Gravity data interpretation using the particle swarm optimisation method with application to mineral exploration

This paper describes a new method based on the particle swarm optimisation (PSO) technique for interpreting the second moving average (SMA) residual gravity anomalies. The SMA anomalies are deduced from the measured gravity data to eradicate the regional anomaly by utilising filters of consecutive window lengths (s-value). The buried structural parameters are the amplitude factor (A), depth (z), location (d) and shape (q) that are estimated from the PSO method. The discrepancy between the measured and the predictable gravity anomaly is estimated by the root mean square error. The PSO method is applied to two different theoretical and three real data sets from Cuba, Canada and India. The model parameters inferred from the method developed here are compared with the available geological and geophysical information.