| 去均值归一化互相关最小二乘逆时偏移及其应用 |
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| 引用本文： | 李庆洋,黄建平,李振春,李娜,李闯.去均值归一化互相关最小二乘逆时偏移及其应用[J].地球物理学报,2016,59(8):3006-3015. |
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| 作者姓名： | 李庆洋 黄建平 李振春 李娜 李闯 |
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| 作者单位： | 1. 中国石油大学(华东)地球科学与技术学院, 青岛 266580;2. 中原油田分公司物探研究院, 河南濮阳 457001 |
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| 基金项目： | 国家自然科学基金（41104069，41274124）、国家重点基础研究发展计划（973计划）项目课题（2014CB239006）、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： | 最小二乘逆时偏移相对于常规逆时偏移具有更高的成像分辨率、振幅保幅性及均衡性等优势,在一定程度上可满足岩性油气藏勘探的需求,是目前研究的热点之一.然而由于实际地下介质的黏滞性和变密度,以及无法准确地估计震源子波等,基于振幅匹配的常规最小二乘逆时偏移算法很难在实际资料处理中取得好的效果.此外,实际数据常包含大量噪声,进一步限制了常规算法的应用.为此,本文通过修改目标泛函,提出了去均值归一化的互相关最小二乘逆时偏移算法,并给出了陆上资料的应用实例.研究表明,归一化策略减弱了算法对子波能量的苛求;互相关算法强调相位匹配,进一步弱化了子波的影响,提升了算法的稳定性和可靠性;去均值思想在处理低信噪比资料方面有较大优势.理论模型和陆上实际资料处理都验证了本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 去均值归一化互相关 实际资料 相位匹配 子波估计 |
| 收稿时间： | 2015-02-02 |
Mean-residual normalized cross-correlation least-squares reverse time migration and its application |
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| LI Qing-Yang,HUANG Jian-Ping,LI Zhen-Chun,LI Na,LI Chuang.Mean-residual normalized cross-correlation least-squares reverse time migration and its application[J].Chinese Journal of Geophysics,2016,59(8):3006-3015. |
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| Authors: | LI Qing-Yang HUANG Jian-Ping LI Zhen-Chun LI Na LI Chuang |
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| Institution: | 1. School of Geoscience, China University of Petroleum(East China), Qingdao 266580, China;2. Geophysical Exploration Research Institute, Zhongyuan Oilfield Company, Henan Puyang 457001, China |
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| Abstract: | Compared to the conventional reverse time migration, the least-squares reverse time migration (LSRTM) has a lot of advantages, such as higher imaging resolution, amplitude preservation and amplitude balance. To a certain extent, LSRTM can meet the needs of lithologic reservoir exploration, thus it is the focus of current research. However, as the Earth is at least a viscoelastic medium with density variations, it is difficult to define a good source signature in the modeling. As a result, the conventional LSRTM algorithm based on the amplitude matching is difficult to achieve a good result in field data processing. In addition, field data often contain a lot of noise, which further limit the application of the conventional LSRTM algorithm. In this paper, by modifying the objective function we propose a mean-residual normalized cross-correlation LSRTM algorithm (MNCC-LSRTM). Then we apply this method to land field data. Studies have shown that the normalization strategy can weaken the demanding of source wavelet estimation. The cross-correlation algorithm emphasizes phase matching, so it can further reduce the influence of the amplitude and enhance the stability and reliability of the algorithm. The mean-residual method has a profound advantage in the treatment of high-level noisy data. Theoretical models and field data processing verify the effectiveness of this algorithm and its suitability for complex models. |
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| Keywords: | Least-squares reverse time migration Mean-residual normalized cross-correlation Field data Phase match Source wavelet estimation |
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