1,721,111 research outputs found
P- and S-wave velocity models from surface wave dispersion curves data transform
No full textThe inversion of surface wave dispersion curves is a well-established investigation approach to provide S-wave velocity models. Recent works have shown the possibility of exploiting the relation between surface wave wavelength and investigation depth to estimate directly the time-average S-wave velocity along a line or over an area. Moreover, the same relation is sensitive to Poisson's ratio and can be therefore used to retrieve also the time-average P-wave velocity model. Here we show that, starting from the estimated time average VS and VP models and using a Dix-like formula, we can estimate local VS and VP models. We use a synthetic 1D example to outline the method and then we show the results on a field dataset
P- and S-wave velocity models of shallow dry sand formations from surface wave multimodal inversion
No full textSurficial formations composed of loose, dry granular materials constitute a challenging target for seismic characterization. They exhibit a peculiar seismic behavior, characterized by a nonlinear seismic velocity gradient with depth that follows a power-law relationship, which is a function of the effective stress. The P- and S-wave velocity profiles are then characterized by a power-law trend, and they can be defined by two power-law exponents αP;S and two power-law coefficients γP;S. In case of depth-independent Poisson’s ratio, the P-wave velocity profile can be defined using the VS power-law parameters and Poisson’s ratio. Because body wave investigation techniques (e.g., P-wave tomography) may perform ineffectively on such materials because of high attenuation, we addressed the potential of surface-wave method for a reliable seismic characterization of shallow formations of dry, uncompacted granular materials. We took into account the dependence of seismic wave velocity on effective pressure and performed a multimodal inversion of surface-wave data, which allowed the VS and VP profiles to be retrieved. The
method requires the selection of multimodal dispersion curve points referring to surface-wave frequency components traveling within the granular media formation and their inversion for the S-wave power-law parameters and Poisson’s ratio.We have tested our method on a synthetic dispersion curve and applied it to a real data set. In both cases, the surficial layer was made of loose dry sand. The test on the synthetic data set confirmed the reliability of the proposed procedure because the thickness and the VS, VP profiles of the sand layer were correctly estimated.
For the real data, the outcomes were validated by other geophysical measurements conducted at the same site and they were in agreement with similar studies regarding loose sand formations
Improved Monte Carlo inversion of surface wave data
No full textInversion of surface wave data suffers from solution non-uniqueness and is hence strongly biased by the initial model. The Monte Carlo approach can handle this nonuniqueness by evidencing the local minima but it is inefficient for high dimensionality problems and makes use of subjective criteria, such as misfit thresholds, to interpret the results. If a smart sampling of the model parameter space, which exploits scale properties of the modal curves, is introduced the method becomes more efficient and with respect to traditional global search methods it avoids the subjective use of control parameters that are barely related to the physical problem. The results are interpreted drawing inference by means of a statistical test that selects an ensemble of feasible shear wave velocity models according to data quality and model parameterization. Tests on synthetic data demonstrate that the application of scale properties concentrates the sampling of model parameter space in high probability density zones and makes it poorly sensitive to the initial boundary of the model parameters. Tests on synthetic and field data, where boreholes are available, prove that the statistical test selects final results that are consistent with the true model and which are sensitive to data quality. The implemented strategies make the Monte Carlo inversion efficient for practical applications and able to effectively retrieve subsoil models even in complex and challenging situations such as velocity inversion
P-and S-wave velocity model estimation from surface wave data
No full textSurface wave analysis performed on data acquired on purpose or extracted from seismic gathers acquired for body wave surveying is a powerful tool for S-wave velocity model estimation. S-wave velocity is relevant for many engineering applications, but the recent application for hydrocarbon exploration data processing (static corrections) makes the estimation of P-wave velocity a desired additional target of the method. Several approaches ranging from joint inversion to approximated direct estimation have been implemented in recent here and are here reviewed. These approaches, applied here on synthetic 1D examples have been also extended to 2D real world applications
Surface-wave method for near-surface characterisation: a tutorial
