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Multi-Seam Coal Deposit Modeling via Principal Component Analysis & GIS

Department of Mining Engineering, Public Power Corporation of Greece, 10432 Athens, Greece
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Minerals 2019, 9(9), 532; https://doi.org/10.3390/min9090532
Received: 30 July 2019 / Revised: 27 August 2019 / Accepted: 30 August 2019 / Published: 31 August 2019
(This article belongs to the Section Mineral Deposits)
Spatial modeling and evaluation is a critical step for planning the exploitation of mineral deposits. In this work, a methodology for the investigation of a multi-seam coal deposit spatial variability is proposed. The study area includes the Klidi (Florina, Greece) multi-seam lignite deposit which is suitable for surface mining. The analysis is based on the original data of 76 exploratory drill-holes in an area of 10 km2, in conjunction with the geological and geomorphological data of the deposit. The analytical methods include drill-hole data analysis and evaluation based on an appropriate algorithm, principal component analysis and geographic information techniques. The results proved to be very satisfactory for the explanation of the maximum variance of the initial data values as well as the identification of the deposit structure and the optimum planning of mine development. The proposed analysis can be also helpful for minimizing cost and optimizing efficiency of surface mining operations. Furthermore, the provided methods could be applied in other areas of geosciences, indicating the theoretical value as well as the important practical implications of the analysis. View Full-Text
Keywords: principal component analysis; mine planning; brown coal deposit modeling; lignite mining; multi-seam (multi-layered) deposit; geographic information systems; drill-hole evaluation principal component analysis; mine planning; brown coal deposit modeling; lignite mining; multi-seam (multi-layered) deposit; geographic information systems; drill-hole evaluation
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Louloudis, G.; Roumpos, C.; Theofilogiannakos, K.; Stathopoulos, N. Multi-Seam Coal Deposit Modeling via Principal Component Analysis & GIS. Minerals 2019, 9, 532.

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