Memory Based Hybrid Dragonfly Algorithm (MHDA): a New Technique for Determining Model Parameter in Vertical Electrical Sounding (VES) Data

Abstract

Abstract Vertical Electrical Sounding (VES) data inversion is a nonlinear inversion problem because several models can fit to the observed data. Therefore, a new approach based on nonlinear optimization technique is implemented which is called Memory based Hybrid Dragonfly Algorithm (MHDA). It is proposed to solve drawback of Dragonfly Algorithm (DA), i.e. low convergence rate which is caused by high exploration behaviour of DA. The drawback can lead to the local optimum solutions. MHDA successfully balances exploration and exploitation behaviours of DA to obtain global optimum solution. In this research, initially, MHDA is tested for the noise contaminated synthetic VES data to assess its performance. Subsequently, MHDA is applied for the field VES data. In both results, MHDA is able to provide Posterior Distribution Model (PDM) which is obtained from exploration process. All accepted models of PDM have lower misfit value than specified tolerance value in the inversion process. The PDM can be used to estimate solution via median value of PDM. Additionally, the uncertainty estimation of obtained solution can be determined from standard deviation value of PDM. The inversion results of synthetic and field VES data indicate that MHDA is an innovative technique to solve VES data inversion problem.