Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Agustin Lobo,David Martí,Jose-Luis Fernandez-Turiel,Jordi Ibáñez-Insa,Emma Garcia,Gisela Barroso

doi:10.3390/rs13163258

Abstract

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces.

Highlights

Hyperspectral images produced by imaging spectrometers are 3D arrays in which each voxel holds a radiance spectrum that is processed to reflectance [1,2]
In case of a crude identification, being able to delimit a given uncertain material facilitates an accurate sampling for complementary techniques such as X-ray diffraction (XRD) or X-ray fluorescence (XRF)
As a previous step to acquire, process and analyze hyperspectral images of a mine excavation face, here we explore the feasibility and interest of such an approach by using hand samples that feature the minerals of interest

Summary

Introduction

Hyperspectral images produced by imaging spectrometers are 3D arrays in which each voxel holds a radiance spectrum that is processed to reflectance [1,2]. The acquisition of single reflectance spectra with spectrometers in the visible, near-infrared, and shortwave infrared wavelength domains (400–2500 nm) is a relatively simple and non-invasive technique that has been used in the laboratory and the field for decades ago [3,4]. The particular optical and electronic properties of each material result, under illumination results in specific spectral features that are often diagnostic of given minerals and rocks [5–. 7], the influence of variations in illumination and viewing geometry, the macrostructure of the sample, and the presence of mixtures in the field-of-view, tend to reduce the diagnostic power of reflectance spectra. In case of a crude identification, being able to delimit a given uncertain material facilitates an accurate sampling for complementary techniques such as X-ray diffraction (XRD) or X-ray fluorescence (XRF).

Objectives

Results

Discussion

Conclusion