Evaluation of automated lithology classification architectures using highly-sampled wireline logs for coal exploration

Three popular supervised machine learners, namely the Naïve Bayes classifier, Support Vector Machine, and Artificial Neural Network, were tested under two architectures: committee (one classifier per well log) and singular (one classifier for all well logs). Favourable performance was achieved under both architectures when the base classifier was tuned to maximise a coal-specific performance metric. Results show that the committee architecture increased overall accuracy, generally by increasing accuracy on the dominant lithology class and reducing the classification rate of minor lithology classes. Overall accuracy was further improved by post-processing to remove thin classified intervals (<10cm). The committee architecture provides the benefits of faster classifier training time through parallelisation, as well as a flexible platform for incorporating additional well logs without the need to retrain existing classifiers.