| 二维叠后地震数据的平稳随机介质参数估计 |
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| 引用本文： | 顾元,朱培民,李辉,李小勇.二维叠后地震数据的平稳随机介质参数估计[J].地球物理学报,2014,57(7):2291-2301. |
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| 作者姓名： | 顾元 朱培民 李辉 李小勇 |
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| 作者单位： | 1. 国土资源部海底矿产资源重点实验室, 广州海洋地质调查局, 广州 510075;2. 中国地质大学(武汉)地球物理与空间信息学院, 武汉 430074;3. 休斯敦大学地球物理系, 休斯敦 77004, 美国 |
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| 基金项目： | 国家自然科学基金(91014002和41174049)资助 |
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| 摘 要： | 随机介质参数估计是随机介质理论应用于地震勘探的关键.本文提出了一种从二维叠后地震数据中估计平稳随机介质参数的方法.文中阐述了二维叠后地震数据与随机介质波阻抗模型的关系，以及随机介质自相关函数参数的估计原理和方法，并结合实例详细介绍了应用功率谱法进行随机介质参数估计的具体步骤;通过多个二维理论模型的估计试验，验证了方法的可行性和正确性;还对实际地震数据进行了随机介质参数的估计试验，结果表明，随机介质参数可以为三角洲沉积相的进一步划分提供参考，反映了该方法有较好的应用前景.相比前人的研究，本文所提出的随机介质参数估计方法是一种真正的二维算法，特别是能给出自相关角度θ的估计，这种基于功率谱的估计方法具有直观且高效率的优点，但也存在着误差较大的问题，需要进一步的改进和完善.
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| 关 键 词： | 随机介质 自相关函数 参数估计 反演 |
| 收稿时间： | 2013-11-11 |
Estimation of 2D stationary random medium parameters from post-stack seismic data |
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| GU Yuan,ZHU Pei-Min,LI Hui,LI Xiao-Yong.Estimation of 2D stationary random medium parameters from post-stack seismic data[J].Chinese Journal of Geophysics,2014,57(7):2291-2301. |
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| Authors: | GU Yuan ZHU Pei-Min LI Hui LI Xiao-Yong |
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| Institution: | 1. MLR Key Laboratory of Marine Mineral Resources, Guangzhou Marine Geological Survey, Guangzhou 510075, China;2. Department of Geophysics, China University of Geosciences, Wuhan 430074, China;3. Department of Geosciences, University of Houston, TX, 77004, USA |
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| Abstract: | The key of applying the random medium theory to seismic exploration is the estimation of random medium parameters (RMP) from seismic data. In this paper, we propose an approach to estimate parameters of stationary random medium from 2D post-stack seismic data. Firstly, the relationship between post-stack seismic data and the impedance model of random medium is established, and the method of parameter estimation of Autocorrelation Function is introduced. Then, by using power spectrum method, the algorithms and procedures on estimating RMP are discussed with an example. Some numerical tests show that the approach is feasible and correct. The test on the real seismic data indicates that the estimated random medium parameters can provide an important reference for further dividing delta sedimentary facies, which reflects the estimation method of RMP is very promising. Comparing with peer-researches, the estimation method in this paper is a true 2D algorithm, especially, which could estimate the autocorrelation angle. This approach based on power spectrum method is obviously efficient and easily understandable but has large errors, and needs to be further improved and perfected in the future research. |
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| Keywords: | Random medium Autocorrelation function Parameter estimation Inversion |
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