Ben Ubong C, Akpan Anthony E, Urang Job Gideon, Akaerue Emmanuel I, Obianwu Victor I
Applied Geophysics Programme, University of Calabar, Calabar, Cross River State, Nigeria.
Heliyon. 2022 Mar 2;8(3):e09027. doi: 10.1016/j.heliyon.2022.e09027. eCollection 2022 Mar.
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.
现有反演方法的解决方案存在效率低下和不确定性问题，因此有必要研究更有效的不适定磁问题反演技术。在本研究中，对社会蜘蛛优化（SSO）算法进行了改进、采用，并成功用于模拟源自简单形状地质结构的磁异常的物理特征。该研究旨在测试SSO算法对不同复杂程度磁数据建模的能力和效率，在具有不同噪声水平的合成数据以及从塞内加尔和埃及的矿区获得的实际野外数据上均成功进行。为了评估所考虑反问题的数学性质，针对合成示例中的每个模型参数对生成了误差能量图。这些图能够对不适定问题的可分辨性模型参数进行预评估。此外，使用Metropolis-Hastings（M-H）采样算法进行了不确定性分析，旨在深入了解所得解的可靠性。结果表明，即使面对受约束的多参数非线性反演问题，该过程收敛速度快且能产生准确结果。其出色的收敛速度和结果准确性表明它是克服与现有算法相关的长期局部最优解问题的优秀方法。结果与实际值的一致性证实了这一在地球物理文献中首创的新方法的有效性。因此，它是进行地球物理数据反演的稳定且高效的工具，因此推荐用于反演具有更高复杂性的地球物理数据，如地震反射和重力数据，这些数据在进行可靠的地质解释之前需要进行许多校正。
