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Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

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Abstract

Nonlinear resistivity inversion requires efficient artificial neural network (ANN) model for better inversion results. An evolutionary BP neural network (BPNN) approach based on differential evolution (DE) algorithm was presented, which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion. In the proposed method, Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter F crit was used to enhance the ability of converging to global optimum. An implementation of proposed DE-BPNN was given, the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets. Two abnormity models were used to verify the feasibility and effectiveness of the proposed method, the results show that the proposed DE-BP algorithm has better performance than BP, conventional DE-BP and other chaotic DE-BP methods in stability and accuracy, and higher imaging quality than least square inversion.

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References

  1. DINES K A, LYTLE R J. Computerized geophysical tomography [J]. Proceedings of the IEEE, 1979, 67(7): 1065–1073.

    Article  Google Scholar 

  2. DAILY W, RAMIREZ A. Electrical resistance tomography during in-situ trichloroethylene remediation at the Savannah River Site [J]. Journal of Applied Geophysics, 1995, 33(4): 239–249.

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. LOKE M H, BARKER R D. Least-squares deconvolution of apparent resistivity pseudosections [J]. Geophysics, 1995, 60(6): 1682–1689.

    Article  Google Scholar 

  5. ZOHDY A A R. A new method for the automatic interpretation of Schlumbeger and Wenner sounding curves [J]. Geophysics, 1989, 54(2): 245–253.

    Article  Google Scholar 

  6. EL-QADY G, KEISUKE U. Inversion of DC resistivity data using neural network [J]. Geophysical Prospecting, 2001, 49(4): 417–430.

    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, SAMSUDIN T, ABDULLAH W. 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. XIU Hai-lang, WU Xiao-ping. 2 D resistivity inversion using the neural network method [J]. Chinese J Geophys, 2006, 49(2): 584–589. (in Chinese)

    Google Scholar 

  10. 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 

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

    Article  Google Scholar 

  12. BAŞTÜRK A, GÜNAY E. Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm [J]. Expert Systems with Applications, 2009, 36(2): 2645–2650.

    Article  Google Scholar 

  13. SUBUDHI B, JENA D. A differential evolution based neural network approach to nonlinear system identification [J]. Applied Soft Computing, 2011, 11(1): 861–871.

    Article  Google Scholar 

  14. CHAUHAN N, RAVI V, KARTHIK D. Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks [J]. Expert Systems with Applications, 2009, 36(4): 7659–7665.

    Article  Google Scholar 

  15. GAO X Z, WANG X, OVASKA S J. Fusion of clonal selection algorithm and differential evolution method in training cascade-correlation neural network [J]. Neurocomputing, 2009, 72(10): 2483–2490.

    Article  Google Scholar 

  16. QIN A K, HUANG V L. Differential evolution algorithm with strategy adaptation for global numerical optimization [J]. IEEE Transactions on Evolutionary Computation, 2009, 13(2): 398–417.

    Article  Google Scholar 

  17. PAN Q K, SUGANTHAN P N, WANG L, GAO L. A differential evolution algorithm with self-adapting strategy and control parameters [J]. Computers & Operations Research, 2011, 38(1): 394–408

    Article  MATH  MathSciNet  Google Scholar 

  18. ZHANG Ling-yun, LIU Hong-fu. The application of ABP method in high-density resistivity method inversion [J]. Chinese J Geophys, 2011, 54(1): 227–233. (in Chinese)

    Article  Google Scholar 

  19. TANG Jing-tian, WANG Feri-yan, REN Zheng-yong, GUO Rong-wen. 3-D direct current resistivity forward modeling by adaptive multigrid finite element method [J]. Journal of Central South University of Technology, 2010, 17(3): 587–592.

    Article  Google Scholar 

  20. STORN R, PRICE K V. Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces [J]. Journal of Global Optimization, 1997, 11(4): 341–359

    Article  MATH  MathSciNet  Google Scholar 

  21. PRICE K V. Differential evolution: A fast and simple numerical optimizer [C]//Fuzzy Information Processing Society, Biennial Conference of the North American, Berkeley: IEEE Computer Society, 1996: 524–527

    Chapter  Google Scholar 

  22. YUAN X, CAO B, YANG B, YUAN Y. Hydrothermal scheduling using chaotic hybrid differential evolution [J]. Energy Conversion and Management, 2008, 49(12): 3627–3633

    Article  Google Scholar 

  23. LU Y, ZHOU J, QIN H, WANG Y, ZHANG Y. Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects [J]. Engineering Applications of Artificial Intelligence, 2011, 24(2): 378–387.

    Article  Google Scholar 

  24. COELHO L S. Reliability-redundancy optimization by means of a chaotic differential evolution approach [J]. Chaos, Solitons & Fractals, 2009, 41(2): 594–602.

    Article  MATH  Google Scholar 

  25. HE D, DONG G, WANG F, MAO Z. Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms [J]. Energy Conversion and Management, 2011, 52(2): 1026–1032.

    Article  Google Scholar 

  26. ZAHARIE D. Critical values for the control parameters of differential evolution algorithms [C]//Proc of 8th International Conference on Soft Computing. Bruno: MENDEL Proceeding Society, 2002: 62–67.

    Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals of Ministry of Education (Central South University), Changsha, 410083, China

    Qian-wei Dai  (戴前伟) & Fei-bo Jiang  (江沸菠)

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

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

Authors
  1. Qian-wei Dai  (戴前伟)
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  2. Fei-bo Jiang  (江沸菠)
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  3. Li Dong  (董莉)
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Corresponding author

Correspondence to Fei-bo Jiang  (江沸菠).

Additional information

Foundation item: Project(20120162110015) supported by the Research Fund for the Doctoral Program of Higher Education, China; Project(41004053) supported by the National Natural Science Foundation of China; Project(12c0241) supported by Scientific Research Fund of Hunan Provincial Education Department, China

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Dai, Qw., Jiang, Fb. & Dong, L. Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm. J. Cent. South Univ. 21, 2018–2025 (2014). https://doi.org/10.1007/s11771-014-2151-9

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  • DOI: https://doi.org/10.1007/s11771-014-2151-9

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