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Application of Differential Evolution Algorithm on Self-Potential Data

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  • Xiangtao Li
  • Minghao Yin

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.

Suggested Citation

  • Xiangtao Li & Minghao Yin, 2012. "Application of Differential Evolution Algorithm on Self-Potential Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0051199
    DOI: 10.1371/journal.pone.0051199
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    Cited by:

    1. Amirhessam Tahmassebi & Amir H Gandomi & Simon Fong & Anke Meyer-Baese & Simon Y Foo, 2018. "Multi-stage optimization of a deep model: A case study on ground motion modeling," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-23, September.
    2. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.

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