Full waveform inversion with image‐guided gradient

Ma, Yuanming; Hale, Dave; Meng, Zhaobo; Gong, Bin

doi:10.1190/1.3513016

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…Then combining the Eqs. (14) and (15), the model perturbation quantity corresponding to the modified quasi-Newton method is expressed as…”

Section: Derivation Of the Modified Quasi-newton Methodsmentioning

confidence: 99%

“…Brossier et al [12] applied the L-BFGS algorithm to elastic wave FWI, and obtained an accelerated and improved convergence compared to the preconditioned conjugate-gradient method. Meanwhile, Ma et al [13] employed a projected Hessian matrix to modify the quasi-Newton BFGS method for reducing both the computational cost and memory required; and applied the image-guided gradient method to FWI, which speeds up the convergence rate by reducing the parameter number [14] . However, the conventional quasi-Newton method only exploits the gradient and model information for approximating the inverse Hessian matrix, and neglects the objective function value.…”

Section: Introductionmentioning

confidence: 99%

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Full Waveform Inversion Based on Modified Quasi‐Newton Equation Quasi‐Newton Equation

Liu

Zhang

et al. 2013

Chinese J of Geophysics

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Waveform inversion is a kind of method to reveal the underground structure and lithology through minimizing the residual error between predicted wavefield and true seismic record using full‐wavefield information. In this paper, we briefly present the principle of the conventional Quasi‐Newton algorithm, and then exploit a new modified Quasi‐Newton equation to modify the conventional Davidon‐Fletcher‐Powell (DFP) and Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) algorithms. Different from past Quasi‐Newton methods, this modified BFGS considers gradient values, model information and objective function values to approximate the inverse matrix of Hessian matrix. Moreover, it almost does not increase the calculation amount for each iteration. The numerical experiment shows that compared with the conventional Quasi‐Newton method, the modified BFGS algorithm can not only preserve the inverse accuracy, but also significantly improve calculation efficiency.

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“…Then combining the Eqs. (14) and (15), the model perturbation quantity corresponding to the modified quasi-Newton method is expressed as…”

Section: Derivation Of the Modified Quasi-newton Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Full Waveform Inversion Based on Modified Quasi‐Newton Equation Quasi‐Newton Equation

Liu

Zhang

et al. 2013

Chinese J of Geophysics

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“…This approach is conceptually similar to the sparse inversion proposed by Ma et al . (). Figure shows the evolution of the objective function with iterations.…”

Section: Examplesmentioning

confidence: 97%

“…We used the complete stacked image to guide the smoothing of the gradient. This approach is conceptually similar to the sparse inversion proposed by Ma et al (2010). Figure 9 shows the evolution of the objective function with iterations.…”

Section: Synthetic Laterally Heterogeneous Modelmentioning

confidence: 97%

Wavefield tomography based on local image correlations

Perrone¹,

Sava

Panizzardi

2014

Geophysical Prospecting

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The estimation of a velocity model from seismic data is a crucial step for obtaining a high-quality image of the subsurface. Velocity estimation is usually formulated as an optimization problem where an objective function measures the mismatch between synthetic and recorded wavefields and its gradient is used to update the model. The objective function can be defined in the data-space (as in full-waveform inversion) or in the image space (as in migration velocity analysis). In general, the latter leads to smooth objective functions, which are monomodal in a wider basin about the global minimum compared to the objective functions defined in the dataspace. Nonetheless, migration velocity analysis requires construction of commonimage gathers at fixed spatial locations and subsampling of the image in order to assess the consistency between the trial velocity model and the observed data. We present an objective function that extracts the velocity error information directly in the image domain without analysing the information in common-image gathers. In order to include the full complexity of the wavefield in the velocity estimation algorithm, we consider a two-way (as opposed to one-way) wave operator, we do not linearize the imaging operator with respect to the model parameters (as in linearized wave-equation migration velocity analysis) and compute the gradient of the objective function using the adjoint-state method. We illustrate our methodology with a few synthetic examples and test it on a real 2D marine streamer data set.

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“…Following Meng (2009), who proposes to use subsurface dips to constrain the inversion, we investigate the image-guided gradient (Ma et al, 2010) to complement low frequencies that are usually unavailable in recorded data. In this paper, we propose image-guided sparse FWI, which aims to make FWI more efficient and more stable and to generate geologically sensible results.…”

Section: Introductionmentioning

confidence: 99%

Image-guided sparse-model full waveform inversion

Hale

Gong

et al. 2012

GEOPHYSICS

Self Cite

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Multiple problems, including high computational cost, spurious local minima, and solutions with no geologic sense, have prevented widespread application of full waveform inversion (FWI), especially FWI of seismic reflections. These problems are fundamentally related to a large number of model parameters and to the absence of low frequencies in recorded seismograms. Instead of inverting for all the parameters in a dense model, image-guided full waveform inversion inverts for a sparse model space that contains far fewer parameters. We represent a model with a sparse set of values, and from these values, we use image-guided interpolation (IGI) and its adjoint operator to compute finely and uniformly sampled models that can fit recorded data in FWI. Because of this sparse representation, image-guided FWI updates more blocky models, and this blockiness in the model space mitigates the absence of low frequencies in recorded data. Moreover, IGI honors imaged structures, so image-guided FWI built in this way yields models that are geologically sensible.

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Full waveform inversion with image‐guided gradient

Abstract: Figure 1. Change of data misfit functions vs. iterations in full waveform inversion and image-guided full waveform inversion.

Cited by 17 publications

References 31 publications

Full Waveform Inversion Based on Modified Quasi‐Newton Equation Quasi‐Newton Equation

Full Waveform Inversion Based on Modified Quasi‐Newton Equation Quasi‐Newton Equation

Wavefield tomography based on local image correlations

Image-guided sparse-model full waveform inversion

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