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International Journal of Geophysics
Volume 2015 (2015), Article ID 134834, 11 pages
Research Article

Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model

1Department of Physics, Vel Tech University, Avadi, Chennai 600062, India
2Department of Physics, Senthamarai College of Arts and Science, Palkalai Nagar, Madurai 625021, India
3Centre for Geotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, India

Received 25 July 2014; Accepted 19 January 2015

Academic Editor: Robert Tenzer

Copyright © 2015 A. Stanley Raj et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzy -means clustering, fuzzy -means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.