Abstract Predictive spatial maps of a mineralising system’s geological features are highly desirable and useful from the industrial standpoint of resource exploration, mining, civil and geotechnical engineering. Additionally, such maps have academic value as theoretical models of complex natural phenomena. However, the production of these maps is a challenging endeavour using models based on geochemical data that are sparely populated with uneven statistical distributions. Using a Conformal Geometric Algebra based formulation that predicts geological features from geochemical datasets in a recent work by the authors (referred to as ‘the hyperfield formulation’), this work extends the methods and techniques to the construction of predictive spatial maps of these geological features. The contribution further introduces a novel “intersection space minimisation procedure” and the “raytracing interpolation procedure” and they are used to a create comprehensive and accurate spatialised maps from a limited number of predictions in conjunction with neural networks and the hyperfield formulation. Case studies are presented where the mineralisation distances and the whole rock Au concentrations are predicted and mapped for the Mount Porter deposit, Northern Territory, Australia.

中文翻译：

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使用基于共形几何代数的毒砂微量元素组合模型的 Greenfields 金矿床勘探技术

摘要 从资源勘探、采矿、土木和岩土工程的工业角度来看，矿化系统地质特征的预测空间图是非常需要和有用的。此外，此类地图作为复杂自然现象的理论模型具有学术价值。然而，使用基于地球化学数据的模型制作这些地图是一项具有挑战性的工作，这些数据很少包含不均匀的统计分布。在作者最近的工作中，使用基于保形几何代数的公式从地球化学数据集预测地质特征（称为“超场公式”），这项工作将方法和技术扩展到构建这些地质特征的预测空间图. 该贡献进一步引入了一种新颖的“交叉空间最小化程序”和“光线跟踪插值程序”，它们用于根据有限数量的预测以及神经网络和超场公式来创建全面而准确的空间化地图。介绍了案例研究，其中预测并绘制了澳大利亚北领地波特山矿床的矿化距离和全岩金浓度。