Recursive ant colony optimization: a new technique for the estimation of function parameters from geophysical field data

Abstract

We introduce Recursive Ant Colony Optimization (RACO), a new technique for the estimation of function parameters from field data obtained from various geophysical surveys. RACO is a modified form of the ant colony method (ACO) and can be used to determine the best solution for various geophysical problems. As RACO is an extension of ACO, it simulates the social behaviour of ants, optimizing their path from the nest to the food source. RACO applies ACO recursively, introducing an additional term ‘depth’ that decides the extent of the recursion. The results of each depth contribute towards the construction of models for the following depth and the range of values for each parameter is condensed around the actual solution. The algorithm is tested on a two parameter mathematical function and to further test its efficiency and stability on real world problems, we applied it on a few geophysical inversion problems. We show its application on synthetic data sets for the self‐potential anomaly (noise free and 10% noise) caused in the case of an inclined sheet like a body buried inside the earth, synthetic 1D vertical electrical sounding (VES) geo‐electric data sets for three‐layer and five‐layer earth models, a real 1D VES data set obtained from the Tangasol region, India and P‐wave logs for two sites located in the Krishna‐Godavari (KG) Basin, India. The results generated by RACO inversions are found to be in good agreement with previous studies conducted using these data sets.