Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face

Abstract

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.

Sensing Technology Applications in the Mining Industry—A Systematic Review

“…In the study concerning the underground quarry, the main focus was on the creation of rock mass models [ 35 ]. As for the quarry surface works, the authors’ aimed to study the impact of the exploitative activities, in the vicinity of the quarry, in terms of air quality [ 42 ], mapping surface areas to plan recovery [ 43 ] or merely to comprehend the mineral distribution [ 44 ], investigating the originators of rock mass failures [ 33 ], and studying the vibrations induced by blasting [ 45 ]. Regarding surface mines, other queries emerged relative to: detection of land changes caused by mining activities [ 46 , 47 ], restoration monitoring [ 48 ], air quality monitoring [ 49 ], and two studies used the sensing systems to build systems—one to monitor disaster [ 31 ] and the other to monitor goaf stress [ 29 ].…”

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

“…This raised the need for a new approach of hyperspectral data acquisition at small angles. Usually, this is achieved by horizontal or slightly tilted sensor mounting on a tripod or rotary stage (in case of a push broom sensor) [20]- [22], alternatively, any other sensor-bearing platform such as a car, boat, or low altitude UAS can be used. Even if the acquisition is straightforward and requires no additional platform, the radiometric and geometric correction of the data is extremely complex (Fig.…”

Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework

“…An addition/reclamation model of a stockpile, which tracks the material through the stockpile, could give a better overview of the material flow. In addition, at locations with a gap (e.g., a blasted bench, truck, stockpiles), it is possible to do new measurements with appropriate sensors (e.g., hyperspectral imaging or RFID tags (radio frequency identification) [13,24]) and consequently increase the fingerprint confidence, which ultimately improves the forecast of the processing behaviour.…”