Various artificial intelligence (AI) algorithms have been employed successfully to map mineral prospectivity for a specific mineral deposit type to assist mineral exploration. Numerous tools have been developed to incorporate AI algorithms, such as ArcSDM and ArcGIS. However, existing tools remain inadequate for geologist-friendly functions, and they are not fully tailored for mineral prospectivity mapping (MPM). This limitation has impeded the advancement and utilization of AI algorithms in MPM. Thus, this study introduced a novel ArcEngine-based software named ArcMPM to expeditiously integrate multi-source prospecting information for MPM using AI algorithms. ArcMPM was developed using Python and C#, based on ArcEngine and Visual Studio 2012, which incorporate two popular machine learning (ML) approaches: random forests (RFs) and convolutional neural networks (CNNs), representing shallow ML and deep learning algorithms, respectively. Moreover, it encompasses a complete procedure suitable for MPM by utilizing the RF and CNN models from sample generation to model evaluation. A case study in the Baguio region of the Philippines illustrated the convenience and effectiveness of utilizing ArcMPM for MPM. The success-rate curves demonstrated that the RF and CNN models developed in ArcMPM, particularly the CNN, exhibited high accuracy in delineating high-prospectivity areas. In addition, the case study proved that, in contrast to other GIS tools, ArcMPM can conveniently generate positive and negative samples under geological constraints, customize the model structure to suit the MPM according to the needs of geologists, and provide evaluation metrics that are accessible and practical to geologists.

各种人工智能 (AI) 算法已成功用于绘制特定矿床类型的矿产前景图，以协助矿产勘探。人们开发了许多工具来整合 AI 算法，例如 ArcSDM 和 ArcGIS。然而，现有工具仍然不足以满足地质学家的需求，而且它们也没有完全适合矿物前景图 (MPM)。这种限制阻碍了 MPM 中 AI 算法的进步和利用。因此，本研究引入了一种名为 ArcMPM 的新型基于 ArcEngine 的软件，可使用 AI 算法快速集成多源勘探信息以进行 MPM。 ArcMPM 是使用 Python 和 C# 开发的，基于 ArcEngine 和 Visual Studio 2012，它结合了两种流行的机器学习 (ML) 方法：随机森林 (RF) 和卷积神经网络 (CNN)，分别代表浅层 ML 和深度学习算法。此外，它通过利用 RF 和 CNN 模型，涵盖了从样本生成到模型评估的适合 MPM 的完整过程。菲律宾碧瑶地区的案例研究说明了利用 ArcMPM 进行 MPM 的便利性和有效性。成功率曲线表明，ArcMPM 中开发的 RF 和 CNN 模型，特别是 CNN，在描绘高前景区域方面表现出高精度。此外，案例研究证明，与其他GIS工具相比，ArcMPM可以在地质约束下方便地生成正样本和负样本，并根据地质学家的需求定制适合MPM的模型结构，并提供可访问的评价指标对地质学家来说也很实用。