave dispersion curves. We demonstrate how a fully nonlinear Bayesian inversion of surface wave dispersion curves can retrieve the temperature and viscosity fields, without having to explicitly parametrize the elastic tensor. Here, we consider simple flow models generated by spherical temperature anomalies. The results show that incorporating geodynamic constraints in surface wave inversion help to retrieve patterns of mantle deformation. The solution to our inversion problem is an ensemble of models (i.e. thermal structures) representing a posterior probability, therefore providing uncertainties for each model parameter.

scholarly journals A MATLAB Package for Calculating Partial Derivatives of Surface-Wave Dispersion Curves by a Reduced Delta Matrix Method

2019 ◽  
Vol 9 (23) ◽  
pp. 5214 ◽  
Author(s):  
Wu ◽  
Wang ◽  
Su ◽  
Zhang
Keyword(s):  
Surface Wave ◽  
Group Velocity ◽  
Love Wave ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Wave Group ◽  
Surface Wave Dispersion ◽  
Partial Derivatives ◽  
Matlab Package ◽  
Derivatives Of

Various surface-wave exploration methods have become increasingly important tools in investigating the properties of subsurface structures. Inversion of the experimental dispersion curves is generally an indispensable component of these methods. Accurate and reliable calculation of partial derivatives of surface-wave dispersion curves with respect to parameters of subsurface layers is critical to the success of these approaches if the linearized inversion strategies are adopted. Here we present an open-source MATLAB package, named SWPD (Surface Wave Partial Derivative), for modeling surface-wave (both Rayleigh- and Love-wave) dispersion curves (both phase and group velocity) and particularly for computing their partial derivatives with high precision. The package is able to compute partial derivatives of phase velocity and of Love-wave group velocity analytically based on the combined use of the reduced delta matrix theory and the implicit function theorem. For partial derivatives of Rayleigh-wave group velocity, a hemi-analytical method is presented, which analytically calculates all the first-order partial differentiations and approximates the mixed second-order partial differentiation term with a central difference scheme. We provide examples to demonstrate the effectiveness of this package, and demo scripts are also provided for users to reproduce all results of this paper and thus to become familiar with the package as quickly as possible.

scholarly journals Geodynamic Tomography: Constraining Upper-Mantle Deformation Patterns from Bayesian Inversion of Surface Waves

2021 ◽  
Author(s):  
John Keith Magali ◽  
Thomas Bodin ◽  
Navid Hedjazian ◽  
Henri Samuel ◽  
Suzanne Atkins
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Upper Mantle ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Bayesian Inversion ◽  
Model Parameters ◽  
Mantle Flow ◽  
Surface Wave Dispersion ◽  
Elastic Tensor

<p>In the Earth’s upper mantle, seismic anisotropy mainly originates from the crystallographic preferred orientation (CPO) of olivine due to mantle deformation. Large-scale observation of anisotropy in surface wave tomography models provides unique constraints on present-day mantle flow. However, surface waves are not sensitive to the 21 coefficients of the elastic tensor, and therefore the complete anisotropic tensor cannot be resolved independently at every location. This large number of parameters may be reduced by imposing spatial smoothness and symmetry constraints to the elastic tensor. In this work, we propose to regularize the tomographic problem by using constraints from geodynamic modeling to reduce the number of model parameters. Instead of inverting for seismic velocities, we parametrize our inverse problem directly in terms of physical quantities governing mantle flow: a temperature field, and a temperature-dependent viscosity. The forward problem consists of three steps: (1) calculation of mantle flow induced by thermal anomalies, (2) calculation of the induced CPO and elastic properties using a micromechanical model, and (3) computation of azimuthally varying surface wave dispersion curves. We demonstrate how a fully nonlinear Bayesian inversion of surface wave dispersion curves can retrieve the temperature and viscosity fields, without having to explicitly parametrize the elastic tensor. Here, we consider simple flow models generated by spherical temperature anomalies. The results show that incorporating geodynamic constraints in surface wave inversion help to retrieve patterns of mantle deformation. The solution to our inversion problem is an ensemble of models (i.e. thermal structures) representing a posterior probability, therefore providing uncertainties for each model parameter.</p>

scholarly journals Crustal structure and surface-wave dispersion, Part III: Theoretical dispersion curves for suboceanic Rayleigh waves*

1953 ◽  
Vol 43 (2) ◽  
pp. 137-144
Author(s):  
W. S. Jardetzky ◽  
Frank Press
Keyword(s):  
Surface Wave ◽  
Crustal Structure ◽  
Rayleigh Waves ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Different Types ◽  
Oceanic Basement ◽  
Rayleigh Wave Dispersion ◽  
Dispersion Part

Abstract Theoretical Rayleigh wave dispersion curves for three different types of sub-oceanic basement layering are presented. Previous conclusions concerning the dispersion of Rayleigh waves across ocean basins are re-examined in the light of the new data.

scholarly journals Crustal structure of south-central Mexico estimated from the inversion of surface-wave dispersion curves using genetic and simulated annealing algorithms

2001 ◽  
Vol 40 (3) ◽  
pp. 181-190
Author(s):  
A. Iglesias ◽  
V. M. Cruz-Atienza ◽  
N. M. Shapiro ◽  
S. K. Singh ◽  
J. F. Pacheco
Keyword(s):  
Simulated Annealing ◽  
Surface Wave ◽  
Crustal Structure ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Central Mexico ◽  
Surface Wave Dispersion ◽  
South Central ◽  
Annealing Algorithms ◽  
Sobre Todo

A partir de catorce sismos de subducción, agrupados en dos trayectorias (una perpendicular y otra paralela a la línea de costa), se calculó un apilado sobre las curvas de dispersión de velocidad de grupo. Estas curvas promedio fueron invertidas usando, por separado, los métodos de algoritmos genéticos y recristalización simulada. Los resultados muestran fuertes diferencias entre ambos modelos corticales, sobre todo, en los parámetros de la capa más somera y en la localización del Moho. Estas diferencias pueden ser explicadas debido a que la primera trayectoria atraviesa el terreno tectonoestratigráfico "Guerrero" y la segunda el "Oaxaca". La inversión con algoritmos genéticos (GA) probó ser considerablemente más rápida que aquélla con recristalización simulada (SA). Por otro lado SA requiere una pequeña cantidad de memoria y alcanza un desajuste menor que G.A.

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