Basis Pursuit Anisotropic Inversion Based on the L1-L2-Norm Regularization

被引:4
|
作者
Luo, Cong [1 ]
Ba, Jing [1 ]
Carcione, Jose M. [1 ,2 ]
Guo, Qiang [1 ]
机构
[1] Hohai Univ, Dept Geol Engn, Nanjing 211100, Peoples R China
[2] Natl Inst Oceanog & Appl Geophys OGS, I-34010 Trieste, Italy
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Basis pursuit inversion (BPI); prestack seismic inversion; sparse constraint; transversely isotropic with vertical axis of symmetry (VTI) medium; PRESTACK SEISMIC INVERSION; TRANSMISSION COEFFICIENTS; REFLECTIVITY; OFFSET;
D O I
10.1109/LGRS.2021.3075062
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
Prestack seismic inversion for VTI media (transversely isotropic with vertical axis of symmetry) is a technique that can be useful to obtain the properties (velocity, density, and anisotropy parameters) of shale reservoirs. Since conventional inversion with smooth constraints (e.g., L-2-norm) is not appropriate, we propose a basis pursuit inversion (BPI) extended to VTI media, where: 1) we decompose the five elasticities into basis pursuit pairs by a dipole decomposition; 2) instead of the commonly used L-1-norm, the L-1-L-2 is implemented as a regularization constraint to achieve higher resolution and stability; and 3) alternating direction method of multipliers (ADMM) is used to obtain the solutions. Since the problem is highly ill-posed, we perform the inversion using PP and PS multicomponent seismic data. The examples (synthetic and real data) verify the higher resolution and better antinoise performance of the proposed method.
引用
下载
收藏
页数:5
相关论文
共 50 条
  • [41] Variable selection in convex quantile regression: L1-norm or L0-norm regularization?
    Dai, Sheng
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2023, 305 (01) : 338 - 355
  • [42] Parameter choices for sparse regularization with the l1 norm
    Liu, Qianru
    Wang, Rui
    Xu, Yuesheng
    Yan, Mingsong
    INVERSE PROBLEMS, 2023, 39 (02)
  • [43] Robust censored regression with l1 -norm regularization
    Beyhum, Jad
    Van Keilegom, Ingrid
    TEST, 2023, 32 (01) : 146 - 162
  • [44] L 1/2 regularization
    Xu ZongBen
    Zhang Hai
    Wang Yao
    Chang XiangYu
    Liang Yong
    SCIENCE CHINA-INFORMATION SCIENCES, 2010, 53 (06) : 1159 - 1169
  • [45] L1-norm based nonlinear inversion of transient electromagnetic data
    Sun HuaiFeng
    Zhang NuoYa
    Liu ShangBin
    Li DunRen
    Chen ChengDong
    Ye QiongYao
    Xue YiGuo
    Yang Yang
    CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2019, 62 (12): : 4860 - 4873
  • [46] Structural damage identification through unscented Kalman inversion with <bold>l</bold>1-norm regularization
    Li, Dan
    Zhou, Jiajun
    Zhang, Jian
    ENGINEERING OPTIMIZATION, 2024, 56 (12) : 2542 - 2564
  • [47] Prestack Seismic Inversion via Nonconvex L1-2 Regularization
    Nie, Wenliang
    Xiang, Fei
    Li, Bo
    Wen, Xiaotao
    Nie, Xiangfei
    APPLIED SCIENCES-BASEL, 2021, 11 (24):
  • [48] Prestack data attenuation compensation based on L1-norm regularization constraint
    Cheng W.
    Wang S.
    Meng J.
    Wang Z.
    Zhang J.
    Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting, 2023, 58 (03): : 567 - 579
  • [49] Bi-l0-l2-norm regularization for blind motion deblurring
    Shao, Wen-Ze
    Li, Hai-Bo
    Elad, Michael
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2015, 33 : 42 - 59
  • [50] Sparse minimal learning machines via l1/2 norm regularization
    Dias, Madson L. D.
    Freire, Ananda L.
    Souza Junior, Amauri H.
    da Rocha Neto, Ajalmar R.
    Gomes, Joao P. P.
    2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 2018, : 206 - 211