DOI QR코드

DOI QR Code

Estimation of kernel function using the measured apparent earth resistivity

  • Kim, Ho-Chan (Department of Electrical Engineering, Jeju National University) ;
  • Boo, Chang-Jin (Department of Electrical Engineering, Jeju International University) ;
  • Kang, Min-Jae (Department of Electronic Engineering, Jeju National University)
  • Received : 2020.06.25
  • Accepted : 2020.07.08
  • Published : 2020.09.30

Abstract

In this paper, we propose a method to derive the kernel function directly from the measured apparent earth resistivity. At this time, the kernel function is obtained through the process of solving a nonlinear system. Nonlinear systems with many variables are difficult to solve. This paper also introduces a method for converting nonlinear derived systems to linear systems. The kernel function is a function of the depth and resistance of the Earth's layer. Being able to derive an accurate kernel function means that we can estimate the earth parameters i.e. layer depth and resistivity. We also use various Earth models as simulation examples to validate the proposed method.

Keywords

References

  1. A. I. Aderibigbe, Isaac Samuel, B. Adetokun and S. Tobi, "Monte Carlo Simulation Approach to Soil Layer Resistivity Modelling for Grounding System," International Journal of Applied Engineering Research, vol. 12, pp. 13759-13766, 2017. Orcid: 0000-0002-9998-549X
  2. B. Zhang, X. Cui, L. Li, and J. L. He, "Parameter estimation of horizontal multilayer earth by complex image method," IEEE Trans. on Power Delivery, vol. 20, pp. 1394-1401, 2005. DOI: 10.1109/TPWRD.2004.834673
  3. U.K. Singh, R.K. Tiwari and S. B. Singh, "One-dimensional of geo-electrical resisitivity sounding data using artificial neural networks- a case study," Computer & Geoscience, vol. 31, pp. 99-108, 2005. https://doi.org/10.1016/j.cageo.2004.09.014
  4. K. H. Jin, M. T. Mccann, E. Froustey, and M. Unser, "Deep convolutional neural network for inverse problems in imaging," IEEE Trans. Image Process, vol. 26, no. 9, pp. 4509-4522, Sep. 2017. DOI: 10.1109/TIP.2017.2713099
  5. G. Cheng, C. Yang, X. Yao, L. Guo, and J. Han, "When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs," IEEE Trans. Geosci. Remote Sens., vol. 56, no. 5, pp. 2811-2821, 2018. DOI: 10.1109/TGRS.2017.2783902
  6. T. Takahashi and T. Kawase, "Analysis of apparent resistivity in a multi-layer earth structure," IEEE Trans. on Power Delivery, vol. 5, pp. 604-612, 1990. DOI: 10.1109/61.53062
  7. F. Dawalibi, "Earth resistivity measurement interpretation techniques," IEEE Trans. on Power Apparatus Systems, vol. 103, pp. 374-382, 1984. DOI: 10.1109/TPAS.1984.318254
  8. J. C. Egbai, "Analysis of kernel function by the transformation of field data", Advanced in Applied Science Research, vol. 3, pp. 508-519, 2012.
  9. S. C. Chapra, Applied Numerical Methods with MATLAB for Engineers and Scientists, 2nd ed. New York: McGrow-Hill, pp. 270-276, 2008.