海洋一次波与多次波联合最小二乘逆时偏移

项目名称： 海洋一次波与多次波联合最小二乘逆时偏移

项目编号： No.41504105

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 叶月明

作者单位： 中国石油天然气股份有限公司勘探开发研究院

项目金额： 20万元

中文摘要： 海洋地震资料普遍发育着强多次反射波，如何利用好多次波已成为提高海洋地震资料成像品质的新突破点，目前，主流的多次波成像方法只是单独利用多次波，受多次波预测精度、构造假象和干涉噪音的严重影响难以推广应用。本课题研究基于波形反演的一次波与多次波联合最小二乘逆时偏移方法，对其中关键因素进行研究。首先根据海洋地震资料的特点，研究适用的地震子波提取方法，应用稳健的全波模拟算子保证地下点格林函数计算的准确性，再基于动态时间规整算法，求取可反映速度误差的时移量来研究残差泛函的优化，修正由于速度不准造成的动力学成像误差。分析局部成像矩阵内构造假象和干涉噪音的分布特征和规律，研究成像矩阵内假象和噪音的压制方法，加速最小二乘偏移迭代收敛速度。分析鬼波和涌浪干扰等海洋资料采集噪音对偏移的影响，提出该方法对实际地震资料的品质要求条件。最终得到适用于海洋地震资料的，稳健高效的一次波与多次波联合最小二乘逆时偏移方法。

中文关键词： 多次波成像；最小二乘偏移；目标函数优化；假象压制；；海洋噪声

英文摘要： There widely exist strong multiples in marine seismic data and how multiples were used is a new breakthrough for improving image quality of marine seismic data. Currently, only multiples were used for imaging in migration of multiples and suffered seriously from the accuracy of multiples prediction, artifacts and interference noises which made it difficult for practical application. In this project, we introduce Joint least-squares reverse time migration of primaries and multiples based on waveform inversion, and focused on key factors. Firstly, based on the character of marine seismic data, proper wavelet estimation method will be proposed. Robust full wave forward modeling operator guarantee the accuracy of subsurface point Green function calculation. According to the dynamic time warping (DIM), time shift for the velocity error can be obtained to optimize residual function which can adjust dynamic imaging error due to inaccurate velocity. Artifacts and interference noises will be suppressed by analyzing the characters of that in local image matrix (LIM) to improve rate of convergence for least-squares reverse time migration of primaries and multiples. Influence of ghost wave and surging wave will be analyzed and proposed seismic data quality requirement for this method. At last, this planned work will provide robust and effective least-squares reverse time migration of primaries and multiples for marine seismic data.

英文关键词： Multiples imaging;Least-squares migration;Optimized objective function;Artifacts suppressing;Marine noise