The identification and mapping of geochemical anomaly patterns have emerged as a more precise and efficient approach for mineral exploration, with deep learning algorithms being extensively employed in this realm. However, existing methodologies require further investigation regarding model interpretability and correlation with established mineral control factors. This paper proposes a regional geochemical anomaly identification method based on the memory-augmented autoencoder (MemAE), incorporating geological controlling factors. Firstly, the MemAE model is introduced to address the excessive generalization capability of the traditional autoencoder (AE) model. Secondly, utilizing multifractal singularity theory, a nonlinear functional relationship between faults and mineral deposits is established. This relationship reveals the controlling effect of faults on mineralization and it is incorporated as a constraint term in the MemAE's loss function. Finally, the constructed geochemical anomaly identification model is employed to delineate prospective mineralization areas, with comparative studies conducted on AE, MemAE, and geologically constrained MemAE models. The results demonstrate that the geologically constrained MemAE exhibits superior performance, achieving an AUC of 0.802. The eight delineated mineralization prospective areas show strong concordance with actual distributions. The proposed method, which considers geological controlling factors, effectively enhances model interpretability and demonstrates excellent geochemical anomaly identification capabilities. Consequently, this approach can be considered a viable methodology for mineral exploration.

地球化学异常模式的识别和绘图已成为一种更精确、更高效的矿物勘探方法，深度学习算法在该领域得到了广泛应用。然而，现有方法需要进一步研究模型的可解释性和与已建立的矿物控制因素的相关性。本文提出了一种基于记忆增强自编码器 （MemAE） 的区域地球化学异常识别方法，并结合地质控制因素。首先，引入 MemAE 模型来解决传统自动编码器 （AE） 模型泛化能力过强的问题;其次，利用多重分形奇点理论，建立了断层与矿床之间的非线性泛函关系。这种关系揭示了断层对矿化的控制作用，并作为约束项纳入 MemAE 的损失函数中。最后，采用构建的地球化学异常识别模型来描绘潜在的矿化区域，并对 AE、MemAE 和地质约束的 MemAE 模型进行了比较研究。结果表明，受地质约束的 MemAE 表现出优异的性能，AUC 为 0.802。8 个划定的矿化前景区域与实际分布高度一致。该方法考虑了地质控制因素，有效地增强了模型的可解释性，并表现出优异的地球化学异常识别能力。因此，这种方法可以被认为是一种可行的矿产勘探方法。