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海底相关层间多次波预测方法
引用本文:叶月明,庄锡进,杨存,刘午牛,周芳明.海底相关层间多次波预测方法[J].中国石油勘探,2014(3):72-77.
作者姓名:叶月明  庄锡进  杨存  刘午牛  周芳明
作者单位:中国石油杭州地质研究院;中国石油大学(北京)地球物理信息工程学院;
基金项目:国家自然科学基金项目“马尼拉俯冲带构造的地震资料精细成像研究”(41206043)
摘    要:层间多次波是地震资料处理领域中最难以衰减的噪声之一,没有固定的规律,而且在偏移剖面中容易产生假象,影响资料处理质量。在海洋地震资料中,海底相关层间多次波广泛存在,由于海洋底界面的海底和海水间存在较强的速度差,当海底面以下存在高速反射层或散射体时,在海底与高速层间会发育较强的与海底相关的层间多次波。针对海底相关层间多次波问题,提出了一种基于波动理论的海底相关的层间多次波衰减方法。鉴于海洋资料海底反射较为明显,通过分离海底一次反射与海底面以下构造的反射波场的褶积与相关运算构建海底相关层间多次波,实现其合理的预测。本方法不依赖于准确的速度场,且具有较高的计算效率,是一种数据驱动型层间多次波预测方法。简单平层模型测试直观地证明了本方法的正确性,最后通过Sigsbee2B模型测试,合理地预测出了海底相关层间多次波,证实了本方法对复杂构造的实用性。

关 键 词:层间多次波  表面多次波  互易理论  数据驱动  格林函数

Method for Prediction of Sea Bottom Related Interval Multiples
Ye Yueming,Zhuang Xijin,Yang Cun,Liu Wuniu,Zhou Fangming.Method for Prediction of Sea Bottom Related Interval Multiples[J].China Petroleum Exploration,2014(3):72-77.
Authors:Ye Yueming  Zhuang Xijin  Yang Cun  Liu Wuniu  Zhou Fangming
Affiliation:1PetroChina Hangzhou Research Institute of Geology; 2 College of Geophysics and Information Engineering, China University of Petroleum (Beijing))
Abstract:Interval multiple is one of noise which is quite difficult to suppress in seismic data processing due to its irregular reflection. It appears as artifact in migration which affects the seismic processing quality. In marine seismic data processing, sea bottom related interval multiples exist widely because the strong impedance difference at sea bottom leads to sea bottom related interval multiple especially under the sea bottom where high velocity layer or scattering body exists. In order to solve this problem, a method for prediction of sea bottom related interval multiple prediction is proposed, which is based on wave equation. Thanks to the clear sea bottom reflection, sea bottom related interval multiple was predicted by convolution and cross-correlation with sea bottom reflection and those wave-field reflected under sea bottom. This is a data-driven multiple prediction method which overcomes the disadvantage of velocity dependence and has higher computation efficiency. Simple model test shows its validity. Finally, based on Sigsbee2B model test, sea bottom related interval multiple is reasonably predicted, confirming the adaptability of this method to complex substructure.
Keywords:interval multiple  surface-related multiple  reciprocity  data-driven  Green function
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