Particle swarm optimization is developed to estimate the model parameters of a 2.5-D sedimentary basin. PSO have been implemented on synthetic data and two field data. An observation has been made that PSO is affected by some levels of noise, but estimated depths are close to the true depths. The PSO results are well correlated with borehole samples and provide more geological viability than Marquardt results. Despite its long computation time, it is very simple to implement.

A new technique, tuned PSO, is developed for optimal convergence, avoiding traps of local minima and computing the model parameters: amplitude coefficient factor, shape factor and depth. A number of exercises were done to select the PSO learning parameters. The applicability and efficacy of the proposed method is implemented on synthetic and field gravity anomalies. The analysed results were compared with published results by other methods that show a significant agreement with the real model.

Presented work allows estimation of buried source without any prior knowledge of underlying formation. This paper deals the application of continuous wavelet transform (CWT) and Euler deconvolution methods to estimate the source depth using magnetic anomalies. These methods are utilized mainly to focus on the fundamental issue for mapping the major coal seam and locating magnetic lineaments over Jharia coalfield, India.