Understanding the contribution of geophysical variables is vital for identifying the ore indicator regions. Both magnetometry and gamma-rays are used to identify the geophysical signatures of the rocks. Density is another key variable for tonnage estimation in mining and needs to be re-estimated in areas of change when a boundary update has been conducted. Modelling these geophysical variables in 3D will enable investigate the properties of the rocks and improve our understanding of the ore. Gaussian Process (GP) was previously used to generate 3D spatial models for grade estimation using geochemical assays. This study investigates the influence of the following two factors on the GP-based autonomously generated 3D geophysical models: the resolution of the input data and the number of nearest samples used in the training process. A case study was conducted on a typical Hammersley Ranges iron ore deposit using geophysical logs, including density, collected from the exploration holes.

翻译：理解地球物理变量的贡献对于识别矿石指示区域至关重要。磁力测量和伽马射线测量均用于识别岩石的地球物理特征。密度是采矿中吨位估算的另一个关键变量，当边界更新完成时，需要在变化区域重新估算。对这些地球物理变量进行三维建模，将有助于研究岩石的性质，并加深我们对矿石的理解。高斯过程（Gaussian Process, GP）此前已被用于利用地球化学分析数据生成三维空间模型以进行品位估算。本研究探讨了以下两个因素对基于高斯过程的自主生成三维地球物理模型的影响：输入数据的分辨率以及训练过程中使用的最近样本数量。以典型的哈默斯利山脉（Hammersley Ranges）铁矿床为例，利用包括密度在内的地球物理测井数据（采集自勘探钻孔）进行了案例研究。