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http://dx.doi.org/10.7471/ikeee.2020.24.4.982

Estimating aquifer location using deep neural network with electrical impedance tomography  

Sharma, Sunam Kumar (Department of Electronic Engineering, Jeju National University)
Khambampati, Anil Kumar (Department of Electronic Engineering, Jeju National University)
Kim, Kyung Youn (Department of Electronic Engineering, Jeju National University)
Publication Information
Journal of IKEEE / v.24, no.4, 2020 , pp. 982-990 More about this Journal
Abstract
Groundwater is essential source of the freshwater. Groundwater is stored in the body of the rocks or sediments, called aquifer. Finding an aquifer is a very important part of the geophysical survey. The best method to find the aquifer is to make a borehole. Single borehole is not a suitable method if the aquifer is not located in the borehole drilled area. To overcome this problem, a cross borehole method is used. Using a cross borehole method, we can estimate aquifer location more precisely. Electrical impedance tomography is use to estimate the aquifer location inside the subsurface using the cross borehole method. Electrodes are placed inside each boreholes and area between these boreholes are analysed. An aquifer is a non-uniform structure with complex shape which can represented by the truncated Fourier series. Deep neural network is evaluated as an inverse problem solver for estimating the aquifer boundary coefficients.
Keywords
Deep neural network; electrical impedance tomography; aquifer; estimation; inverse problem; Fourier coefficient;
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