International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Deep learning classifier for the number of layers in the subsurface structure

  • Kim, Ho-Chan (Department of Electrical Engineering, Jeju National University) ;
  • Kang, Min-Jae (Department of Electronic Engineering, Jeju National University)
  • Received : 2021.07.05
  • Accepted : 2021.07.13
  • Published : 2021.09.30
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Abstract

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

Keywords

Acknowledgement

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF-2018R1D1A1B07045976) in (2018).

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