Essa Khalid S, Gomaa Omar A, Elhussein Mahmoud, Géraud Yves, Diraison Marc, Diab Zein E
Geophysics Department, Faculty of Science, Cairo University, Giza, 12613, Egypt.
GeoRessources Laboratory, University of Lorraine, Nancy, 54500, France.
Sci Rep. 2025 Feb 10;15(1):5000. doi: 10.1038/s41598-025-88350-4.
Here, we present a remarkable methodology for unveiling subsurface structures with the potential to transform the exploration of mineral and ores resources, as well as the study of volcanic activity. By incorporating the Metaheuristic Bat algorithm (MBA) with the second horizontal gravity gradient (SHG) and employing variable window lengths, we aim to eliminate the regional effect in gravity data, thereby improving the precision of subsurface structure parameter estimation. Through rigorous evaluation on synthetic cases, we have demonstrated the robustness of our approach and its ability to handle diverse geological complexities and noise levels. Furthermore, our method has been applied to actual gravity data from three distinct locations: Canada, India, and Cuba, yielding excellent results that confirm the reliability and applicability of our methodology to real-world geological settings. We are confident that the use of variable window lengths in the SHG computation, coupled with the optimization of the global optimal solution via the Metaheuristic Bat Algorithm, can significantly contribute to the enhanced precision of subsurface structural parameter estimation. We hope our research will inspire others to explore this groundbreaking methodology and continue advancing the field of subsurface structure optimization.
在此，我们提出了一种非凡的方法，用于揭示地下结构，该方法有可能改变矿产资源勘探以及火山活动研究的方式。通过将元启发式蝙蝠算法（MBA）与二阶水平重力梯度（SHG）相结合，并采用可变窗口长度，我们旨在消除重力数据中的区域效应，从而提高地下结构参数估计的精度。通过对合成案例的严格评估，我们证明了我们方法的稳健性及其处理各种地质复杂性和噪声水平的能力。此外，我们的方法已应用于来自加拿大、印度和古巴三个不同地点的实际重力数据，取得了优异的结果，证实了我们方法在实际地质环境中的可靠性和适用性。我们相信，在SHG计算中使用可变窗口长度，再加上通过元启发式蝙蝠算法优化全局最优解，能够显著提高地下结构参数估计的精度。我们希望我们的研究能激励其他人探索这种开创性的方法，并继续推动地下结构优化领域的发展。
