Pattern Recognition and Image Analysis

, Volume 28, Issue 4, pp 830–840 | Cite as

Method of Estimating the Geometric Parameters of a Three-Dimensional Object from Resistivity Survey Data

  • I. V. ZhurbinEmail author
  • O. M. Nemtsova
  • A. G. Zlobina
  • D. V. Gruzdev
Applied Problems


The testing of the proposed method for estimating the geometric parameters of a three-dimensional object from the data of computer-aided simulation and field experiments confirmed by excavation provide the possibility to determine the location of anomalous-resistance objects under the ground and to perform the quantitative estimation of their geometric parameters (shape, dimensions, and burial depth) from resistivity survey data. It is shown that the analysis of the vector pictures of main resistance change directions provides the possibility to estimate the spatial location of an object of search (along the depth and in horizontal “sections”) at a qualitative level and to determine its relative resistance. The application of the scalar product function and the adaptive fuzzy clustering algorithm to these vector pictures provides the possibility to estimate the shape of an object of search and the range of its burial depths. Using the A* optimal path search algorithm, it is possible to plot the boundary line of an object on a set of horizontal “sections.”


resistivity survey vector picture object of search geometric parameters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Electrical Exploration: Handbook of Geophysics, 2nd ed. (in 2 volumes), Ed. by V. K. Khmelevskoy and V. M. Bondarenko, Vol. 1 (Nedra, Moscow, 1989) [in Russian].Google Scholar
  2. 2.
    A. A. Nikitin, Statistical Methods for Allocation of Geophysical Anomalies (Nedra, Moscow, 1979) [in Russian].Google Scholar
  3. 3.
    A. A. Nikitin and V. K. Khmelevskoy, Integration of Geophysical Methods (GERS, Tver, 2004) [in Russian].Google Scholar
  4. 4.
    J. Ogden, S. Kaey, G. Earl, K. Strutt, and S. Kay, “Geophysical prospection at Portus: An evaluation of an integrated approach to the interpretation of subsurface archaeological features,” in Proc. 37th Computer Applications to Archaeology Conference (CAA 2009) (Colonial Williamsburg Foundation, Williamsburg, VA, 2010), pp. 273–284.Google Scholar
  5. 5.
    Y.–C. Song, H.–D. Meng, M. J. O’Grady, and G. M. P. O’Hare, “The application of cluster analysis in geophysical data interpretation,” Comput. Geosci. 14 (2). P. 263–271 (2010).Google Scholar
  6. 6.
    M. H. Loke, Tutorial: 2–D and 3–D electrical imaging surveys (University of Alberta, 2012).Google Scholar
  7. 7.
    A. Arato, S. Piro, and L. Sambuelli, “3D inversion of ERT data on an archaeological site using GPR reflection and 3D inverted magnetic data as a priori information,” Near Surface Geophys. 13 (6), 545–556 (2015)Google Scholar
  8. 8.
    A. M. Pavlova, The application of shallow electrical exploration for studying three–dimensionally inhomogeneous media, Ph. D. Thesis, Lomonosov Moscow State University, Moscow, 2014 [in Russian].Google Scholar
  9. 9.
    O. M. Nemtsova, I. V. Zhurbin, and A. G. Zlobina, “Vector analysis of geophysical data of shallow resistivity survey to determine 3d boundaries of resistivity anomalous object,” Eng. Phys., No. 1, 76–87 (2017) [in Russian].Google Scholar
  10. 10.
    L. S. Edwards, “A modified pseudosection for resistivity and induced polarization,” Geophys. 42 (5), 1020–1036 (1977).CrossRefGoogle Scholar
