High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan

Diana Krupnik,Shuhab D Khan

doi:10.3390/min10110967

Abstract

The study of hand samples is a significant aspect of geoscience. This work showcases a technique for relatively quick and inexpensive mineral characterization, applied to a Cretaceous limestone formation and for sulfide-rich quartz vein samples from Northern Pakistan. Spectral feature parameters are derived from mineral mixtures of known abundance and are used for mineral mapping. Additionally, three well-known classification techniques—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Neural Network—are compared. Point counting results from petrographic thin sections are used for validation the limestone samples, and QEMSCAN mineral maps for the sulfide samples. For classifying the carbonates, the SVM classifier produced results that are closest to the training set—with 84.4% accuracy and a kappa coefficient of 0.8. For classifying sulfides, SAM produced mineral abundances that were closest to the validation data, possibly due to the low reflectance of sulfides throughout the short-wave infrared spectrum with some differences in the overall spectral shape.

Highlights

Applications of hyperspectral imaging (HSI) are gaining popularity in various fields such as agriculture [1,2], food quality monitoring [3,4], medical studies [5,6], forensics [7,8], geologic studies [9,10,11,12,13], and many others [14,15,16]
Astore Valley in Northern Pakistan (Figure 2). These samples were analyzed using using QEMSCAN at Colorado School of Mines, and the results suggest that gold mineralization is QEMSCAN at Colorado School of Mines, and the results suggest that gold mineralization is associated associated with base metal sulfides, chalcopyrite and galena [34,35]
Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Neural Network classifiers were compared, finding SVM yielded the highest accuracy for classifying carbonates

Summary

Introduction

Applications of hyperspectral imaging (HSI) are gaining popularity in various fields such as agriculture [1,2], food quality monitoring [3,4], medical studies [5,6], forensics [7,8], geologic studies [9,10,11,12,13], and many others [14,15,16]. Close-range HSI has been implemented as a non-invasive, high-resolution alternative to traditional methods of chemical characterization of materials of interest. Various classification algorithms have been applied to hyperspectral images to map endmember materials [17,18,19]. High-resolution imaging has the potential for increased accuracy of mineral abundance calculations

Objectives

Methods

Conclusion