Abstract
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.
Highlights
The self-potential (SP) method is one of the most important optimization problems in geophysics, which is on the basis of the measurement of natural occurring potential difference generated chief from electrochemical, electro kinetic and thermoelectric sources
The SP method has been used in a wide range of applications in geophysics, including prospecting purposes [1,2], archaeology [3], engineering problem [4], cave detection [5,6] and geothermal exploration [7,8]
The software is written in Matlab-7 language and experiments are made on a Pentium 3.0 GHz Processor with 1.0 GB of memory
Summary
The self-potential (SP) method is one of the most important optimization problems in geophysics, which is on the basis of the measurement of natural occurring potential difference generated chief from electrochemical, electro kinetic and thermoelectric sources. We have viewed different kinds of methods advanced to handle the SP problem: the method of characteristic points [9]; the curve matching method [10,11]; the method of least square approaches [12,13]; the method of derivative analysis and gradients [14]. Other techniques have been used on such as a numerical gradient method, direct interpretation. These techniques can be categorized into two parts: derivative-based method and global search method
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