Open AccessGenetic Algorithm Coupled with Neural Networks to Guesstimate the Subsurface Features of the Earth
Author(s) -
Abhishek Raj,
Y. Srinivas,
R. Damodharan,
B. Chendhoor,
Manorama Vimal
Publication year - 2020
Publication title -
journal of model based research
Language(s) - English
Resource type - Journals
ISSN - 2643-2811
DOI - 10.14302/issn.2643-2811.jmbr-20-3449
Subject(s) - artificial neural network , genetic algorithm , algorithm , inversion (geology) , computer science , vertical electrical sounding , earth (classical element) , field (mathematics) , soft computing , geophysics , artificial intelligence , machine learning , geology , mathematics , groundwater , paleontology , geotechnical engineering , structural basin , aquifer , pure mathematics , mathematical physics
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.