Comparison among the inversion results of surface wave dispersion curves by the artificial neural networks and local and global search methods

2020 ◽  
Author(s):  
Alexandr V. Yablokov ◽  
◽  
Aleksander S. Serdyukov ◽  
Georgy N. Loginov ◽  
◽  
...  
Keyword(s):  
Surface Wave ◽  
Synthetic Data ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Global Search ◽  
Training Data ◽  
Surface Wave Dispersion ◽  
Search Methods ◽  
Data Set ◽  
Artificial Neural

We propose a new method for the inversion of surface wave dispersion curves based on the application of an artificial neural network and we suggest a data–driven approach for selecting the range of the space parameters for the calculating training data set. The synthetic data processing results showed that the accuracy of the proposed method is superior local search and equivalent to global search methods, whereas the proposed method is more robust in the presence of noise.

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

A model study of elastic waves in a layered sphere

1967 ◽  
Vol 57 (5) ◽  
pp. 959-981
Author(s):  
Victor Gregson
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Elastic Waves ◽  
Wave Dispersion ◽  
Flat Space ◽  
Dispersion Curves ◽  
Body Waves ◽  
Surface Wave Dispersion ◽  
Steel Sphere ◽  
Long Wave

abstract Elastic waves produced by an impact were recorded at the surface of a solid 12.0 inch diameter steel sphere coated with a 0.3 inch copper layer. Conventional modeling techniques employing both compressional and shear piezoelectric transducers were used to record elastic waves for one millisecond at various points around the great circle of the sphere. Body, PL, and surface waves were observed. Density, layer thickness, compressional and shear-wave velocities were measured so that accurate surface-wave dispersion curves could be computed. Surface-wave dispersion was measured as well as computed. Measured PL mode dispersion compared favorably with theoretical computations. In addition, dispersion curves for Rayleigh, Stoneley, and Love modes were computed. Measured surface-wave dispersion showed Rayleigh and Love modes were observed but not Stoneley modes. Measured dispersion compared favorably with theoretical computations. The curvature correction applied to dispersion calculations in a flat space has been estimated to correct dispersion values at long-wave lengths to about one per cent of correct dispersion in a spherical model. Measured dispersion compared with such flat space dispersion corrected for curvature proved accurate within one per cent at long wave lengths. Two sets of surface waves were observed. One set was associated with body waves radiating outward from impact. The other set was associated with body waves reflecting at the pole opposite impact. For each set of surface waves, measured dispersion compared favorably with computed dispersion.

scholarly journals A fast, convenient program for computation of surface-wave dispersion curves in multilayered media

1961 ◽  
Vol 51 (4) ◽  
pp. 495-502
Author(s):  
Frank Press ◽  
David Harkrider ◽  
C. A. Seafeldt
Keyword(s):  
Surface Wave ◽  
Rayleigh Waves ◽  
High Speed ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Mail Order ◽  
Mantle Structure ◽  
Wave Analysis ◽  
Multilayered Media ◽  
Surface Wave Dispersion

Abstract Surface wave analysis has become an important tool for exploration of crustal and mantle structure. The need exists for fast, convenient digital computer programs for computing theoretical dispersion curves and displacements for Rayleigh waves and Love waves. One such program for an IBM 7090 computer is described and made available to the scientific community. Among the conveniences are mail-order service, high speed, and choice of many options.

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.

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