A Fast Local Level Set Method for Inverse Gravimetry

被引:41
|
作者
Isakov, Victor [2 ]
Leung, Shingyu [3 ]
Qian, Jianliang [1 ]
机构
[1] Michigan State Univ, Dept Math, E Lansing, MI 48824 USA
[2] Wichita State Univ, Dept Math & Stat, Wichita, KS 67208 USA
[3] Hong Kong Univ Sci & Technol, Dept Math, Hong Kong, Hong Kong, Peoples R China
基金
美国国家科学基金会;
关键词
Level set methods; inverse gravimetry; fast algorithms; numerical continuation; RECONSTRUCTION; EQUATIONS; EVOLUTION;
D O I
10.4208/cicp.100710.021210a
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
We propose a fast local level set method for the inverse problem of gravimetry. The theoretical foundation for our approach is based on the following uniqueness result: if an open set D is star-shaped or x(3)-convex with respect to its center of gravity, then its exterior potential uniquely determines the open set D. To achieve this purpose constructively, the first challenge is how to parametrize this open set D as its boundary may have a variety of possible shapes. To describe those different shapes we propose to use a level-set function to parametrize the unknown boundary of this open set. The second challenge is how to deal with the issue of partial data as gravimetric measurements are only made on a part of a given reference domain a To overcome this difficulty, we propose a linear numerical continuation approach based on the single layer representation to find potentials on the boundary of some artificial domain containing the unknown set D. The third challenge is how to speed up the level set inversion process. Based on some features of the underlying inverse gravimetry problem such as the potential density being constant inside the unknown domain, we propose a novel numerical approach which is able to take advantage of these features so that the computational speed is accelerated by an order of magnitude. We carry out numerical experiments for both two- and three-dimensional cases to demonstrate the effectiveness of the new algorithm.
引用
收藏
页码:1044 / 1070
页数:27
相关论文
共 50 条
  • [41] Underwater image segmentation based on fast level set method
    Li, Yujie
    Xu, Huiliang
    Li, Yun
    Lu, Huimin
    Serikawa, Seiichi
    INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING, 2019, 19 (04) : 562 - 569
  • [42] A level-set method for fast image segmentation based on local pre-fitting and bilateral filtering
    Zou, Le
    Chen, Qianqian
    Wu, Zhize
    Thanh, Dang N. H.
    ENGINEERING COMPUTATIONS, 2025, 42 (01) : 96 - 116
  • [43] A fast local level set adjoint state method for first arrival transmission traveltime tomography with discontinuous slowness
    Li, Wenbin
    Leung, Shingyu
    GEOPHYSICAL JOURNAL INTERNATIONAL, 2013, 195 (01) : 582 - 596
  • [44] On the use of the fitting method in solving inverse problems of gravimetry and magnetometry
    Bulakh, E. G.
    IZVESTIYA-PHYSICS OF THE SOLID EARTH, 2006, 42 (02) : 156 - 160
  • [45] A parallel method of solving nonlinear inverse problems in gravimetry and magnetometry
    Boikov, I. V.
    Boikova, A. I.
    IZVESTIYA-PHYSICS OF THE SOLID EARTH, 2009, 45 (03) : 248 - 257
  • [46] SOLUTION OF THE INVERSE LINEAR GRAVIMETRY PROBLEM BY METHOD OF FRACTIONAL REGULARIZATION
    STRAKHOV, VN
    DOPOVIDI AKADEMII NAUK UKRAINSKOI RSR SERIYA B-GEOLOGICHNI KHIMICHNI TA BIOLOGICHNI NAUKI, 1987, (04): : 23 - 27
  • [47] On the use of the fitting method in solving inverse problems of gravimetry and magnetometry
    E. G. Bulakh
    Izvestiya, Physics of the Solid Earth, 2006, 42 : 156 - 160
  • [48] A parallel method of solving nonlinear inverse problems in gravimetry and magnetometry
    I. V. Boikov
    A. I. Boikova
    Izvestiya, Physics of the Solid Earth, 2009, 45 : 248 - 257
  • [49] Local level set segmentation method combined with narrow band
    Li, Yushi
    Zhou, Jun
    Li, Junlong
    Liu, Chunsheng
    MIPPR 2007: AUTOMATIC TARGET RECOGNITION AND IMAGE ANALYSIS; AND MULTISPECTRAL IMAGE ACQUISITION, PTS 1 AND 2, 2007, 6786
  • [50] A Local Curvature Based Adaptive Particle Level Set Method
    Wang, Cheng
    Wang, Wanli
    Pan, Shucheng
    Zhao, Fuyu
    JOURNAL OF SCIENTIFIC COMPUTING, 2022, 91 (01)