Mutually Constrained Inversion of Common Offset GPR Reflection and Surface Wave Dispersion Data in Layered Media

2019 ◽  
Author(s):  
Shufan Hu ◽  
Yonghui Zhao ◽  
Shuangcheng Ge
Keyword(s):  
Surface Wave ◽  
Layered Media ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Dispersion Data

scholarly journals Simultaneous Inversion of Multiwindow Surface Wave Dispersion Data in 2D Layered Media

2021 ◽  
Vol 660 (1) ◽  
pp. 012044
Author(s):  
Shufan Hu ◽  
Yonghui Zhao ◽  
Shuangcheng Ge ◽  
Wenda Bi ◽  
Ruiqing Shen
Keyword(s):  
Surface Wave ◽  
Layered Media ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Simultaneous Inversion ◽  
Dispersion Data

Short-period surface-wave dispersion and shallow crustal structure of central and eastern Tennessee

1992 ◽  
Vol 82 (2) ◽  
pp. 962-979
Author(s):  
Paul C. Yao ◽  
James Dorman
Keyword(s):  
Surface Wave ◽  
Half Space ◽  
Crustal Structure ◽  
Group Velocity Dispersion ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Zero Crossing ◽  
Clastic Rocks ◽  
Dispersion Data ◽  
Short Period

Abstract Group velocity dispersion of explosion-generated seismic surface waves with periods ranging from 0.2 to 1.5 sec is used to investigate shallow crustal structure of eastern and central Tennessee. Several modes of both Rayleigh and Love waves can be identified and separated on the seismograms of seven SARSN regional network stations by zero-phase digital filtering. Dispersion data for sinusoidal wave motion were based on digitized zero-crossing times. By forward modeling, we find that a wave guide of at least two layers over a half-space can adequately represent our particular multi-mode, narrow-band observations. In a layered section about 3 km thick, lower velocities characterize outcropping clastic rocks of the Cumberland plateau, and higher velocities correspond to shallow carbonate rocks of the Nashville Dome. Half-space shear velocities of about 3.9 km/sec appear to represent lower Paleozoic carbonate lithology deeper than 2 to 4 km on most of the observed paths. Our best data, interpreted jointly with earlier data of Oliver and Ewing (1958) and of Chen et al. (1989), have a composite period range of 0.2 to 40 sec, but they represent different Appalachian paths. Group velocities over this broad spectrum are satisfied by a complex model with two low-velocity layers. The uniqueness of this model cannot be demonstrated, but it represents important hypotheses concerning regional geologic features that can be tested more rigorously by improved surface-wave dispersion data.

Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints

2020 ◽  
Vol 221 (2) ◽  
pp. 938-950
Author(s):  
Pingping Wu ◽  
Handong Tan ◽  
Changhong Lin ◽  
Miao Peng ◽  
Huan Ma ◽  
...  
Keyword(s):  
Surface Wave ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Uniqueness Problem ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Surface Wave Dispersion ◽  
Gradient Constraints ◽  
Dispersion Data

SUMMARY Multiphysics imaging for data inversion is of growing importance in many branches of science and engineering. Cross-gradient constraint has been considered as a feasible way to reduce the non-uniqueness problem inherent in inversion process by finding geometrically consistent images from multigeophysical data. Based on OCCAM inversion algorithm, a direct inversion method of 2-D profile velocity structure with surface wave dispersion data is proposed. Then we jointly invert the profiles of magnetotelluric and surface wave dispersion data with cross-gradient constraints. Three synthetic models, including block homogeneous or heterogeneous models with consistent or inconsistent discontinuities in velocity and resistivity, are presented to gauge the performance of the joint inversion scheme. We find that owning to the complementary advantages of the two geophysical data sets, the models recovered with structure coupling constraints exhibit higher resolution in the classification of complex geologic units and settle some imaging problems caused by the separate inversion methods. Finally, a realistic velocity model from the NE Tibetan Plateau and its corresponding resistivity model calculated by empirical law are used to test the effectiveness of the joint inversion scheme in the real geological environment.

scholarly journals Oceanic lithosphere-asthenosphere boundary from surface wave dispersion data

2014 ◽  
Vol 119 (2) ◽  
pp. 1079-1093 ◽  
Author(s):  
G. Burgos ◽  
J.-P. Montagner ◽  
E. Beucler ◽  
Y. Capdeville ◽  
A. Mocquet ◽  
...  
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Oceanic Lithosphere ◽  
Surface Wave Dispersion ◽  
Dispersion Data

Bayesian Inversion of Receiver Functions and Surface Wave Dispersion Data in the Brazilian Northeast

2017 ◽  
Author(s):  
Thuany Patrícia Costa de Lima ◽  
Hrvoje Tkalčić ◽  
Seongryong Kim ◽  
Jordi Julià
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
Bayesian Inversion ◽  
Surface Wave Dispersion ◽  
Dispersion Data ◽  
Brazilian Northeast

scholarly journals Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

2020 ◽  
Vol 223 (3) ◽  
pp. 1741-1757
Author(s):  
S Earp ◽  
A Curtis ◽  
X Zhang ◽  
F Hansteen
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
North Sea ◽  
Shear Wave Velocity ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Dispersion Data

SUMMARY Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network (NN) based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of NNs called mixture density networks (MDNs), to invert dispersion curves for shear wave velocity models and their non-linearized uncertainty. MDNs are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties as additional inputs to the MDN gives substantially more reliable velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26 772 locations to produce a 3-D velocity model in 21 s on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3-D subsurface imaging and monitoring.

scholarly journals SWinvert: a workflow for performing rigorous 1-D surface wave inversions

2020 ◽  
Vol 224 (2) ◽  
pp. 1141-1156
Author(s):  
Joseph P Vantassel ◽  
Brady R Cox
Keyword(s):  
Surface Wave ◽  
Open Source ◽  
High Performance ◽  
Broad Band ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
University Of Texas ◽  
Wave Inversion ◽  
Dispersion Data ◽  
Band Limited

SUMMARY SWinvert is a workflow developed at The University of Texas at Austin for the inversion of surface wave dispersion data. SWinvert encourages analysts to investigate inversion uncertainty and non-uniqueness in shear wave velocity (Vs) by providing a systematic procedure and specific actionable recommendations for surface wave inversion. In particular, the workflow encourages the use of multiple layering parametrizations to address the inversion's non-uniqueness, multiple global searches for each parametrization to address the inverse problem's non-linearity and quantification of Vs uncertainty in the resulting profiles. While the workflow uses the Dinver module of the popular open-source Geopsy software as its inversion engine, the principles presented are of relevance to analysts using other inversion programs. To illustrate the effectiveness of the SWinvert workflow and to develop a set of benchmarks for use in future surface wave inversion studies, synthetic experimental dispersion data for 12 subsurface models of varying complexity are inverted. While the effects of inversion uncertainty and non-uniqueness are shown to be minimal for simple subsurface models characterized by broad-band dispersion data, these effects cannot be ignored in the Vs profiles derived for more complex models with band-limited dispersion data. To encourage adoption of the SWinvert workflow, an open-source Python package (SWprepost), for pre- and post-processing of surface wave inversion data, and an application on the DesignSafe-Cyberinfrastructure (SWbatch), for performing batch-style surface wave inversions with Dinver using high-performance computing, have been developed and released in conjunction with this work. The SWinvert workflow is shown to provide a methodical procedure and a powerful set of tools for performing rigorous surface wave inversions and quantifying the uncertainty in the resulting Vs profiles.

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