No full textSurface-wave methods (SWMs) are very powerful tools for the near-surface characterization of sites. They can be used to determine the shear-wave velocity and the damping ratio overcoming, in some cases, the limitations of other shallow seismic techniques. The different steps of SWM have to be optimized, taking into consideration the conditions imposed by the small scale of engineering problems. This only allows the acquisition of apparent dispersion characteristics: i.e. the high frequencies and short distances involved make robust modelling algorithms necessary in order to take modal superposition into account. The acquisition has to be properly planned to obtain quality data over an adequate frequency range. Processing and inversion should enable the interpretation of the apparent dispersion characteristics, i.e. evaluating the local quality of the data, filtering coherent noise due to other seismic events and determining energy distribution, higher modes and attenuation. The different approaches that are used to estimate and interpret the dispersion characteristics are considered. Their potential and limits with regard to sensitivity to noise, reliability and capability of extracting significant information present in surface waves are discussed. The theory and modelling algorithms, and the acquisition, processing and inversion procedures suitable for providing stiffness and damping ratio profiles are illustrated, with particular attention to reliability and resolutio
Physically constrained 2D joint inversion of surface and body wave tomography
Full text linkJoint inversion of different geophysical methods is a powerful tool to overcome the limitations of individual inversions. Body wave tomography is used to obtain P-wave velocity models by inversion of P-wave travel times. Surface wave tomography is used to obtain S-wave velocity models through inversion of the dispersion curves data. Both methods have inherent limitations. We focus on the joint body and surface waves tomography inversion to reduce the limitations of each individual inversion. In our joint inversion scheme, the Poisson ratio was used as the link between P-wave and S-wave velocities, and the same geometry was imposed on the final velocity models. The joint inversion algorithm was applied to a 2D synthetic dataset and then to two 2D field datasets. We Compare the obtained velocity models from individual inversions and the joint inversion. We show that the proposed joint inversion method not only produces superior velocity models, also generates physically more meaningful and accurate Poisson ratio models
Retrieving lateral variation from surface wave dispersion curve analysis
No full textSurface wave analysis is usually applied as a 1D tool to estimate VS profiles. Here we evaluate the potential of surface wave analysis for the case of lateral variations. Lateral variations can be characterized by exploiting the data redundancy of the ground roll contained in multifold seismic data. First, an automatic processing procedure is applied that allows stacking dispersion curves obtained from different records and which retrieves experimental uncertainties. This is carried out by sliding a window along a seismic line to obtain an ensemble of dispersion curves associated to a series of spatial coordinates. Then, a laterally constrained inversion algorithm is adopted to handle 2D effects, although a 1D model has been assumed for the forward problem solution. We have conducted different tests on three synthetic data sets to evaluate the effects of the processing parameters and of the constraints on the inversion results. The same procedure, applied to the synthetic data, was then tested on a field case. Both the synthetic and field data show that the proposed approach allows smooth lateral variations to be properly retrieved and that the introduction of lateral constraints improves the final result compared to individual inversion
Joint Inversion of P-waves refraction travel times and surface wave dispersion curves
No full textRayleigh wave dispersion curves and P refraction travel times are jointly inverted through a damped least square algorithm which accounts simultaneously for both datasets, solving for common thicknesses and respective VP and VS values. The velocities are coupled through the introduction of P-wave velocity values that are used for both the refraction and the surface wave forward modelling. Since the sensitivity of surface waves to P-wave velocity is low, the problem is strongly coupled on the thicknesses and weakly coupled on the velocities. The surface wave - P-wave refraction joint inversion algorithm is effective in solving hidden layer problem, which would lead to big interpretational errors in the case of individual inversion of P dromocrones. The approach is effective for inversion of 1D layered models as shown in one example for the inversion of experimental data, leading to better results than individual inversions also in the case of surface wave
Retrieving 2D structures from surface-wave data by meansof space-varying spatial windowing
No full textSurface-wave techniques are mainly used to retrieve 1D subsurface models. However, in 2D environments, the 1D approach usually neglects the presence of lateral variations and because the surface-wave path crosses different materials, the resulting model is a simplified or misleading description of the site. We tested a processing technique to retrieve 2D structures from surface-wave data acquired with a limited number of receivers. Our technique was based on a twostep process. First, we extracted several local dispersion curves along the survey line using a spatial windowing based on a set of Gaussian windows with different shapes; the window maxima span the survey line so that we were able to extract a dispersion curve from the seismic record for every window. This provided a set of local dispersion curves each of them referring to a different subsurface portion. This space varying spatial windowing provided a good compromise between wavenumber resolution and the lateral resolution of the obtained local dispersion curves. In the second step, we inverted the retrieved set of dispersion curves using a laterally constrained inversion scheme. We applied this procedure to the processing of synthetic and real data sets and the method proved to be successful in reconstructing even complex 2D structures in the subsurfac
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