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
DINES K A, LYTLE R J. Computerized geophysical tomography [J]. Proceedings of the IEEE, 1979, 67(7): 1065–1073.
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.
SHIMA H. 2-D and 3-D resistivity image reconstruction using crosshole data [J]. Geophysics, 1992, 57(10): 1270–1281.
LOKE M H, BARKER R D. Least-squares deconvolution of apparent resistivity pseudosections [J]. Geophysics, 1995, 60(6): 1682–1689.
ZOHDY A A R. A new method for the automatic interpretation of Schlumbeger and Wenner sounding curves [J]. Geophysics, 1989, 54(2): 245–253.
EL-QADY G, KEISUKE U. Inversion of DC resistivity data using neural network [J]. Geophysical Prospecting, 2001, 49(4): 417–430.
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.
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.
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)
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.
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.
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.
SUBUDHI B, JENA D. A differential evolution based neural network approach to nonlinear system identification [J]. Applied Soft Computing, 2011, 11(1): 861–871.
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.
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.
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.
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
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)
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.
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
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
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
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.
COELHO L S. Reliability-redundancy optimization by means of a chaotic differential evolution approach [J]. Chaos, Solitons & Fractals, 2009, 41(2): 594–602.
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.
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.
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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