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http://dx.doi.org/10.7471/ikeee.2021.25.4.650

A study on estimating the interlayer boundary of the subsurface using a artificial 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.25, no.4, 2021 , pp. 650-663 More about this Journal
Abstract
Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.
Keywords
Electrical impedance tomography; artificial neural network; front points; subsurface; interlayer estimation;
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