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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Geological Structure-Guided Initial Model Building for Prestack AVO/AVA Inversion
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Geological Structure-Guided Initial Model Building for Prestack AVO/AVA Inversion

机译：Prestack AVO / AVA反转的地质结构引导初始模型建筑

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摘要

Reconstructing an accurate and high-resolution subsurface model is attractive in the fields of both geology and seismology. However, due to the band-limited characteristics of seismic data, the inversion greatly depends on the reliability of the initial model. A fairly acceptable initial model could lay a good foundation for seismic inversion. In this article, we first introduce a well-log interpolation method with the local slope as a constraint for building a high-fidelity starting model in prestack amplitude versus offset/angle (AVO/AVA) inversion. First, we briefly review the basic theory of general seismic inversion. Then, instead of using the conventional preconditioned least-squares method, we introduce shaping regularization theory into the geological structure-guided well-log interpolation to accelerate the convergence. We use the plane-wave destruction (PWD) algorithm to extract the slope attribute from seismic data, images, or velocity models. The slope is used as the constraint to solve the inverse problem based on the shaping regularization method. Numerical examples demonstrate that the proposed initial model building method performs better than the conventional ones. It greatly improves the accuracy of inversion results. Furthermore, we apply the proposed model building method to the inverse problems of AVO/AVA inversion and reservoir parameter estimation of several field data sets for the first time, which demonstrate encouraging performance.
机译：重建准确和高分辨率的地下模型在地质和地震学的领域具有吸引力。然而，由于地震数据的带状特性，反演大大取决于初始模型的可靠性。一个相当可接受的初始模型可以为地震反演奠定良好的基础。在本文中，我们首先将局部斜率引入一个良好的对数插值方法，作为构建Prestack幅度与偏移/角度（AVO / AVA）反转的高保真启动模型的约束。首先，我们简要介绍了一般地震反演的基本理论。然后，而不是使用传统的预处理最小二乘法，我们将塑造正则化理论介绍到地质结构引导的井口插值中，以加速收敛。我们使用平面波破坏（PWD）算法从地震数据，图像或速度模型中提取斜率属性。基于成形正则化方法用作解决逆问题的约束。数值示例表明，所提出的初始模型构建方法比传统的初始模型构建方法更好地执行。它大大提高了反演结果的准确性。此外，我们将所提出的模型构建方法应用于第一次现场数据集的AVO / AVA反转和储库参数估计的逆问题，这表明了令人鼓舞的性能。

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