Application of Differential Evolution Algorithm on Self-Potential Data

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods.

[1]  E. Pekșen,et al.  Application of particle swarm optimization on self-potential data , 2011 .

[2]  M. K. Paul.  DIRECT INTERPRETATION OF SELF‐POTENTIAL ANOMALIES CAUSED BY INCLINED SHEETS OF INFINITE HORIZONTAL EXTENSIONS , 1965 .

[3]  Wenyin Gong,et al.  Enhancing the performance of differential evolution using orthogonal design method , 2008, Appl. Math. Comput..

[4]  El-Sayed M. Abdelrahman,et al.  New methods for shape and depth determinations from SP data , 2003 .

[5]  S. Reddy,et al.  Interpretation of self-potential anomalies of some simple geometric bodies , 1970 .

[6]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[7]  M. Goldie.  Self‐potentials associated with the Yanacocha high‐sulfidation gold deposit in Peru , 2002 .

[8]  Mohamed Sultan,et al.  New least-squares algorithm for model parameters estimation using self-potential anomalies , 2008, Comput. Geosci..

[9]  H. El-Araby,et al.  A new method for complete quantitative interpretation of self-potential anomalies , 2004 .

[10]  El-Sayed M. Abdelrahman,et al.  Shape and depth solutions from numerical horizontal self-potential gradients , 1997 .

[11]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[12]  Hui Li,et al.  An effective method for accurate prediction of the first hyperpolarizability of alkalides , 2012, J. Comput. Chem..

[13]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[14]  B. N. P. Agarwal.  Quantitative Interpretation of Self-potential Anomalies , 1984 .

[15]  P. Meiser,et al.  A METHOD FOR QUANTITATIVE INTERPRETATION OF SELFPOTENTIAL MEASUREMENTS , 1962 .

[16]  The Self-Potential (SP) Method: an Inexpensive Reconnaissance and Archaeological Mapping Tool , 1984 .

[17]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[18]  Minghao Yin,et al.  Hybrid Differential Evolution with Biogeography-Based Optimization for Design of a Reconfigurable Antenna Array with Discrete Phase Shifters , 2011 .

[19]  E. Abdelrahman,et al.  Shape and depth determinations from second moving average residual self-potential anomalies , 2009 .

[20]  Ville Tirronen,et al.  Scale factor local search in differential evolution , 2009, Memetic Comput..

[21]  B. Murty,et al.  Nomogram for the complete interpretation of spontaneous potential profiles over sheet-like and cylindrical two-dimensional sources , 1985 .

[22]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[23]  Sulhi Hasan Yungul,et al.  Interpretation of spontaneous polarization anomalies caused by spheroidal ore bodies , 1950 .

[24]  C. Mendonça.  Forward and inverse self-potential modeling in mineral exploration , 2008 .

[25]  B. B. Bhattacharya,et al.  A NOTE ON THE USE OF A NOMOGRAM FOR SELF‐POTENTIAL ANOMALIES* , 1981 .

[26]  Burke J. Minsley,et al.  Minimization of self-potential survey mis-ties acquired with multiple reference locations , 2008 .

[27]  R. Corwin,et al.  The self-potential method in geothermal exploration , 1979 .

[28]  Wenyin Gong,et al.  DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization , 2010, Soft Comput..

[29]  A. A. Ammar,et al.  A Least-squares Approach to Shape Determination from Residual Self-potential Anomalies , 1997 .