In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.

As an earthquake occurred in Iburi subprefecture, Japan on September 2018, Sapporo has also been damaged by ground subsidence. Ground subsidence can occur in various area, especially in urban areas causing considerable damage to human lives and properties. Currently, ground subsidence is one of the most important problems all over the world. However, quantitative research about ground subsidence due to earthquake that particularly occurred in urban area was not well studied. To prevent and minimize critical damage caused by subsidence, this study aims to construct hazard map to predict susceptible subsidence place in East area of Sapporo, using geographic information system (GIS) and frequency ratio (FR) model. To determine major factors related to ground subsidence, spatial databases were constructed from geological map, slope data, land use map, borehole data, precipitation data, railroad and subway line data and earthquake information data. Weight of each factors were estimated by FR model to analyze degree of subsidence hazard and construct subsidence hazard map. Constructed subsidence susceptibility map was compared with reported actual ground subsidence area for verification and showed prediction accuracy of 85.17%. This result showed sufficient possibility for reliable susceptibility mapping of ground subsidence using GIS and FR model.

The mining industry makes up a large portion of the gross domestic product (GDP) in Australia, although securing human resources remains a problem in that field. The aim of this paper is to identify Australian university mining students’ preferences, considering it as potential employees’ preferences, for labour conditions at mining sites by means of a discrete choice experiment to promote efficient improvements in labour conditions in the mining industry. The data of 93 respondents analysed in this paper was collected by survey carried out in two universities in Australia. The result of the study showed that students have preferences on several factors such as wage, fatality rate, working position, commuting style, and company. Students having specific sociodemographic characters were found to show specific preferences on labour conditions. The results of this study indicate the potential average of appropriate monetary compensation for each factor.