Application of Artificial Neural Network for the Inversion of Electrical Resistivity Data

Johnson, O. L. and Aizebeokhai, A. P. (2017) Application of Artificial Neural Network for the Inversion of Electrical Resistivity Data. Journal of Informatics and Mathematical Sciences, 9 (2). pp. 297-316. ISSN 0975-5748 (online); 0974-875X (print)

PDF Download (1MB)

Abstract

The inversion of most geophysical data sets is complex due to the inherent non-linearity of the inverse problem. This usually leads to non-uniqueness of solutions to the inverse problem. Artificial neural network (ANN) has been used effectively to address several non-linear and non-stationary inverse problems. This study is essentially an assessment of the effectiveness of estimating subsurface resistivity model parameters from apparent resistivity measurements using ANN. Multi-layered earth models for different geologic environments were used to generate synthetic apparent resistivity data. The synthetic apparent resistivity data were generated using linear filter method embedded in the RES1D program. Neural network toolbox on MATLAB was used to design, train and test a developed neural network that was employed in the inversion of the apparent resistivity sounding data sets. Resilient feed-forward back propagation algorithm was used to train the network. The network was trained with 50% of the synthetic apparent resistivity data sets and their corresponding multi-layered earth models. 25% of the data set was used to test the network and the network was validated with another 25% of the data set. The network was then used to invert field data obtained from Iyanna- Iyesi, southwestern Nigeria. The results obtained from ANN responses were compared with that of a conventional geoelectrical resistivity inversion program (WINRESIST); the results indicate that ANN is effective in the inversion of geoelectrical resistivity sounding data for multi-layered earth models.

Item Type:	Article
Uncontrolled Keywords:	Artificial Neural Network (ANN)
Subjects:	Q Science > QC Physics
Divisions:	Faculty of Engineering, Science and Mathematics > School of Physics
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	12 Apr 2018 11:59
Last Modified:	12 Apr 2018 11:59
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/10582

Actions (login required)

View Item