Abstract
The inefficiencies and uncertainties surrounding solutions from existing inversion methods have necessitated investigation for more efficient techniques for the inversion of ill-posed magnetic problems. In this study, the Social Spider Optimization (SSO) algorithm has been modified, adopted and successfully used in modelling physical characteristics of magnetic anomalies originating from simple-shaped geologic structures. The study, aimed at testing the capacity and efficiency of the SSO algorithm to model magnetic data of varying complexity, was successfully conducted on both synthetic data with varying levels of noise and real field data obtained from mining fields in Senegal and Egypt. To assess the mathematical nature of the inverse problem considered, error energy maps were produced for each model parameter pairs in the synthetic examples. These maps enabled the pre-assessment of the resolvability model parameter for the ill-posed problem. In addition, uncertainty analysis aimed at providing insight to the reliability of the obtained solutions was carried out using the Metropolis-Hastings (M-H) sampling algorithm. Results show that the procedure converges fast and generates accurate results even when confronted with constrained multi-parameter non-linear inversion problems. Its outstanding converging speed and accuracy of the results reveal it as an excellent procedure for overcoming agelong problems of local optimal solutions associated with pre-existing algorithms. The consistency of the results with actual values affirms the efficacy of the new procedure which is pioneering in geophysical literature. It is therefore a stable and efficient tool for performing geophysical data inversion and is therefore recommended for use in inverting geophysical data with higher complexities like seismic reflection and gravity data, that require many corrections to be performed before reliable geological interpretations can be made.