Skip to main content
SpringerLink
Log in

An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging

  • Geological, Civil, Energy and Traffic Engineering
  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network (RBFNN) based on information criterion (IC) and particle swarm optimization (PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE’s information criterion (AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks (BPNNs) and traditional least square(LS) inversion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. SHIMA H, SAKAYAMA T. Resistivity tomography: An approach to 2-D resistivity inverse problems [J]. SEG Technical Program Expanded Abstracts, 1987: 59–61.

    Google Scholar 

  2. SHIMA H. 2-D and 3-D resistivity image reconstruction using crosshole data [J]. Geophysics, 1992, 57(10): 1270–1281.

    Article  Google Scholar 

  3. LOKE M H, BARKER R D. Least-squares deconvolution of apparent resistivity pseudosections [J]. Geophysics, 1995, 60(6): 1682–1690.

    Article  Google Scholar 

  4. LESUR V, CUER M, STRAUB A. 2-D and 3-D interpretation of electrical tomography measurements, Part 2: The inverse problem [J]. Geophysics, 1999, 64(2): 396–402.

    Article  Google Scholar 

  5. EL-QADY G, USHIJIMA K. Inversion of DC resistivity data using neural networks [J]. Geophysical Prospecting, 2001, 49(4): 417–430.

    Article  Google Scholar 

  6. SRINIVAS Y, RAJ A S, OLIVER D H, MUTHURAJ D, CHANDRASEKAR N. A robust behavior of feed forward back propagation algorithm of artificial neural networks in the application of vertical electrical sounding data inversion [J]. Geoscience Frontiers, 2012, 3(5): 729–736.

    Article  Google Scholar 

  7. SINGH U K, TIWARI R K, SINGH S B. Neural network modeling and prediction of resistivity structures using VES Schlumberger data over a geothermal area [J]. Computers & Geosciences, 2013, 52: 246–257.

    Article  Google Scholar 

  8. NEYAMADPOUR A, TAIB S, WAN A T. Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB application [J]. Computers & Geosciences, 2009, 35(11): 2268–2274.

    Article  Google Scholar 

  9. SINGH U K, TIWARI R K, SINGH S B. Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network [J]. Nonlinear Processes in Geophysics, 2010, 17(1): 65–76.

    Article  Google Scholar 

  10. NEYAMADPOUR A, ABDULLAH W W, TAIB S. Inversion of quasi-3D DC resistivity imaging data using artificial neural networks [J]. Journal of Earth System Science, 2010, 119(1): 27–40.

    Article  Google Scholar 

  11. NEYAMADPOUR A, ABDULLAH W W, TAIB S, NIAMADPOUR D. 3D inversion of DC data using artificial neural networks [J]. Studia Geophysica et Geodaetica, 2010, 54(3): 465–485.

    Article  Google Scholar 

  12. HO T L. 3-D inversion of borehole-to-surface electrical data using a back-propagation neural network [J]. Journal of Applied Geophysics, 2009, 68(4): 489–499.

    Article  Google Scholar 

  13. CHANDWANI V, AGRAWAL V, NAGAR R. Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks [J]. Expert Systems with Applications, 2015, 42(2): 885–893.

    Article  Google Scholar 

  14. SAMANTA B, BANDOPADHYAY S. Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit [J]. Computers & Geosciences, 2009. 35(8): 1592–1602.

    Article  Google Scholar 

  15. KAFTAN I, SALK M, SENOL Y. Evaluation of gravity data by using artificial neural networks case study: Seferihisar geothermal area (Western Turkey) [J]. Journal of Applied Geophysics, 2011, 75(4): 711–718.

    Article  Google Scholar 

  16. CHEN S, COWAN C F N, GRANT P M. Orthogonal least squares learning algorithm for radial basis function networks [J]. IEEE Transactions on Neural Networks, 1991, 2(2): 302–309.

    Article  Google Scholar 

  17. KENNEDY J, EBERHART R. Particle swarm optimization [C]// IEEE International Conference on Neural Networks. Perth, Australia: IEEE Press, 1995: 1942–1948.

    Google Scholar 

  18. TSEKOURAS G E. A simple and effective algorithm for implementing particle swarm optimization in RBF network’s design using input-output fuzzy clustering [J]. Neurocomputing, 2013, 108: 36–44.

    Article  Google Scholar 

  19. LI J, LIU X. Melt index prediction by RBF neural network optimized with an MPSO-SA hybrid algorithm [J]. Neurocomputing, 2011, 74(5): 735–740.

    Article  Google Scholar 

  20. TSEKOURAS G E, TSIMIKAS J. On training RBF neural networks using input–output fuzzy clustering and particle swarm optimization [J]. Fuzzy Sets and Systems, 2013, 221: 65–89.

    Article  MathSciNet  MATH  Google Scholar 

  21. MENG Y, ZOU J, GAN X, ZHAO L. Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm [J]. Neurocomputing, 2012, 83: 212–221.

    Article  Google Scholar 

  22. SHI Y, EBERHART R. A modified particle swarm optimizer [C]// IEEE International Conference on Evolutionary Computation. Washington DC, US: IEEE Press, 1998: 69–73.

    Google Scholar 

  23. ZHOU P, LI D, WU H, CHENG F. The automatic model selection and variable kernel width for RBF neural networks [J]. Neurocomputing, 2011, 74(17): 3628–3637.

    Article  Google Scholar 

  24. KOKSHENEV I, PADUA B A. An efficient multi-objective learning algorithm for RBF neural network [J]. Neurocomputing, 2010, 73(16): 2799–2808.

    Article  Google Scholar 

  25. BOLBOACA S D, LORENTZ J. Comparison of quantitative structure-activity relationship model performances on carboquinone derivatives [J]. The Scientific World Journal, 2009, 9: 1148–1166.

    Article  Google Scholar 

  26. DAI Qian-wei, JIANG Fei-bo, DONG Li. Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm [J]. Journal of Central South University, 2014, 21: 2018–2025.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    Fei-bo Jiang  (江沸菠), Qian-wei Dai  (戴前伟) & Li Dong  (董莉)

  2. College of Physics and Information Science, Hunan Normal University, Changsha, 410081, China

    Fei-bo Jiang  (江沸菠)

  3. Department of Information Science and Engineering, Hunan International Economics University, Changsha, 410205, China

    Li Dong  (董莉)

Authors
  1. Fei-bo Jiang  (江沸菠)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian-wei Dai  (戴前伟)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Dong  (董莉)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian-wei Dai  (戴前伟).

Additional information

Foundation item: Project(41374118) supported by the National Natural Science Foundation, China; Project(20120162110015) supported by Research Fund for the Doctoral Program of Higher Education, China; Project(2015M580700) supported by the China Postdoctoral Science Foundation, China; Project(2016JJ3086) supported by the Hunan Provincial Natural Science Foundation, China; Project(2015JC3067) supported by the Hunan Provincial Science and Technology Program, China; Project(15B138) supported by the Scientific Research Fund of Hunan Provincial Education Department, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, Fb., Dai, Qw. & Dong, L. An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging. J. Cent. South Univ. 23, 2129–2138 (2016). https://doi.org/10.1007/s11771-016-3269-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-016-3269-8

Key words

Search

Navigation