  11. 11.
    I. V. Zhurbin and D. V. Malyugin, “On the method of visualization of electrometric data,” Archaeol. Prospect. 5 (2), 73–79 (1998).CrossRefGoogle Scholar
  12. 12.
    Geoecological survey of oil industry enterprises, Ed. by V. A. Shevnin and I. N. Modin (RUSSO, Moscow, 1999) [in Russian].Google Scholar
  13. 13.
    M. N. Marchenko, V. I. Stankevich, A. Yu. Tereshchenko, et al., Some Issues of Metrological Support for Engineering and Geophysical Surveys. Electrical Exploration by Resistivity and IP Methods (Lomonosov Moscow State University, Moscow, 2013) [in Russian].Google Scholar
  14. 14.
    I. V. Zhurbin and D. V. Grusdev, “Multi–electrode equipment and software for shallow electrical survey in archaeology,” Razvedka i Okhrana Nedr (Prospect and Protection of Mineral Resources), No. 12, 37–38 (2004) [in Russian].Google Scholar
  15. 15.
    B. Jähne, Digital Image Processing (Springer, Berlin, Heidelberg, 2005; Technosphera, Moscow, 2007).zbMATHGoogle Scholar
  16. 16.
    T. Terano, K. Asai, and M. Sugeno (eds.): Applied Fuzzy Systems (Omsya, Tokyo, 1989; Mir, Moscow, 1993; Academic Press Professional, San Diego, 1994).Google Scholar
  17. 17.
    A. A. Barsegyan, M. S. Kupriyanov, V. V. Stepanenko, and I. I. Kholod, Data Analysis Methods and Models: OLAP and Data Mining, Textbook (BKhV–Petersburg, Saint Petersburg, 2004) [in Russian].Google Scholar
  18. 18.
    A. G. Zlobina and I. V. Zhurbin, “The restoration of the borders of the object according to the shallow resistivity survey by the method of fuzzy clustering,” Geoinformatika, No. 3, 19–25 (2015) [in Russian].Google Scholar
  19. 19.
    N. J. Nilsson, Problem–Solving Methods in Artificial Intelligence (McGraw–Hill, New York, 1971; Mir, Moscow, 1973, pp. 70–80).Google Scholar
  20. 20.
    S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed. (Prentice Hall, Englewood Cliffs, NJ, 2003; Williams, Moscow, 2007).Google Scholar
  21. 21.
    W. A. Yasnoff, J. K. Miu, and J. W. Bacus, “Error measures for scene segmentation,” Pattern Recogn. 9 (4), 217–231 (1977).CrossRefGoogle Scholar
  22. 22.
    H. Zhang, J. E. Fritts, and S. A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods,” Comput. Vision Image Understanding 110 (2), 260–280 (2008).CrossRefGoogle Scholar
  23. 23.
    P. P. Kol’tsov, A. S. Osipov, A. S. Kutsaev, A. A. Kravchenko, N. V. Kotovich, and A. V. Zakharov, “On the quantitative performance evaluation of image analysis algorithms,” Comput. Opt. 39 (4), 542–556 (2015) [in Russian].CrossRefGoogle Scholar
  24. 24.
    M. G. Ivanova and I. V. Zhurbin, “Archaeological and geophysical studies of medieval settlements in the Cheptsa river basin,” Russian Archaeology, No. 1, 40–53 (2014) [in Russian].Google Scholar
  25. 25.
    M. G. Ivanova and R. N. Modin, “Kushmansky ancient settlement Uchkakar of the X–XIIIth centuries: Materials of external part in the context of medieval settlements development,” in Trudy Kamskoj Arheologo–Jetnograficheskoj Jekspedicii (Proceedings of the Kamsky Archaeological and Ethnographic Expedition), Issue X (Perm State Humanitarian and Pedagogical University, Perm’, 2015), pp. 138–151 (2015) [in Russian].Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • I. V. Zhurbin
    • 1
    Email author
  • O. M. Nemtsova
    • 1
  • A. G. Zlobina
    • 1
  • D. V. Gruzdev
    • 1
  1. 1.Physical-Technical Institute, Ural BranchRussian Academy of SciencesIzhevskRussia

Personalised recommendations