Two-Grid Genetic Algorithm Full Waveform Inversion of Surface Waves: Two Actual Data Examples

Xing, Zhen; Mazzotti, Alfredo

doi:10.3997/2214-4609.201801375

Cited by 5 publications

(3 citation statements)

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“…Although we could use different norms to define the objective function (Brossier et al 2010), here we use the L2-norm since it is the most widely-used objective function in both MASW and FWI. Although attempts have been made in using global optimization algorithms to solve the inverse problem in FWI (Zeng et al 2011a;Aleardi et al 2016;Xing and Mazzotti 2018), most of the FWI studies use gradient-based local optimization algorithms due to a huge number of parameters in m. The huge number of parameters also makes the direct numerical calculation of the Jacobian matrix (Fréchet derivative) computationally expensive. The gradient of the FWI misfit function with respect to model parameters, however, can be calculated efficiently using an adjoint state algorithm (Plessix 2006), in which only two wavefield simulations are required: One simulation of the forward-propagating wavefield (state variable), and one simulation of the back-propagating residual wavefield (adjoint-state variable).…”

Section: Classical Fwimentioning

confidence: 99%

High-Resolution Characterization of Near-Surface Structures by Surface-Wave Inversions: From Dispersion Curve to Full Waveform

2019

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Surface waves are widely used in near-surface geophysics and provide a non-invasive way to determine near-surface structures. By extracting and inverting dispersion curves to obtain local 1D S-wave velocity profiles, multichannel analysis of surface waves (MASW) has been proven as an efficient way to analyze shallow-seismic surface waves. By directly inverting the observed waveforms, full-waveform inversion (FWI) provides another feasible way to use surface waves in reconstructing near-surface structures. This paper provides a state of the art on MASW and shallow-seismic FWI, and a comparison of both methods. A two-parameter numerical test is performed to analyze the nonlinearity of MASW and FWI, including the classical, the multiscale, the envelope-based, and the amplitude-spectrum-based FWI approaches. A checkerboard model is used to compare the resolution of MASW and FWI. These numerical examples show that classical FWI has the highest nonlinearity and resolution among these methods, while MASW has the lowest nonlinearity and resolution. The modified FWI approaches have an intermediate nonlinearity and resolution between classical FWI and MASW. These features suggest that a sequential application of MASW and FWI could provide an efficient hierarchical way to delineate near-surface structures. We apply the sequential-inversion strategy to two field data sets acquired in Olathe, Kansas, USA, and Rheinstetten, Germany, respectively. We build a 1D initial model by using MASW and then apply the multiscale FWI to the data. High-resolution 2D S-wave velocity images are obtained in both cases, whose reliabilities are proven by borehole data and a GPR profile, respectively. It demonstrates the effectiveness of combining MASW and FWI for high-resolution imaging of near-surface structures.

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Section: Classical Fwimentioning

confidence: 99%

High-Resolution Characterization of Near-Surface Structures by Surface-Wave Inversions: From Dispersion Curve to Full Waveform

2019

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“…The Rayleigh wave dispersion curve carries a large amount of information regarding the underground strata. Many surface wave signal processing methods have emerged such as MASW [14][15][16], SASW [17], H/V spectral ratios [18] and shallow-seismic full waveform inversion [19][20][21]. MASW is the most widely used method and the vertical variation of the elastic parameters of the near-surface medium can be inverted from it.…”

Section: Introductionmentioning

confidence: 99%

The High-Speed Inversion of Rayleigh Wave and its Microtremor Application Analysis

Wang

2021

IEEE Access

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The application of Rayleigh waves in geological prospecting and engineering has been widely tested and used as a new method of shallow seismic exploration. The previous method of surface wave inversion needed to calculate the corresponding phase velocity that satisfied the dispersion function. This process increases the computation amount and reduces the surface wave inversion efficiency. This paper proposes a method to solve the problem by using different definition methods of the objective function. The method greatly reduces the amount of computation while results in approximately equal results with traditional method. This cuts the inversion time to 1% of conventional methods and provides favorable conditions for multiple inversions to suppress multiple solutions. We set artificial initial parameters with linear constraints in our velocity increasing model test to accelerate the convergence rate of the target function and obtain accurate results quicker. Finally, this method is applied to the microtremor dispersion data with a strong disturbance, and the inversion results are consistent with the high-density electrical profile and geological survey data. INDEX TERMS Rayleigh wave, Genetic algorithm, High-speed inversion, Microtremor explorationThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…Rayleigh‐wave measurements are highly sensitive to the S‐wave velocity ( Vs ), and for this reason they are extensively used for geotechnical characterization or seismic site response studies (e.g., Socco and Strobbia, 2004). Over the last years, the full‐waveform inversion of surface waves is getting growing attention thanks to the increased computational power of modern parallel architectures (Gross et al ., 2017; Xing and Mazzotti 2019). However, well‐established methods still rely on dispersion curve inversion under the assumption of a 1D subsurface structure (e.g., Socco and Boiero, 2008; Maraschini and Foti, 2010; Cercato, 2011; Foti et al ., 2018; Di Giulio et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets

Aleardi

Salusti

Pierini

2020

Near Surface Geophysics

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We compare two Monte Carlo inversions that aim to solve some of the main problems of dispersion curve inversion: deriving reliable uncertainty appraisals, determining the optimal model parameterization and avoiding entrapment in local minima of the misfit function. The first method is a transdimensional Markov chain Monte Carlo that considers as unknowns the number of model parameters, that is the locations of layer boundaries together with the Vs and the Vp/Vs ratio of each layer. A reversible‐jump Markov chain Monte Carlo algorithm is used to sample the variable‐dimension model space, while the adoption of a parallel tempering strategy and of a delayed rejection updating scheme improves the efficiency of the probabilistic sampling. The second approach is a Hamiltonian Monte Carlo inversion that considers the Vs, the Vp/Vs ratio and the thickness of each layer as unknowns, whereas the best model parameterization (number of layer) is determined by applying standard statistical tools to the outcomes of different inversions running with different model dimensionalities. This work has a mainly didactic perspective and, for this reason, we focus on synthetic examples in which only the fundamental mode is inverted. We perform what we call semi‐analytical and seismic inversion tests on 1D subsurface models. In the first case, the dispersion curves are directly computed from the considered model making use of the Haskell–Thomson method, while in the second case they are extracted from synthetic shot gathers. To validate the inversion outcomes, we analyse the estimated posterior models and we also perform a sensitivity analysis in which we compute the model resolution matrices, posterior covariance matrices and correlation matrices from the ensembles of sampled models. Our tests demonstrate that major benefit of the transdimensional inversion is its capability of providing a parsimonious solution that automatically adjusts the model dimensionality. The downside of this approach is that many models must be sampled to guarantee accurate posterior uncertainty. Differently, less sampled models are required by the Hamiltonian Monte Carlo algorithm, but its limits are the computational effort related to the Jacobian computation, and the multiple inversion runs needed to determine the optimal model parameterization.

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Two-Grid Genetic Algorithm Full Waveform Inversion of Surface Waves: Two Actual Data Examples

Cited by 5 publications

References 0 publications

High-Resolution Characterization of Near-Surface Structures by Surface-Wave Inversions: From Dispersion Curve to Full Waveform

High-Resolution Characterization of Near-Surface Structures by Surface-Wave Inversions: From Dispersion Curve to Full Waveform

The High-Speed Inversion of Rayleigh Wave and its Microtremor Application Analysis

Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets

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