scholarly journals A statistical assessment of seismic models of the U.S. continental crust using Bayesian inversion of ambient noise surface wave dispersion data

Tectonics ◽  
2017 ◽  
Vol 36 (7) ◽  
pp. 1232-1253 ◽  
Author(s):  
T. M. Olugboji ◽  
V. Lekic ◽  
W. McDonough
Keyword(s):  
Surface Wave ◽  
Continental Crust ◽  
Ambient Noise ◽  
Wave Dispersion ◽  
Bayesian Inversion ◽  
Surface Wave Dispersion ◽  
Statistical Assessment ◽  
Dispersion Data ◽  
Seismic Models ◽  
The U.S

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

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.

A gradient-based model parametrization using Bernstein polynomials in Bayesian inversion of surface wave dispersion

2017 ◽  
Vol 211 (1) ◽  
pp. 528-540 ◽  
Author(s):  
Jeremy M. Gosselin ◽  
Stan E. Dosso ◽  
John F. Cassidy ◽  
Jorge E. Quijano ◽  
Sheri Molnar ◽  
...  
Keyword(s):  
Surface Wave ◽  
Bernstein Polynomials ◽  
Wave Dispersion ◽  
Bayesian Inversion ◽  
Surface Wave Dispersion ◽  
Gradient Based

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

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 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.

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