Self-potential (SP) method has many applications, where the interpretation of SP data can be used for qualitative and quantitative interpretation. However, inversion of SP data in this paper is of quantitative interpretation and consists of highly non-linear, multimodal data and deploys global optimum method (GOM). Micro-differential evolution (MDE) is a GOM with small or micro-population size (5–8 populations) for each iteration. Consequently, this approach involves small numbers of forward computation in the inversion process. Two MDE variants, including adaptive MDE (\( \mu \)JADE) and vectorized random mutation factor (MVDE) were tested first for different level of noises containing synthetic SP data with single anomaly and applied to synthetic SP data of multiple anomalies. The MDE variants are reliable and effective for inverting noisy SP data. Furthermore, in order to check the rationality of MDE variants, the algorithm is applied to seven field data from different applications, including groundwater exploration, shear zone tracing, water accumulation in landslides and embankment stability assessment. The model parameters revealed by MDE variants are accurate and show good agreement with the previous results estimated using other approaches. In addition, MDE variants also require fewer forward modelling calculations than other optimization approaches.

中文翻译：

---

一种有效的全局优化方法，利用微差分进化进行自势数据反演

自势（SP）方法具有许多应用，其中SP数据的解释可用于定性和定量解释。但是，本文中SP数据的反演是定量解释，由高度非线性的多峰数据组成，并采用了全局最优方法（GOM）。微分进化（MDE）是每次迭代都具有较小或较小种群大小（5-8种群）的GOM。因此，该方法在反演过程中涉及少量的正向计算。两个MDE变体，包括自适应MDE（\（\ mu \）首先测试JADE和向量化随机突变因子（MVDE）的不同级别的噪声，这些噪声包含具有单个异常的合成SP数据，并应用于多个异常的合成SP数据。MDE变体对带噪声的SP数据是可靠且有效的。此外，为了检查MDE变量的合理性，该算法被应用于来自不同应用的七个现场数据，包括地下水勘探，剪切带追踪，滑坡中的积水和路堤稳定性评估。MDE变体揭示的模型参数是准确的，并且与使用其他方法估算的先前结果显示出很好的一致性。另外，与其他优化方法相比，MDE变体还需要更少的正向建模计算。