• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.454-459
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• 2012

A kernel function of apparent earth resistivity can be estimated using the apparent earth resistivity measured with Wenner's 4 point method. It becomes to solve a nonlinear system to estimate the kernel function of apparent earth resistivity. However it is not simple to get solution of nonlinear system with many unknown variables. This paper suggests the method of estimating kernel function by linearizing this nonlinear system. Finally, various examples of earth structure have been simulated to evaluate the proposed method in this paper.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.268-273
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• 2019

The apparent soil resistivity is used for estimating multilayer soil parameters, such as, layer's depth and soil resistivity. The soil parameters are estimated by continuously revising those parameters until the error between the measured and calculated apparent soil resistivity reaches to allowable level. The equation for calculating the apparent soil resistivity is complicated and time consumed, because it is composed of an infinite integral which includes a zero order Bessel's function of the first kind. In this paper, a fast algorithm for calculating the apparent soil resistivity of horizontal multilayer earth structure is proposed using exponential sampling method.

• Journal of IKEEE
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• v.23 no.1
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• pp.318-321
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• 2019

The apparent soil resistivity is used for estimating multilayer soil parameters, such as, layer's depth and soil resistivity. The apparent soil resistivity can be measured, and also can be calculated if soil parameters are given, becacuse the apparent soil resistivity is a function of these parameters. Therefore, any optimization algorithms can be used to find these parameters which make the calculated apparent soil resistivity close to the measured one. The equation for calculating the apparent soil resistivity is complicated and time consumed, because it is composed of an infinite integral which includes a zero order Bessel's function of the first kind. In this paper, a fast algorithm for calculating the apparent soil resistivity of horizontal multilayer earth structure has been presented using complex image method.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.97-104
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• 2020

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.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.51-58
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• 2021

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.

• Economic and Environmental Geology
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• v.17 no.2
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• pp.101-107
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• 1984

This paper describes dipole-dipole apparent resistivity responses from near-surface cavities in otherwise homogeneous earth materials. In applying the dipole-dipole resistivity method to the problem of locating and delineating subsurface cavities, it is important to know apparent resistivity responses not only for conductive bodies but also for resistive ones. Dipole-dipole apparent resistivities for these bodies are calculated by the numerical modeling technique using an integral equation solution. The magnitude and pattern of apparent resistivity is highly dependent on the ratio of body resistivity to background resistivity. In conductive bodies, the largest anomaly of apparent resistivity appears at the outside of the body. In resistive bodies, however, the position of the largest anomaly coincides with the location of the body. The field results gathered at Okinawa, Japan in 1978 showed that peak anomalies occurred at the locations of air-filled cavities.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.350-356
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• 2005

Earth equipments are essential to protect humans and other types of equipment from abnormal conditions. Earth resistance and potential must be restricted within a low value. An estimation algorithm of earth parameters and equivalent resistivity is introduced to calculate reliable earth resistance in this research. The proposed algorithm is based on the relationship between apparent resistances and earth parameters. The proposed algorithm, which approximates the non-linear characteristics of earth by using the Artificial Neural Network (ANN), estimates the earth parameters and equivalent resistivity. The effectiveness of the proposed method is verified with case studies.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.33-40
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• 2009

In field surveys using the dipole-dipole electrical resistivity method, we often encounter negative apparent resistivity. The term 'negative apparent resistivity' refers to apparent resistivity values with the opposite sign to surrounding data in a pseudosection. Because these negative apparent resistivity values have been regarded as measurement errors, we have discarded the negative apparent resistivity data. Some people have even used negative apparent resistivity data in an inversion process, by taking absolute values of the data. Our field experiments lead us to believe that the main cause for negative apparent resistivity is neither measurement errors nor the influence of self potentials. Furthermore, we also believe that it is not caused by the effects of induced polarization. One possible cause for negative apparent resistivity is the subsurface geological structure. In this study, we provide some numerical examples showing that negative apparent resistivity can arise from geological structures. In numerical examples, we simulate field data using a 3D numerical modelling algorithm, and then extract 2D sections. Our numerical experiments demonstrate that the negative apparent resistivity can be caused by geological structures modelled by U-shaped and crescent-shaped conductive models. Negative apparent resistivity usually occurs when potentials increase with distance from the current electrodes. By plotting the voltage-electrode position curves, we could confirm that when the voltage curves intersect each other, negative apparent resistivity appears. These numerical examples suggest that when we observe negative apparent resistivity in field surveys, we should consider the possibility that the negative apparent resistivity has been caused by geological structure.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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• pp.76-82
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• 2002

This paper presents the method of sail-resistivity estimation using the backpropagation(BP) neural network. Existing estimation programs are expensive, and their estimation methods need complex techniques and take much time. Also, those programs have not become well spreaded in Korea yet. Soil resistivity estimation method using BP algorithm has studied for the reason mentioned above. This paper suggests the method which differs from expensive program or graphic technology requiring many input stages, complicated calculation and professional knowledge. The equivalent earth resistivity can be presented immediately after inputting apparent resistivity through the personal computer with a simplified Program without many Processing stages. This program has the advantages of reasonable accuracy, rapid processing time and confident of anti users.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.186-191
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• 2013

Earth equipments are essential to protect ITS facilities from abnormal situation. In this research, an estimation algorithm of earth parameters and equivalent resistivity is introduced. Traditional estimation methods can be divided into graphic method and numerical method. The result of graphic method is varied by the ability of expert or repeated calculation and it is hard to estimate the parameters precisely. The numerical method requires special techniques such as optimizing theory, and numerous calculations, whose results can be varied with initial values. The proposed algorithm is based on the relationship between apparent resistances and earth parameters and approximates the nonlinear characteristics of earth using ANN(artificial neural networks). The effectiveness of proposed method is verified in case studies.