| 基于稀疏反演理论的自动叠加速度反演方法 |
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| 引用本文： | 徐文君,殷俊锋,王华忠,冯波.基于稀疏反演理论的自动叠加速度反演方法[J].地球物理学报,2017,60(7):2791-2800. |
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| 作者姓名： | 徐文君 殷俊锋 王华忠 冯波 |
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| 作者单位： | 1. 同济大学数学系, 上海 200092;2. 同济大学海洋与地球科学学院, 波现象与反演成像研究组, 上海 200092 |
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| 基金项目： | 中国博士后基金（2016M600331），国家自然科学基金（41604091，41374117，11271289），国家重点基础研究发展计划（973计划）项目（2011CB201002），国家重大专项（2011ZX05005-005-008HZ，2011ZX05006-002，2011ZX05023）以及中央高校基础研究经费资助. |
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| 摘 要： | 叠加速度分析技术是常规地震资料处理中的重要环节,也是经典的时间域速度建模方法.叠加速度分析技术主要包括速度谱计算和拾取两个步骤.至今为止,多数研究工作通过提高速度谱的分辨率以及抗噪声能力,获得高质量的速度谱从而有利于拾取.本文的目标是将叠加速度分析技术转为一个全自动化的处理流程.从参数估计的角度出发,将叠加速度估计转化为稀疏反演框架下的模型参数估计问题,并通过稀疏反演算法自动反演叠加速度,进而提高叠加速度建模的效率.为实现这一目标,首先给出了正问题的定义,即层状介质中CMP道集的预测模型,利用叠加速度、垂向双程走时(t_0)以及反射子波以及CMP道集时距关系(如双曲时距关系)可以预测CMP道集.接着,速度分析反问题可以描述为已知观测的CMP道集,估计模型参数(叠加速度及t_0时间等).利用模型参数的稀疏性作为约束条件并用L_0范数作为模型稀疏性的度量准则,叠加速度分析可以转化为L_0范数约束下的稀疏反演问题.本文提出了一种基于预测校正思想的匹配追踪算法求解上述反问题,实现了自动叠加速度建模并为后续的高精度速度反演方法提供较好的初始模型.理论和实际资料的测试结果证明了本文方法的有效性.
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| 关 键 词： | 叠加速度分析 速度谱 自动速度建模 CMP道集 稀疏反演 |
| 收稿时间： | 2016-06-12 |
Automatic estimation of stacking velocity based on sparse inversion |
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| XU Wen-Jun,YIN Jun-Feng,WANG Hua-Zhong,FENG Bo.Automatic estimation of stacking velocity based on sparse inversion[J].Chinese Journal of Geophysics,2017,60(7):2791-2800. |
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| Authors: | XU Wen-Jun YIN Jun-Feng WANG Hua-Zhong FENG Bo |
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| Institution: | 1. Department of Mathematics, Tongji University, Shanghai 200092, China;2. School of Ocean and Earth Science, Wave Phenomena and Inversion Imaging Group(WPI), Tongji University, Shanghai 200092, China |
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| Abstract: | Stacking velocity analysis is a routine procedure in seismic data processing, and also a classical method for construction of initial velocity models. Until now, many researchers are trying to improve the stacking velocity spectra by computing a better semblance, considering the AVO effect or improving the anti-noise ability of algorithm. However, it is seldom discussed on how to calculate the stacking velocity automatically. In this paper, we attempt to solve this problem by combining the velocity spectra calculation and picking procedure into a model parameter estimation under the framework of sparse inversion. Therefore, it is possible to invert the stacking velocity automatically and shorten the turn-around time of initial velocity model building and reduce human costs considerably. To solve this problem, first we give the definition of a forward problem, which is the prediction model for CMP gather using stacking velocity and t0 time as model parameters. Then, the inverse problem is defined as finding the sparse model parameters with the given CMP gather. Using the sparsity of model parameters as a model constraint, we reformulate the conventional stacking velocity analysis problem as a new sparse inverse problem, and present a new matching pursuit (MP) algorithm to solve it. The proposed method is quite promising for automatic construction of the initial model, and can provide a good initial model for subsequent high-resolution velocity inversion. Numerical and field data tests demonstrate the effectiveness of the proposed method. |
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| Keywords: | Stacking velocity analysis Velocity spectrum Automatic velocity model building CMP gather Sparse inversion |
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