Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Wu, Hao; Chen, Yingpin; Shu, Li; Peng, Zhenming

doi:10.3390/en12142744

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…By comparing conventional GBM image with the corrected reflectivity image, we found that the image after correction has more balanced amplitudes in both the shallower and the deeper parts of the model, as well as more abundant wavenumber contents. We chose a local part of the model to show the quantitative im- Next, we implemented conventional GBM to produce the subsurface image and then performed the correction to produce a broadband reflectivity using Equation (5). The image from the conventional GBM and the corrected image using the proposed method are shown in Figure 3a,b, respectively.…”

Section: Numerical Examplesmentioning

confidence: 99%

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An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

et al. 2021

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The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

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Section: Numerical Examplesmentioning

confidence: 99%

“…Acoustic impedance (AI), as a basic physical parameter, is defined as the product of density and velocity, which plays a key role to connect the surface seismic data to the subsurface model [2,3]. Therefore, the broadband AI imaging is crucial for seismic interpretation and beneficial to reservoir predication [4,5].…”

Section: Introductionmentioning

confidence: 99%

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

et al. 2021

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“…Consequently, inversing AI from seismic data is significant research. In the past decades, several methods were proposed to solve this problem [4]- [6]. The pro-stack AI inversion is a popular one as it can transform the inversion problem to a linear optimization problem which can be solved easily [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Seismic Acoustic Impedance Inversion Using Mixed Second-Order Fractional ATpV Regularization

Chen

et al. 2020

IEEE Access

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Seismic acoustic impedance (AI) inversion is widely used in geophysics as AI can indicate rock characteristics and facilitate stratigraphic analysis. However, traditional AI inversion suffers from a multi-solution problem. To overcome this barrier, anisotropic total p-variation (ATpV) regularization has been applied in inversion as it can improve the accuracy by Lp quasi-norm. Nevertheless, this regularization results in the staircase effect and the scattering effect. To reduce these two effects, we introduced the mixed second-order variations and the fractional difference in AI inversion based on ATpV and proposed a novel AI inversion method using mixed second-order fractional anisotropic total p-variation (MS_FATpV) regularization. Moreover, the alternating direction method of multipliers (ADMM) algorithm is used to build the inversion framework. Numerical experiments demonstrate that the fractional difference and the mixed second-order variant can reduce the staircase and scattering effects. The proposed method reduces the multiplicity and improves the accuracy than some state-of-the-art methods based on anisotropic total variation (ATV).

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Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain

Zhao

et al. 2020

Energies

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Seismic reflection coefficient inversion in the joint time-frequency domain is a method for inverting reflection coefficients using time domain and frequency domain information simultaneously. It can effectively improve the time-frequency resolution of seismic data. However, existing research lacks an analysis of the factors that affect the resolution of inversion results. In this paper, we analyze the influence of parameters, such as the length of the time window, the size of the sliding step, the dominant frequency band, and the regularization factor of the objective function on inversion results. The SPGL1 algorithm for basis pursuit denoising was used to solve our proposed objective function. The applied geological model and experimental field results show that our method can obtain a high-resolution seismic reflection coefficient section, thus providing a potential avenue for high-resolution seismic data processing and seismic inversion, especially for thin reservoir inversion and prediction.

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Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Cited by 3 publications

References 49 publications

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

Seismic Acoustic Impedance Inversion Using Mixed Second-Order Fractional ATpV Regularization

Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain

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