Feasibility of waveform inversion of Rayleigh waves for shallow shear-wave velocity using a genetic algorithm

Zeng, Chong; Xia, Jianghai; Miller, Richard D.; Tsoflias, G. P.

doi:10.1016/j.jappgeo.2011.09.028

Cited by 34 publications

(5 citation statements)

References 45 publications

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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%

“…The inclusion of surface waves in the wavefields also increases the nonlinearity of FWI (Gélis et al 2007;Brossier et al 2009). Numerical examples (Romdhane et al 2011;Zeng et al 2011a;Bretaudeau et al 2013;Borisov et al 2017;Pan et al 2018a) have demonstrated that FWI is a promising way in quantitatively imaging near-surface structures. Applications of FWI on field data sets (Tran et al 2013;Kallivokas et al 2013;Amrouche and Yamanaka 2015;Nguyen et al 2016;Pan et al 2016b;Dokter et al 2017) have also proved the applicability as well as the high resolution of FWI for characterizing near-surface heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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 second category is the completely nonlinear global optimization algorithm, including the global optimization algorithm based on random sampling in the solution space and the random search algorithm based on meta heuristic. The former is represented by Monte Carlo method Sun et al, 2022;Yang & Yuen, 2021), while the latter is represented by particle swarm optimization (PSO) (Ai et al, 2021;Poormirzaee, 2016Poormirzaee, , 2018, cuckoo search algorithm (Poormirzaee & Fister Jr, 2021), genetic algorithm (Qin et al, 2020;Zeng et al, 2011), firefly algorithm (Zhou et al, 2014), artificial neural network (Jian et al, 2011;Yablokov et al, 2021) and simulated annealing (Calderón-Macías & Luke, 2007;Chong et al, 2015;Lu et al, 2016;Pei et al, 2007). The gradient-based liner algorithm is characterized by rigorous derivative derivation, but depends on the selection of initial solution and the calculation of partial derivative matrix.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Luo

2023

Preprint

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Rayleigh wave is widely applied in engineering exploration and geotectonic research. While how to reconstruct the corresponding geological information via Rayleigh wave is the critical process and difficulty. This paper presents an inversion method of Rayleigh wave dispersion curves based on BP neural network and PSO. In this work, a sample set that referring to the actual stratum distribution is firstly generated. Then, BP neural network is adopted to train the nonlinear mapping relationship between the dispersion curves and the shear wave velocity of each stratum. The trained BP neural network can quickly output a predicted value with rationality but poor precision, which can be utilized as the initial model of PSO inversion. PSO will then be adopted to further optimize the inversion result on the basis of BP neural network prediction. The combination of BP neural network and PSO aims at overcoming the defects of BP neural network that unable to carry out continual optimization and the slow optimization of PSO in the absence of reasonable initial solution. Finally, the effectiveness of the proposed algorithm is verified by a series of synthetic models and an active-source Rayleigh wave experiment carried out in a new railway project from Baotou, Inner Mongolia to Yinchuan, Ningxia.

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“…Surface waves are dispersive and sensitive to subsurface shear wave velocity (Vs) structures. Since Vs structure is crucial for understanding both deep earth structures and shallow subsurface, numerous methods, multi‐channel analysis of surface wave (MASW) (Park et al., 1999; Xia et al., 1999) and full waveform inversion (FWI) type of methods (Brossier et al., 2009; Li et al., 2017; Pan et al., 2016, 2019; Tran et al., 2013; Zeng et al., 2011) for example, are developed to invert Versus structure from surface wave recordings. These recordings can be acquired using active or controlled seismic sources, and they have also been discovered to be retrievable from ambient noise (Aki, 1957; Park et al., 2004; Shapiro & Campillo, 2004; Shapiro et al., 2005).…”

Section: Introductionmentioning

confidence: 99%

Improving Surface Wave Retrieval From Traffic Noise by Deconvolution of the Decomposed Wavefield

Cao

Huang

et al. 2023

Earth and Space Science

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Traffic noise is an important type of passive seismic data because it usually includes strong dispersive surface wave components and can be easily accessed. It can be used to extract virtual surface waves via seismic interferometry algorithms for the purpose of imaging subsurface shear wave velocity distribution. In this paper, we propose a scheme to improve the retrieval of surface waves from traffic noise recorded using linear arrays along traffic roads. By deconvolving the decomposed traffic noise wavefield, robust surface wave traces can be computed from a short noise record. First the far‐field component of the traffic noise recording is extracted and separated into unidirectionally propagating components. Then deconvolution interferometry is applied to these separated far‐field wavefield to extract surface wave Green's function. With this scheme, crosstalk noise and near‐field artifacts are excluded from the computation, and surface wave traces with high signal‐to‐noise ratio (SNR) are achieved using short traffic noise traces. In a synthetic test virtual surface waves estimated with the proposed method show significantly higher SNR than those computed with the conventional interferometry workflows, and matches well with simulated active source traces. A field data example with traffic noise recorded in a distributed acoustic sensing experiment also shows that surface waves estimated using the proposed methodology demonstrate higher SNR than those computed with the conventional interferometry schemes and that the virtual surface waves generated using 4 s of traffic noise demonstrate signal quality comparable to the surface waves recorded in this experiment with an active source.

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Feasibility of waveform inversion of Rayleigh waves for shallow shear-wave velocity using a genetic algorithm

Cited by 34 publications

References 45 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

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Improving Surface Wave Retrieval From Traffic Noise by Deconvolution of the Decomposed Wavefield

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