Enhancing coal quality estimation through multiple geophysical log analysis

Abstract

Coal quality information such as ash content, density, volatile matter and specific energy are important to the coal mining industry for mine planning, design, extraction, beneficiation and utilisation. These parameters are traditionally obtained through laboratory analyses conducted on drill-core samples from exploration drill holes. This process is expensive and time consuming. In this paper, we use a multi-variable data analysis algorithm based on the Radial Basis Function (RBF) neural network methods to estimate coal quality parameters from routinely-acquired multiple geophysical logs such as density, gamma ray and sonic logs. The performance of this RBF-based approach was demonstrated using both self-controlled training data sets and an independent data set from a mine. It was observed that although the density logs play a key role in coal parameter estimation, the use of multiple types of geophysical logs, including logs with different resolutions such as short spaced density log DENB and long spaced density log DENL, improves the estimation accuracy. It is therefore expected that the use of additional geophysical logs such as photoelectric factor (PEF), SIROLOG and PGNAA, which provide data of geochemical constituents, should improve estimates of coal quality parameters.