| 逆子波域消除多次波方法研究 |
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| 引用本文： | 金德刚,常旭,刘伊克.逆子波域消除多次波方法研究[J].地球物理学报,2008,51(1):250-259. |
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| 作者姓名： | 金德刚 常旭 刘伊克 |
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| 作者单位： | 中国科学院地质与地球物理研究所工程地质力学重点实验室， 北京 100029 |
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| 基金项目： | 中国科学院知识创新工程项目 |
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| 摘 要： | SRMA（与表面相关多次波的衰减）算法包含预测和相减两步.相减算法中，当多次波与反射波同相轴相交时，如何有效减去多次波、保留反射波，是面临的主要问题.通过分析非正交性对滤波器（逆子波）的影响，可以证明：逆子波因非正交性产生的误差呈近似的高斯分布.在此基础上，本文提出了在逆子波域（单道自适应相减滤波的滤波算子的集合），利用其误差的概率分布特征，对逆子波进行估计，用逆子波的估计对逆子波进行校正来消除多次波的方法.其步骤为：首先用SRMA方法预测出表面多次波，并对每一单炮进行单道自适应相减，得到逆子波，形成逆子波域；其次，在逆子波域采用中值滤波，提取接近真实逆子波的逆子波估计；第三，在逆子波域用逆子波估计对畸变的逆子波进行校正；最后采用校正后的逆子波来衰减多次波.通过简单模型和SMARRT模型的测试，该方法不仅能够有效减去多次波，而且在相交的区域，能够保持反射波同相轴的连续性并恢复其正确的振幅.
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| 关 键 词： | SRMA 逆子波域 逆子波估计 相关系数 高斯分布 正交性 |
| 文章编号： | 0001-5733（2008）01-0250-10 |
| 收稿时间： | 2007-07-12 |
| 修稿时间： | 2007-10-27 |
Research of multiple elimination method in inverse wavelet domain |
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| JIN De-Gang,CHANG Xu,LIU Yi-Ke.Research of multiple elimination method in inverse wavelet domain[J].Chinese Journal of Geophysics,2008,51(1):250-259. |
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| Authors: | JIN De-Gang CHANG Xu LIU Yi-Ke |
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| Institution: | Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China |
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| Abstract: | SRMA（Surface Related Multiple Attenuation）algorithm has two steps including prediction and subtraction. In subtraction, it is very important to attenuate multiple events effectively and preserve primary events under the situation when multiple events cross with or overlap on primary. Under some reasonable hypothetic conditions，the error of inverse wavelet which is caused by non-orthogonality is nearly Gauss distribution. Based on the conclusion, we adopt a new method to eliminate multiples, which is to use the statistic character of error, obtain inverse wavelet's estimation, correct misshaped inverse wavelets for non-orthogonality in inverse wavelet domain by the estimation. The method mainly has four steps. Firstly, after getting predicted multiples, we use adaptive subtraction algorithm to eliminate multiples and get some inverse wavelets corresponding to every trace in one shot gather, then construct inverse wavelets domain by those inverse wavelets. Secondly, use median filtering method to get inverse wavelet estimation in inverse domain. Thirdly, correct the misshaped inverse wavelets in inverse domain by the inverse wavelet estimation. Finally, use new corrected inverse wavelets as new filters to eliminate multiples. It is proved that the method is valid and can keep continuation and amplitude of the primaries after simple and complex model testing. |
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| Keywords: | SRMA |
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