Essa Khalid S, Abo-Ezz Eid R, Géraud Yves, Diraison Marc, Toushmalani Reza
Geophysics Department, Faculty of Science, Cairo University, Giza, P.O. 12613, Egypt.
GeoRessources Laboratory, University of Lorraine, Nancy, 54500, France.
Heliyon. 2024 May 16;10(10):e31391. doi: 10.1016/j.heliyon.2024.e31391. eCollection 2024 May 30.
The interpretation of gravity anomalies is crucial for identifying subsurface mineralized targets and understanding the density variations between the targets and the surrounding structures. To confirm the presence of ore and mineral targets, simple geometric bodies are often used. One of the commonly used global metaheuristic algorithms for gravity data analysis is the particle optimization algorithm. In this study, we employed this method to determine the parameters of buried bodies that resemble finite vertical cylinders by inferring gravity anomalies profiles (amplitude coefficient, depth to top, depth to bottom, origin, and length of the target representing the difference between two depths). The algorithm utilizes particle movement to identify the best way to reach the global or optimum solution. The algorithm's performance was evaluated on synthetic-examples with and without noise (5 % and 10 % levels) and also verified on a real dataset for mineral exploration from Canada. The results showed that the algorithm's stability and accuracy were not affected by the presence of noise and multi-models. Moreover, the field case results were consistent with the existing geological information, borehole data, and previously published outcomes.
重力异常解释对于识别地下矿化目标以及了解目标与周围构造之间的密度变化至关重要。为了确认矿石和矿物目标的存在，常使用简单几何体。用于重力数据分析的常用全局启发式算法之一是粒子优化算法。在本研究中，我们采用此方法通过推断重力异常剖面（振幅系数、顶部深度、底部深度、原点以及代表两个深度之差的目标长度）来确定类似有限垂直圆柱体的埋藏体参数。该算法利用粒子移动来确定达到全局或最优解的最佳方式。在有噪声（5%和10%水平）和无噪声的合成示例上评估了该算法的性能，并在加拿大的一个用于矿产勘探的真实数据集上进行了验证。结果表明，该算法的稳定性和准确性不受噪声和多模型的影响。此外，野外实例结果与现有的地质信息、钻孔数据以及先前发表的成果一致。
