Title: Geoelectrical data processing using neuro fuzzy pattern recognition scheme for unambiguous subsurface modelling

Authors: A. Stanley Raj; D. Hudson Oliver; Y. Srinivas

Addresses: Department of Physics, Loyola College, Nungambakkam, Chennai 600 034, India ' Department of Physics, Scott Christian College, Nagercoil, India ' Centre for GeoTechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu-627012, India

Abstract: Geoinversion takes off different forms to assess the subsurface formations more noticeably. The evolution of soft computing inversion technique makes the geoinversion to the next level of modelling parameters. In this research work, the novel neuro fuzzy pattern recognition approach was introduced to solve the non-linearity involved in geoelectrical resistivity data for appraising the subsurface parameters. The novelty encompasses in generating the patterns using Artificial Neural Networks (ANN) for the geoelectrical resistivity data obtained from the Vertical Electrical Sounding (VES) data as well as the pattern recognition done by the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm is predominant in mitigating the near world truth information that is available. Moreover, the ambiguities of the principle of equivalence have been reduced further by incorporating the Dar-Zarrouk parameter evaluation of longitudinal conductance and transverse resistivity. Thus, this tool could be a good alternate for any conventional algorithm for unravelling such complex problems.

Keywords: resistivity inversion; vertical electrical sounding; VES; adaptive neuro fuzzy inference system; ANFIS; Dar-Zarrouk parameters; soft computing.

DOI: 10.1504/IJHST.2017.087927

International Journal of Hydrology Science and Technology, 2017 Vol.7 No.4, pp.364 - 389

Received: 04 Jan 2016
Accepted: 27 Sep 2016
Published online: 13 Nov 2017 *

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