Estimating model parameters from SP anomaly of sheet-shaped sources using Differential Search and Particle Swarm Optimization Algorithms

In this study, the efficiency of estimating the model parameters of sheet-shaped single and multiple-sources of the self-potential (SP) anomaly using the Differential Search algorithm (DSA) was investigated. First of all, noise-free and noisy synthetic anomalies were calculated for a single sheet-shaped source, and its model parameters were estimated by DSA. The DSA inversion was also done for a model consisting of three inclined sheets. In order to test the effectiveness of the method, the same processes were repeated with a more conventional algorithm, PSO, and the solutions of both methods were compared. The results of synthetic anomaly analyses have shown that DSA can predict the parameters as accurately as PSO. Then, both algorithms were also applied to two field SP anomalies (Surda and Beldih) that have been evaluated by different algorithms in the literature. The source of the Surda anomaly is modelled as one sheet, whereas the source model of the Beldih anomaly is assumed to consist of three sheets. The five model parameters for each model were estimated using both algorithms and it is determined that they are in good agreement with the findings of the previous studies. The contribution of the regional background anomaly to the synthetic and field anomalies was also included and regional coefficients were estimated. Finally, it can be concluded that DSA can solve the source parameters without the need for the initial values required in conventional iterative inversion methods and is an efficient and promising algorithm for determining the parameters of SP sources.