a novel and proficient algorithm for the inversion of geoelectrical resistivity data using adaptive neuro fuzzy inference system (anfis)

Research Article
Y.Srinivas, A.Stanley Raj* , D.Hudson Oliver , D.Muthuraj , N.Chandrasekar
ANFIS, Vertical electrical sounding, Resistivity inversion, Layer model

Electrical Resistivity Method is one of the Geophysical techniques used to investigate the nature of the subsurface formations of earth. Generally, this kind of non-linear parameter estimation problem needs high computation to obtain the probable model. Moreover the error performance and computational time are more important while interpreting the model parameters of the subsurface viz., true resistivity and depth. But recent development in computational techniques paves way for producing approximate solutions to the non linear problem that are very much resembling the true nature of the earth. One of the most emerging soft computing techniques is Adaptive Neuro Fuzzy Inference System (ANFIS) in which the concepts of Artificial Neural Networks and Fuzzy logic have been integrated. This integrated concept helps the algorithm to generate more synthetic data to obtain best fit model on the basis of minimizing the root mean square error percent. In this paper, Vertical Electrical Sounding (VES) data has been interpreted by newly proposed efficient algorithm supported by ANFIS to identify the subsurface strata of the earth. The inverted results have been correlated with available lithologs and found to be correlating very well. Thus this paper projects a different approach in interpreting the geoelectrical resistivity data using ANFIS. In this novel and generalized algorithm, the interpretation of the vertical electrical sounding has done successfully with more accurate layer model and is represented as Graphical User Interface (GUI).