The self-potential method (SP) has been used extensively to reveal some model parameters of various ore deposits. However, estimating these parameters can be challenging due to the mathematical nature of the inversion process. To address this issue, we propose here a novel global optimizer called the Modified Barnacles Mating Optimizer (MBMO). We improved upon the original approach by incorporating a variable genital length strategy, a novel barnacle offspring evolving method, and an out-of-bounds correction approach. The MBMO has not been previously applied to geophysical anomalies. Prior to inversion of real data sets, modal and sensitivity Analyzes were conducted using a theoretical model with multiple sources. The Analyzes revealed that the problem is modal in nature, model parameters have varying levels of sensitivity, and an algorithm that can well balance global exploration with local exploitation is required to solve this problem. The MBMO was tested on theoretical SP anomalies and four real datasets from Türkiye, Canada, India, and Germany. Its performance was compared to the original version under equal conditions. Uncertainty determination studies were carried out to comprehend the reliability of the solutions obtained via both algorithms. The findings indicated clearly that the MBMO outperformed its original version in estimating the model parameters from SP anomalies. The modifications presented here improved its ability to search for the global minimum effectively. In addition to geophysical datasets, experiments with 11 challenging benchmark functions demonstrated the advantages of MBMO in optimization problems. Theoretical and field data applications showed that the proposed algorithm can be used effectively in model parameter estimations from SP anomalies of ore deposits with the help of total gradient anomalies.

自势法（SP）已被广泛用于揭示各种矿床的一些模型参数。然而，由于反演过程的数学性质，估计这些参数可能具有挑战性。为了解决这个问题，我们在这里提出了一种新颖的全局优化器，称为修改藤壶交配优化器（MBMO）。我们通过结合可变生殖器长度策略、新颖的藤壶后代进化方法和越界校正方法对原始方法进行了改进。 MBMO 之前尚未应用于地球物理异常。在对真实数据集进行反演之前，使用具有多个来源的理论模型进行模态和敏感性分析。分析表明，该问题本质上是模态问题，模型参数具有不同程度的敏感度，需要一种能够很好地平衡全局探索与局部开发的算法来解决该问题。 MBMO 在理论 SP 异常和来自土耳其、加拿大、印度和德国的四个真实数据集上进行了测试。在同等条件下将其性能与原始版本进行了比较。进行了不确定性测定研究，以了解通过两种算法获得的解决方案的可靠性。研究结果清楚地表明，MBMO 在根据 SP 异常估计模型参数方面优于其原始版本。这里提出的修改提高了其有效搜索全局最小值的能力。除了地球物理数据集之外，11 个具有挑战性的基准函数的实验也证明了 MBMO 在优化问题中的优势。理论和现场数据应用表明，该算法可以有效地利用总梯度异常来估计矿床SP异常的模型参数。