An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration
An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration
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基于宽带高斯光束偏移的有效声阻抗成像
DOI：
10.3390/en14144105
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发表时间：
2021-07
期刊：
影响因子：
3.2
通讯作者：
Huazhong Wang
中科院分区：
文献类型：
--
作者：
Shaoyong Liu;Wenting Zhu;Zhe Yan;Peng Xu;Huazhong Wang
地下声阻抗（AI）模型的估计是油气勘探地震资料处理的重要环节。全波形反演（FWI）是利用地面采集地震资料反演地下参数的一种有效方法。然而，地震数据和地下模型之间的强非线性关系在实践中会导致不收敛和不稳定的问题。为了将非线性反演分解为多个线性步骤，提出了一种基于宽带反射率的二维人工智能反演成像方法。首先，提出了一种基于高斯光束偏移（GBM）的地下点扩散函数（PSF）和常规图像生成方法。然后，宽带反射率可以通过相对于所计算的PSF对图像实施反卷积来获得。假设AI模型的低波数部分可以由背景速度推导出，我们通过将获得的宽带反射率合并为AI模型的高波数部分来实现AI反演成像方案，并产生宽带AI结果。基于GBM的宽带偏移作为所提出的二维AI反演成像的计算热点，仅包括两个GBM和一个高斯波束反偏移（Born建模）过程。因此，开发的宽带GBM比使用最小二乘偏移（LSM）的宽带成像更有效，需要多次迭代（每次迭代包括一个Born建模和一个偏移过程），以最小化数据残差的目标函数。数值算例表明了该方法的有效性和应用潜力。
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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