Assessing the viability of underground slate mining by combining an expert system with a GIS

Abstract

The slate sector in Galicia (NW Spain) is facing a growing crisis resulting from the gradual increase in the exploitation ratios for open quarries and increasingly stringent environmental restrictions. Given that good geotechnical quality reduces the costs of any underground mining operation, and — for slate and other ornamental rock — conditions the possibility of extracting blocks of a size acceptable to roofing slate transformation and finishing plants, we created a geotechnical quality index, aimed specifically at analysing slate masses, that applied techniques from the machine learning field (Support Vector Machines). This geotechnical quality index, together with the factors that affect underground mining operations, was integrated in a Geographical Information System, which would enable all relevant information on a slate mass to be viewed and analysed simultaneously.