Sabbaghi Hamid, Tabatabaei Seyed Hassan, Fathianpour Nader
Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran.
Sci Rep. 2024 Mar 14;14(1):6236. doi: 10.1038/s41598-024-56644-8.
Generative adversarial networks (GAN) and various deep autoencoders have been frequently executed to recognize multi-element geochemical anomalies linked to different ore resources in recent decade. Efficient recognition of multi-element geochemical anomaly patterns is a significant issue in mineral exploration targeting. Traditional procedures have not sufficient capability to perform efficient pattern recognition. While, deep learning algorithms as influential subset of machine learning algorithms can present magnificent conclusions in classification and pattern recognition. Because those have robust ability in extracting high-level features of complex inputs. Although, many deep learning algorithms were used to recognize geochemical anomalies but the GANs have demonstrated specific dignity in recognizing multi-element geochemical anomaly patterns. But, these frameworks should be constrained to learn geological knowledge and yield reasonable potential maps. In this regard, a novel geologically-constrained GANomaly was trained with frequency domain training data to recognize multi-element geochemical anomalies. Application of the geologically-constrained GANomaly network with considering mineral system parameters of the Au-Cu mineralization in the Feyzabad district, NE Iran was eventuated to suitable results. The success-rate curves demonstrated that produced map of frequency domain geochemical data has traced 86.68% Au-Cu occurrences via 30% corresponded area while produced map of spatial domain geochemical data has traced 80.13% Au-Cu occurrences via 30% corresponded area.
近十年来，生成对抗网络（GAN）和各种深度自动编码器经常被用于识别与不同矿产资源相关的多元素地球化学异常。高效识别多元素地球化学异常模式是矿产勘查目标中的一个重要问题。传统方法没有足够的能力进行高效的模式识别。而深度学习算法作为机器学习算法中有影响力的子集，在分类和模式识别方面能得出出色的结论。因为它们在提取复杂输入的高级特征方面具有强大的能力。尽管许多深度学习算法被用于识别地球化学异常，但GAN在识别多元素地球化学异常模式方面已显示出独特的优势。但是，这些框架需要受到地质知识的约束，以生成合理的潜力图。在这方面，一种新颖的地质约束GANomaly模型使用频域训练数据进行训练，以识别多元素地球化学异常。在伊朗东北部费扎巴德地区应用考虑了金 - 铜矿化矿物系统参数的地质约束GANomaly网络，取得了合适的结果。成功率曲线表明，频域地球化学数据生成的图通过30%的相应区域追踪到了86.68%的金 - 铜矿点，而空间域地球化学数据生成的图通过30%的相应区域追踪到了80.13%的金 - 铜矿点。
