Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Impact factor

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS


Zh. Vychisl. Mat. Mat. Fiz.:
Year:
Volume:
Issue:
Page:
Find




Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register

Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2020, Volume 60, Number 6, Pages 1027–1034
DOI: https://doi.org/10.31857/S0044466920060101 (Mi zvmmf11092) 		 
This article is cited in 6 scientific papers (total in 6 papers)

Reconstruction of magnetic susceptibility using full magnetic gradient data

Y. Wangabc, I. I. Kolotovd, D. V. Lukyanenkod, A. G. Yagolad

a Key Laboratory of Petroleum Resource Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029 P. R. China
b University of the Chinese Academy of Sciences, Beijing, 100049 P. R. China
c Institutions of Earth Science, Chinese Academy of Sciences, Beijing,100029 P. R. China
d Faculty of Physics, Lomonosov Moscow State University
Citations (6)
References:
PDF
HTML
DOI: https://doi.org/10.31857/S0044466920060101
Abstract: The paper discusses the specificities of solving the inverse problem of reconstructing the magnetic susceptibility using complete tensor magnetic gradient data. This problem reduces to solving a system of two three-dimensional Fredholm integral equations of the first kind, one of which relates the magnetic susceptibility of a bounded body to the magnetic field induced by it and the other, to the full magnetic induction gradient tensor. For a series of model examples, it is demonstrated that the use of the full magnetic induction gradient tensor significantly improves the quality of the reconstructed function that determines the magnetic susceptibility.
Key words: magnetostatics, magnetic susceptibility, full magnetic induction gradient tensor, inverse problem.
Funding agency Grant number
Russian Foundation for Basic Research 19-51-53005
17-01-00159
National Natural Science Foundation of China 11911530084
Ministry of Science and Technology (MOST) of China 2018YFC0603500
The research was carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University [22, 23].
Received: 28.10.2019
Revised: 28.10.2019
Accepted: 11.02.2020
English version:
Computational Mathematics and Mathematical Physics, 2020, Volume 60, Issue 6, Pages 1000–1007
DOI: https://doi.org/10.1134/S096554252006010X
Bibliographic databases:
Document Type: Article
UDC: 519.6
Language: Russian
Citation: Y. Wang, I. I. Kolotov, D. V. Lukyanenko, A. G. Yagola, “Reconstruction of magnetic susceptibility using full magnetic gradient data”, Zh. Vychisl. Mat. Mat. Fiz., 60:6 (2020), 1027–1034; Comput. Math. Math. Phys., 60:6 (2020), 1000–1007
Citation in format AMSBIB
\Bibitem{WanKolLuk20}
\by Y.~Wang, I.~I.~Kolotov, D.~V.~Lukyanenko, A.~G.~Yagola
\paper Reconstruction of magnetic susceptibility using full magnetic gradient data
\jour Zh. Vychisl. Mat. Mat. Fiz.
\yr 2020
\vol 60
\issue 6
\pages 1027--1034
\mathnet{http://mi.mathnet.ru/zvmmf11092}
\crossref{https://doi.org/10.31857/S0044466920060101}
\elib{https://elibrary.ru/item.asp?id=42809599}
\transl
\jour Comput. Math. Math. Phys.
\yr 2020
\vol 60
\issue 6
\pages 1000--1007
\crossref{https://doi.org/10.1134/S096554252006010X}
\isi{https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=Publons&SrcAuth=Publons_CEL&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000555591800007}
\scopus{https://www.scopus.com/record/display.url?origin=inward&eid=2-s2.0-85088933600}
Linking options:
  • https://www.mathnet.ru/eng/zvmmf11092
  • https://www.mathnet.ru/eng/zvmmf/v60/i6/p1027
    • This publication is cited in the following 6 articles:
    1. Igor Kolotov, Dmitry Lukyanenko, Inna Stepanova, Yanfei Wang, Anatoly Yagola, “Recovering the Near-Surface Magnetic Image of Mercury from Satellite Observations”, Remote Sensing, 15:8 (2023), 2125  crossref
    2. I. E. Stepanova, A. V. Shchepetilov, P. S. Mikhailov, “Analytical Models of Time-Dependent Physical Fields of the Earth: Local Version”, Fizika zemli, 2023:2 (2023), 20  crossref
    3. I. E. Stepanova, A. V. Shchepetilov, P. S. Mikhailov, “Analytical Models of Time-Dependent Physical Fields of the Earth: Local Version”, Izv., Phys. Solid Earth, 59:2 (2023), 120  crossref
    4. Stepanova I.E., Gudkova T.V., Salnikov A.M., Batov A.V., “A New Approach to Analytical Modeling of Mars'S Magnetic Field”, Inverse Probl. Sci. Eng., 30:1 (2022), 41–60  crossref  mathscinet  isi  scopus
    5. I. E. Stepanova, A. V. Shchepetilov, P. S. Mikhailov, “Analytical Models of the Physical Fields of the Earth in Regional Version with Ellipticity”, Izv., Phys. Solid Earth, 58:3 (2022), 406  crossref
    6. I. Kolotov, D. Lukyanenko, I. Stepanova, Ya. Wang, A. Yagola, “Recovering the magnetic image of Mars from satellite observations”, J. Imaging, 07:11 (2021), 234  crossref  isi
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
    Statistics & downloads:
    Abstract page:145
    References:30
     
      Contact us:
    		  Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2025