A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

Abstract

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

Optimising noise intervened data processes for inverse geoelectrical problem using adaptive neuro fuzzy inference system (ANFIS)

“…The model has a unique feature in which, it can express linguistically the characteristics of a complex non-linear system. Geoelectrical resistivity inversion problem was analysed using the ANFIS model, which produces less mean square error (Srinivas et al 2012a(Srinivas et al , 2012bStanley Raj et al 2014, 2015. The fuzzy modelling was first explored by Takagi and Sugeno (1985) and later ANFIS network was developed by Jang (1993).…”

BP Neural Network and improved Particle Swarm Optimization for Transient Electromagnetic Inversion

“…Jiang et al (2016b) presented a pruning Bayesian neural network (PBNN) method for resistivity imaging and solved the instability, local minimization problems. Raj et al (2014) solved non-linear apparent resistivity inversion problems with ANN. The ANN has been widely applied in electric prospecting data interpretation for its powerful fitting ability.…”

Transient electromagnetic inversion based on particle swarm optimization and differential evolution algorithm

“…In recent years, global stochastic optimization methods have become popular for geophysical inversion and have attracted great attention due to their versatility as search methods (e.g. Tran and Hiltunen, 2012;Raj et al, 2014;Wu et al, 2018). Compared with classical deterministic inversion meth-ods (Farquharson and Oldenburg, 1993;Auken et al, 2014), these methods offer many benefits, including their zero-order characteristics (i.e.…”