This paper presents an innovative end-to-end workflow for mineral exploration, integrating ambient noise tomography (ANT) and artificial intelligence (AI) to enhance the discovery and delineation of mineral resources essential for the global transition to a low carbon economy. We focus on copper as a critical element, required in significant quantities for renewable energy solutions. We show the benefits of utilising ANT, characterised by its speed, scalability, depth penetration, resolution, and low environmental impact, alongside artificial intelligence (AI) techniques to refine a continent-scale prospectivity model at the deposit scale by fine-tuning our model on local high-resolution data. We show the promise of the method by first presenting a new data-driven AI prospectivity model for copper within Australia, which serves as our foundation model for further fine-tuning. We then focus on the Hillside IOCG deposit on the prospective Yorke Peninsula. We show that with relatively few local training samples (orebody intercepts), we can fine tune the foundation model to provide a good estimate of the Hillside orebody outline. Our methodology demonstrates how AI can augment geophysical data interpretation, providing a novel approach to mineral exploration with improved decision-making capabilities for targeting mineralization, thereby addressing the urgent need for increased mineral resource discovery.

翻译：本文提出了一种创新的端到端矿产勘探工作流程，融合环境噪声层析成像（ANT）与人工智能（AI）技术，以提升对全球低碳经济转型所需关键矿产资源的发现与圈定能力。我们聚焦于铜这一关键元素——可再生能源解决方案对其需求量巨大。研究表明，利用ANT（具有快速、可扩展、高穿透深度、高分辨率及低环境影响的特性）与人工智能技术相结合，通过本地高分辨率数据对大陆尺度远景区预测模型进行精细调优，能够在矿床尺度实现预测精度提升。我们首先展示了首个数据驱动的澳大利亚铜矿AI远景区预测模型（作为后续精细调优的基础模型），验证了该方法的前景。随后聚焦于约克半岛远景区内的Hillside铁氧化物铜金矿床：结果显示，仅需少量本地训练样本（矿体截穿点），即可通过基础模型的迁移学习实现对Hillside矿体形态的准确估计。该研究方法揭示了人工智能如何增强地球物理数据解译能力，为矿产勘探提供了具有优化决策能力的创新路径，从而有效应对当前亟需加速矿产资源发现的紧迫需求。