An iterative Tikhonov regularization downward continuation of magnetic anomaly

被引:1
|
作者
Li, Houpu [1 ]
Zhao, Bairu [2 ,3 ]
Zhang, Henglei [2 ,3 ]
机构
[1] Naval Univ Engn, Sch Elect Engn, Wuhan 430000, Peoples R China
[2] Univ Geosci, Key Lab Geol Survey & Evaluat, Minist Educ, Wuhan 430074, Peoples R China
[3] China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China
基金
美国国家科学基金会;
关键词
Downward continuation; Magnetic anomaly; Tikhonov regularization; POTENTIAL-FIELD DATA; PARAMETER;
D O I
10.1016/j.jappgeo.2024.105354
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The Tikhonov regularization downward continuation (TRDC) is one of the most robust methods to enhance magnetic anomalies, where the determination of an optimum value of the regularization parameter is a crucial task. In this study, we propose an iterative Tikhonov regularization downward continuation (ITRDC) strategy to downward continue magnetic anomalies with no sensitives to the regularization parameter, which is beneficial for accurate downward continuation. We show that using some arbitrary values for the regularization parameter in the proposed ITRDC method, the expected downward continued field can be obtained by a series of iterations. Based on synthetic and field data examples using a large continuation distance of 40 times of the data spacing, we show that the proposed method is more accurate and stable than the standard TRDC.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] A novel Tikhonov regularization-based iterative method for structural damage identification of offshore platforms
    Wang, Shuqing
    Xu, Mingqiang
    Xia, Zhipeng
    Li, Yingchao
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 2019, 24 (02) : 575 - 592
  • [42] A novel Tikhonov regularization-based iterative method for structural damage identification of offshore platforms
    Shuqing Wang
    Mingqiang Xu
    Zhipeng Xia
    Yingchao Li
    Journal of Marine Science and Technology, 2019, 24 : 575 - 592
  • [43] A new choice rule for regularization parameters in Tikhonov regularization
    Ito, Kazufumi
    Jin, Bangti
    Zou, Jun
    APPLICABLE ANALYSIS, 2011, 90 (10) : 1521 - 1544
  • [44] Projected Newton method for noise constrained Tikhonov regularization
    Cornelis, J.
    Schenkels, N.
    Vanroose, W.
    INVERSE PROBLEMS, 2020, 36 (05)
  • [45] Sampled Tikhonov regularization for large linear inverse problems
    Slagel, J. Tanner
    Chung, Julianne
    Chung, Matthias
    Kozak, David
    Tenorio, Luis
    INVERSE PROBLEMS, 2019, 35 (11)
  • [46] An improved regularized downward continuation of potential field data
    Zeng, Xiaoniu
    Liu, Daizhi
    Li, Xihai
    Chen, Dingxin
    Niu, Chao
    JOURNAL OF APPLIED GEOPHYSICS, 2014, 106 : 114 - 118
  • [47] GCV for Tikhonov regularization by partial SVD
    Caterina Fenu
    Lothar Reichel
    Giuseppe Rodriguez
    Hassane Sadok
    BIT Numerical Mathematics, 2017, 57 : 1019 - 1039
  • [48] Projected nonstationary iterated Tikhonov regularization
    Guangxin Huang
    Lothar Reichel
    Feng Yin
    BIT Numerical Mathematics, 2016, 56 : 467 - 487
  • [49] On the convergence of algorithms with Tikhonov regularization terms
    Bruno Dinis
    Pedro Pinto
    Optimization Letters, 2021, 15 : 1263 - 1276
  • [50] Infinite-σ limits for Tikhonov regularization
    Lippert, Ross A.
    Rifkin, Ryan M.
    JOURNAL OF MACHINE LEARNING RESEARCH, 2006, 7 : 855 - 876