In this paper, we present a compilation of the different radiation transport codes for the Martian surface that are currently used by various space agencies and institutions. In addition, as the execution of the tasks necessary to process all of these radiation data requires a high computational processing capacity, we link it to cloud computing, which is found to be an appropriate tool regarding the required resources.

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.