  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.
