CWT was applied for lithology detection over the Costa Rica margin, using gamma ray logs as a best indicator. It is observed that four major sedimentary layers terminated there with a gabbroic sill. DWT was applied on density logs to identify small changes in the rock matrix. Accordingly, wavelet-based fractal analysis was applied on every sedimentary layer. This analysis indicates that a certain portion of wells contain abundant shale and low-energy environments.

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.