Factors Influencing Autonomously Generated 3D Geophysical Spatial Models

TL;DR

This paper examines how input data resolution and sample size affect Gaussian Process-based 3D geophysical models for mineral exploration, using a case study of an iron ore deposit.

Contribution

It investigates the impact of data resolution and sample number on GP-based 3D geophysical modeling in mineral exploration.

Findings

Higher data resolution improves model accuracy.

Increasing the number of nearest samples enhances model reliability.

Case study demonstrates practical application in iron ore deposit analysis.

Abstract

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.