Elastic least-squares reverse time migration of steeply dipping structures using prismatic reflections

被引:3
|
作者
Wu, Zheng [1 ]
Liu, Yuzhu [1 ,2 ]
Yang, Jizhong [2 ]
机构
[1] Tongji Univ, Sch Ocean & Earth Sci, Shanghai 20092, Peoples R China
[2] Tongji Univ, State Key Lab Marine Geol, Shanghai 20092, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
WAVE-FORM INVERSION; FIELD; EQUATION;
D O I
10.1190/geo2021-0423.1
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The migration of prismatic reflections can be used to delineate steeply dipping structures, which is crucial for oil and gas exploration and production. Elastic least-squares reverse time migration (ELSRTM), which considers the effects of elastic wave propagation, can be used to obtain reasonable subsurface reflectivity estimations and interpret multicomponent seismic data. In most cases, we can only obtain a smooth migration model. Thus, conventional ELSRTM, which is based on the first-order Born approximation, considers only primary reflections and cannot resolve steeply dipping structures. To address this issue, we develop an ELSRTM framework, called PrisELSRTM, which can jointly image primary and prismatic reflections in multicomponent seismic data. When Pris-ELSRTMis directly applied to multicomponent records, near-vertical structures can be resolved. However, the application of imaging conditions established for prismatic reflections to primary reflections destabilizes the process and leads to severe contamination of the results. Therefore, we further improve the PrisELSRTM framework by separating prismatic reflections from recorded multicomponent data. By removing artificial imaging conditions from the normal equation, primary and prismatic reflections can be imaged based on unique imaging conditions. The results of synthetic tests and field data applications demonstrate that the improved Pris-ELSRTM framework produces high-quality images of steeply dipping P-and S-wave velocity structures. However, it is difficult to delineate steep density structures because of the insensitivity of the density to prismatic reflections.
引用
收藏
页码:S75 / S94
页数:20
相关论文
共 50 条
  • [21] A wavefield-separation-based elastic least-squares reverse time migration
    Gu, Bingluo
    Li, Zhenchun
    Han, Jianguang
    GEOPHYSICS, 2018, 83 (03) : S279 - S297
  • [22] Elastic least-squares reverse-time migration with density variation for flaw imaging in heterogeneous structures
    Rao, Jing
    Yang, Jizhong
    He, Jiaze
    Huang, Ming
    Rank, Ernst
    SMART MATERIALS AND STRUCTURES, 2020, 29 (03)
  • [23] A new scheme of wavefield decomposed elastic least-squares reverse time migration
    Lv, Wenhao
    Du, Qizhen
    Fu, Li-Yun
    Li, Qingqing
    Zhang, Jianlei
    Zou, Zhen
    FRONTIERS IN EARTH SCIENCE, 2022, 10
  • [24] Viscoacoustic reverse time migration of prismatic wave for steeply dipped structures
    Qu Y.
    Wei Z.
    Liu C.
    Li Z.
    Xu K.
    Li R.
    1600, Science Press (55): : 793 - 803
  • [25] Prestack correlative least-squares reverse time migration
    Liu, Xuejian
    Liu, Yike
    Lu, Huiyi
    Hu, Hao
    Khan, Majid
    GEOPHYSICS, 2017, 82 (02) : S159 - S172
  • [26] Staining algorithm for least-squares reverse time migration
    Liu, Chang
    Qu, Yingming
    Li, Zhenchun
    Zeng, Shenghan
    Yang, Tingyu
    Zhao, Weijie
    JOURNAL OF APPLIED GEOPHYSICS, 2023, 219
  • [27] Least-squares reverse time migration using convolutional neural networks
    Zhang, Wei
    Gao, Jinghuai
    Yang, Tao
    Jiang, Xiudi
    Sun, Wenbo
    GEOPHYSICS, 2021, 86 (06) : R959 - R971
  • [28] Full wavefield least-squares reverse time migration
    Davydenko, Mikhail
    Verschuur, Eric
    GEOPHYSICS, 2021, 86 (05) : WC67 - WC74
  • [29] Improving the gradient in least-squares reverse time migration
    Liu, Qiancheng
    JOURNAL OF GEOPHYSICS AND ENGINEERING, 2016, 13 (02) : 172 - 180
  • [30] Prestack correlative elastic least-squares reverse time migration based on wavefield decomposition
    Shi, Ying
    Li, Songling
    Zhang, Wei
    Journal of Applied Geophysics, 2021, 194