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

SURFACE‐WAVE DISPERSION APPLIED TO THE DETECTION OF SEDIMENTARY BASINS

Geophysics ◽  
1975 ◽  
Vol 40 (1) ◽  
pp. 40-55 ◽  
Author(s):  
Robert H. Tatham
Keyword(s):  
Surface Wave ◽  
Crustal Structure ◽  
Gulf Coast ◽  
Sedimentary Basins ◽  
Wave Dispersion ◽  
Petroleum Exploration ◽  
Underground Nuclear Explosion ◽  
Surface Wave Dispersion ◽  
Mississippi Embayment ◽  
Model Calculations

Seismic surface‐wave velocities are greatly affected by crustal structure. Because there is a strong contrast in the physical properties of clastic sediments and underlying basement materials, surface‐wave dispersion provides a fast, convenient, and inexpensive means of detecting sedimentary basins and estimating their thickness. Model calculations and published reports of explosion studies indicate that sedimentary thicknesses as shallow as 500 m (∼1650 ft) should be detectable by analysis of routinely recorded earthquake seismograms. This study demonstrates the use of seismic surface‐wave dispersion to detect sedimentary basins and to estimate their thickness. The technique is used first for the Mississippi embayment region of the U.S. Gulf Coast, where the crustal structure is known and the results can be verified, and then applied to offshore Greenland, where the crustal structure is unmapped but a sedimentary basin is suspected. The data used are available seismograms of natural earthquakes and, for the Gulf Coast area, an underground nuclear explosion. Because this technique requires only existing, readily available data and may be applied to many regions of the world, it offers an attractive reconnaissance tool in petroleum exploration. In the present study, surface‐wave dispersion and the effects of shallow crustal structure are reviewed in light of this application, and the advantages and limitations of the technique are explored.

scholarly journals CRUSTAL STRUCTURE AND SURFACE-WAVE DISPERSION

1955 ◽  
Vol 66 (7) ◽  
pp. 913 ◽  
Author(s):  
JACK E. OLIVER ◽  
MAURICE EWING ◽  
FRANK PRESS
Keyword(s):  
Surface Wave ◽  
Crustal Structure ◽  
Wave Dispersion ◽  
Surface Wave Dispersion

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.

Sign in / Sign up

Export Citation Format

Share Document