Drill-Core Hyperspectral and Geochemical Data Integration in a Superpixel-Based Machine Learning Framework

Abstract

The analysis of drill-core samples is one of the most important steps in the mining industry for the exploration and discovery of mineral resources. Geochemical assays are a common approach to represent the abundance of different chemical elements and aid at quantifying the concentrations of the important ore accumulations. However, their acquisition is time-consuming and usually averages of long core portions. Hyperspectral data are increasingly being used in the mining industry to complement the analysis of drill-cores due to their efficiency and fast turn-around time. Moreover, hyperspectral imaging is a technique able to provide data with high spatial resolution. In this article, we propose to integrate the complementary information derived from hyperspectral and geochemical data via a superpixel-based machine learning framework. This framework considers the difference in spatial resolution through segmentation. We extract labels from the geochemical assays and select, from the hyperspectral data, representative samples for each measurement. A supervised machine learning classification (composite kernel support vector machine) is then used to extrapolate the elements relative abundance to the entire core length. We propose an innovative integration of hyperspectral data covering different regions of the electromagnetic spectrum in a kernel-based framework to facilitate the identification of a larger amount of elements. A qualitative and quantitative evaluation of the results demonstrates the capabilities of the proposed method, which provides approximately 20% more accurate results than the pixel-based approach. Results also imply that the method could be beneficial for the reduction of geochemical assays needed for the detailed analysis of the cores.

Keywords

• Data integration
• drill-cores
• geochemical data
• hyperspectral data
• machine learning (ML)
• superpixel segmentation