Depth and shape factor estimation of gravity anomalies with simple geometery using Particle Swarm Optimization algorithm

One of the most important geophysical problems in exploration of mineral deposits is to estimate the depth of buried structure using observed gravity data. In this paper, we are trying to estimate mass anomaly depth by using one of the intelligence methods as Particle Swarm Optimization (PSO) with simple shapes as sphere, horizontal and vertical cylinder. In this modeling, two parameters of depth (z) and shape factor (q) were considered as particles and the maximum and minimum depth were used as the prior information. The method is tested for synthetic models with random noise. The method gives precise results for synthetic models contaminated with random noise which is quite acceptable and promising. This technique was also successfully applied to real data for mineral exploration. The applied real data belongs to an area with hilly topography located in the Fars province close to the Abadeh city where the barite deposit is under exploration. The method is used for a profile of real data that is provided from the residual anomalies and passed from the main detected positive anomaly in the area. The estimated depth from this method was 8.15 m which was in good agreement with the results obtained through Euler method and also outcrop‐scale observations.