王彦飞^1,2,,
唐静³,
耿伟峰⁴,
王成祥⁴
1. 中国科学院地质与地球物理研究所, 中国科学院油气资源研究重点实验室, 北京 100029
2. 中国科学院大学, 北京 100049
3. 西南石油大学地球科学与技术学院, 成都 610500
4. 中国石油集团东方地球物理勘探有限责任公司物探技术研究中心, 河北涿州 072751

基金项目: 中国石油集团《弹性波地震成像技术合作研发课题》和国家自然科学基金项目（91630202）联合资助


详细信息

作者简介: 王彦飞, 中国科学院地质与地球物理研究所研究员, 2002年毕业于中国科学院数学与系统科学研究院, 现从事地学大数据与人工智能、地球物理反演理论及计算的研究工作.E-mail:yfwang@mail.iggcas.ac.cn

中图分类号: P631

收稿日期:2017-06-01
修回日期:2018-01-19
上线日期:2018-03-05

Sparse solution of PP-PS joint inversion with constraint of particle filtering

WANG YanFei^1,2,,
TANG Jing³,
GENG WeiFeng⁴,
WANG ChengXiang⁴
1. Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Geosciences & Technology, Southwest Petroleum University, Chengdu 610500, China
4. R & D center, BGP, CNPC, Hebei Zhuozhou 072751, China


MSC: P631

--> Received Date: 01 June 2017
Revised Date: 19 January 2018
Available Online: 05 March 2018


摘要
摘要:随着地震勘探目标从构造型油气藏向岩性油气藏的转变，地震勘探难度日益增大，这就要求从地震数据中获得更多可靠且具有明确地质含义的属性信息，并充分利用这些属性信息来对储层的岩性、岩相进行分析.AVO三参数反演能够从振幅随炮检距的变化信息中直接提取纵波速度、横波速度以及密度来估计岩石和流体的性质，进而对储层进行预测.然而，AVO反演本身是一个不适定的问题，加上地震纵波反射系数对横波速度和密度的不敏感，会造成单纯利用纵波地震数据进行反演的结果误差大.随着地震接收和数据处理技术的发展，越来越多的****对PP-PS联合反演方法进行了研究并在实际资料中得以运用.融合转换横波地震数据的联合反演在一定程度上提高了反演的精度，降低了解的不稳定性.但是在信噪比较低的情况下，联合反演的效果受到了限制.本文从优化理论出发，提出了基于粒子滤波提供先验知识的l₁范数约束极小化问题的稀疏解算法.并将上述方法运用到了不同的模型中，通过比较分析，证实了该方法在不同信噪比资料中的有效性和在信噪比较低情况下的优势.
关键词: 联合反演/
粒子滤波/
正则化/
l₁范数/
稀疏解

Abstract:With the seismic prospecting target changing from structural reservoirs to lithologic reservoirs, it requires more reliable attribute information with clear geological meanings from seismic data to identify the lithology or lithoface information of the reservoirs. The three-term AVO inversion can be used to estimate the P-wave velocity, S-wave velocity and density of the rock and fluid's properties through the amplitude variations with offset. However, the AVO inversion is essentially an ill-posed problem. The pure seismic P-wave approaches are not sensitive to the shear wave velocity and density, which causes errors in the inversion results. With the development of seismic data acquisition and data processing technology, more and more scholars begin to study the PP-PS joint inversion and apply it to field data. The joint inversion can improve the inversion accuracy and to some extent reduce the inversion instability. However, in the low signal-to-noise ratio situations, we cannot obtain good results from the joint inversion. In this paper, we propose the l₁ norm constrained sparse optimization method with the initial model generated from the particle filtering. The effectiveness of this method is verified through model tests with three different signals-to-noise ratios and its advantage in the low SNR inversion is verified by comparing it with the conventional method.
Key words:Joint inversion/
Particle filtering/
Regularization/
l₁ norm/
Sparse solution



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