Combination of dispersion curves from MASW measurements

Olafsdottir, Elin Asta; Bessason, Bjarni; Erlingsson, Sigurður

doi:10.1016/j.soildyn.2018.05.025

Cited by 29 publications

(15 citation statements)

References 37 publications

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“…The experimental dispersion curves obtained by using different source offset lengths were in good agreement, as indicated by a coefficient of variation below 5% at each frequency (Figure 18f). In line with previous findings [49,60], the lowest frequency components displayed more variability than components in the higher frequency range. Analysis of shots applied at both ends of the receiver The dispersion analysis tool of MASWaves [38] was used for processing the acquired time series and identifying the fundamental mode of Rayleigh wave propagation.…”

Section: Field Data Inversionsupporting

confidence: 93%

“…The manual aspect of the dispersion curve identification may further introduce additional uncertainties to the dispersion curve estimates. Nevertheless, it is possible to provide a measure of the error associated with the experimental data by collecting repeated shots and evaluating the statistical distributions of the extracted phase velocity values at each frequency or wavelength [49]. In general, the longer wavelength (lower frequency) part of the dispersion curve has been found to be characterized by higher uncertainty than its shorter wavelength (higher frequency) range [60,61].…”

Section: Synthetic Data Inversionmentioning

confidence: 99%

“…Finally, a dataset, acquired at a well-characterized geo-test site, was analyzed to verify the robustness of the proposed strategy. As recommended by Olafsdottir et al [49], the dataset includes a statistical sample of dispersion curves which makes it possible to quantify the variability in estimated phase velocity values. After conducting the Monte Carlo-based search, using different layering parameterizations, inference is drawn from the set of simulated V S -profiles based on the observed spread in the experimental data.…”

Section: Introductionmentioning

confidence: 99%

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Open-Source MASW Inversion Tool Aimed at Shear Wave Velocity Profiling for Soil Site Explorations

2020

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The shear wave velocity profile is of primary interest for geological characterization of soil sites and elucidation of near-surface structures. Multichannel Analysis of Surface Waves (MASW) is a seismic exploration method for determination of near-surface shear wave velocity profiles by analyzing Rayleigh wave propagation over a wide range of wavelengths. The inverse problem faced during the application of MASW involves finding one or more layered soil models whose theoretical dispersion curves match the observed dispersion characteristics. A set of open-source MATLAB-based tools for acquiring and analyzing MASW field data, MASWaves, has been under development in recent years. In this paper, a new tool, using an efficient Monte Carlo search technique, is introduced to conduct the inversion analysis in order to provide the shear wave velocity profile. The performance and applicability of the inversion scheme is demonstrated with synthetic datasets and field data acquired at a well-characterized geotechnical research site.

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Section: Field Data Inversionsupporting

confidence: 93%

Section: Synthetic Data Inversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Open-Source MASW Inversion Tool Aimed at Shear Wave Velocity Profiling for Soil Site Explorations

2020

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“…Rayleigh waves are traditionally acquired by a multi‐channel vertical‐component linear array and are activated by a hammer vertically impacting on a fixture (Olafsdottir, Bessason and Erlingsson ). High‐resolution linear Radon transform (HLRT) is used to extract its dispersion curves (Luo et al .…”

Section: Introductionmentioning

confidence: 99%

Rayleigh‐wave dispersion analysis using complex‐vector seismic data

Qiu

Wang

2019

Near Surface Geophysics

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A B S T R A C TIdentification of different modes of Rayleigh waves is essential in surface-wave surveys. Multi-mode Rayleigh waves can provide higher accuracy of the near-surface structure than the fundamental mode alone. However, some modes or frequencies of Rayleigh waves may be absent in the vertical-component seismic data. To complement the dispersion information, a method based on complex-vector seismic data is proposed. We construct the complex vector by setting the radial component and vertical component as the real part and imaginary part, respectively. Then, high-resolution linear Radon transform is used to obtain the multi-mode Rayleighwave dispersion image of the complex-vector seismic data. Based on different dispersion characteristics of the radial and vertical components, the dispersion images of the complex-vector seismic data show better performance against interferences and mode misidentification. Synthetic and field examples demonstrate advantages of the complex-vector method over the traditional vertical-component method in spectral bands and dispersion curve mode identification. Therefore, a more robust and accurate near-surface S-wave velocity structure can be expected compared to the traditional vertical-component Rayleigh-wave method.

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“…Перспективными направлениями развития метода MASW является повышение точности и помехоустойчивости определения фазовых скоростей поверхностных волн. К таким исследованиям относятся полевые работы по выявлению оптимальных параметров системы наблюдений [8,9], а также разработка алгоритма спектрального анализа на основе временно-частотного представления сейсмограмм с использованием SFK-преобразования [10]. Применение SFK-преобразования обеспечивает автоматическое извлечение плавных и более точных по сравнению с полученными при помощи стандартных подходов дисперсионных кривых [11].…”

Section: Introductionunclassified

Solving of the Inversion Problem of the Method Multichannel Analysis of Surface Waves Based on the Artificial Neural Network

Yablokov

Loginov

Serdyukov

et al. 2019

Interexpo GEO-Siberia

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The study is devoted to improving the efficiency of the method of multichannel analysis of surface waves due to the implementation of a new algorithm for the selection of 1D-models of the shear wave velocity by inverse dispersion curves based on machine learning. It is proposed to use a multilayer fully-connected artificial neural network (ANN) for the estimate of horizontally layered models composed of interpolated velocity values on a uniform grid with an arbitrary step. Various types of correlation dependence of the parameters of the velocity model on dispersion curves were investigated. The optimal type of the objective function was determined for ANN learning on the basis of these studies. The advantages of using a trained ANN for the problem of inversion dispersion curves are: the ability to estimate layered models with sufficient accuracy for their use at subsequent stages of seismic data processing, resistance to noise, no significant requirements for computational resources and the absence of the need for additional adjustment of parameters. The tests of the trained ANN, with the architecture proposed in this study, show high accuracy in validation (about 96%), which indicates the success of training and the actually perspective of using neural networks for inversion problems.

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Combination of dispersion curves from MASW measurements

Cited by 29 publications

References 37 publications

Open-Source MASW Inversion Tool Aimed at Shear Wave Velocity Profiling for Soil Site Explorations

Open-Source MASW Inversion Tool Aimed at Shear Wave Velocity Profiling for Soil Site Explorations

Rayleigh‐wave dispersion analysis using complex‐vector seismic data

Solving of the Inversion Problem of the Method Multichannel Analysis of Surface Waves Based on the Artificial Neural Network

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