Genetic Algorithm Coupled with Neural Networks to Guesstimate the Subsurface Features of the Earth

Electrical resistivity method is often used to estimate the subsurface structure of the earth. Many inversion algorithms are available to estimate the subsurface features. However, predicting the exact parameter in the non-linear subsurface of the earth is difficult because of its complex composition. Soft computing tools can approximate the subsurface parameters more clearly. Each soft computing tool has certain advantages and disadvantages. A hybrid formation of algorithms will make the decision more appropriate than depending on a single tool. Here in our study the data obtained through Vertical Electrical Sounding has been used to determine the sub surface characteristics of earth viz., true resistivity and thickness. Artificial Neural Networks (ANN) requires certain optimizing procedures. Here in this paper, Genetic Algorithm (GA) is applied to optimize Artificial Neural Networks (ANN). This coupled approach is tested with the field data. Error percentage of algorithm nearly mimics the behavior of earth and is verified. The best performance result shows that this technique can be implemented to estimate the non-linear characteristics of the earth more noticeably.