| 基于Student′s t分布的不依赖子波最小二乘逆时偏移 |
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| 引用本文： | 李庆洋,黄建平,李振春.基于Student′s t分布的不依赖子波最小二乘逆时偏移[J].地球物理学报,2017,60(12):4790-4800. |
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| 作者姓名： | 李庆洋 黄建平 李振春 |
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| 作者单位： | 中国石油大学(华东)地球科学与技术学院, 青岛 266580 |
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| 基金项目： | 国家重点基础研究发展计划项目（2014CB239006），国家自然科学基金（41104069）及中央高校基本科研业务经费专项资金（14CX06072A）联合资助. |
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| 摘 要： | 最小二乘逆时偏移(Least-Squares Reverse Time Migration,LSRTM)与常规偏移相比具有更高的成像分辨率、振幅保真性及均衡性等优势,是当前研究的热点之一.震源子波的估计直接影响LSRTM结果的好坏,在实际情况下考虑到震源子波的空变特性,其估计十分困难.为了消除子波对LSRTM结果的影响,本文发展了基于卷积目标泛函的不依赖子波LSRTM算法.目标泛函由观测记录卷积模拟记录的参考道以及模拟记录卷积观测记录的参考道组成,由于观测子波和模拟子波在目标泛函的两项中同时存在,从而消除了子波的影响.此外,常用的基于L2范数拟合的LSRTM算法对噪声非常敏感,尤其是当地震数据中含有异常值时,常规LSRTM无法得到满意的结果.Student′s t分布相比L2范数具有更好的稳健性,本文将其推广到不依赖子波LSRTM中,提升了算法的稳健性,最后通过理论模型及实际资料试算验证了算法的有效性和对复杂模型的适应性.
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| 关 键 词： | 最小二乘逆时偏移 不依赖子波 Student's t分布 目标泛函 卷积 |
| 收稿时间： | 2016-06-13 |
Source-independent least-squares reverse time migration using student's t distribution |
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| LI Qing-Yang,HUANG Jian-Ping,LI Zhen-Chun.Source-independent least-squares reverse time migration using student's t distribution[J].Chinese Journal of Geophysics,2017,60(12):4790-4800. |
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| Authors: | LI Qing-Yang HUANG Jian-Ping LI Zhen-Chun |
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| Institution: | School of Geosciences of China University of Petroleum(East China), Qingdao 266580, China |
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| Abstract: | Compared to the conventional migration method, the least-squares reverse time migration (LSRTM) has a lot of advantages, such as higher imaging resolution, amplitude preservation and amplitude balance. It is the focus of current research. However, the source wavelet estimation for LSRTM is a very difficult task. The challenge of determining source strength, which can vary from source to source, is even greater. In this paper, we developed a source-independent LSRTM using convolved wavefields. The misfit function consists of the convolution of the observed wavefields with a reference trace from the modeled wavefields, plus the convolution of the modeled wavefields with a reference trace from the observed wavefield. In this case, the source wavelet of the observed and the modeled wavefields are equally convolved with both terms in the misfit function, and thus, the effects of the source wavelets are eliminated. In addition, the field data often contain a lot of noise. The L2 norm based LSRTM algorithm is very sensitive to noise, especially when the data contains outliers. In this case, the conventional LSRTM result is seriously contaminated by noise. Compared to L2 norm, Student's t distribution has better robustness. We extend the Student's t distribution to the SILSRTM algorithm. Theoretical models and field data processing verify the effectiveness of the algorithm and suitability for complex models. |
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| Keywords: | Least-squares reverse time migration Source-independent Student's t distribution Object function Convolution |
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