Improving the subjective labelling of interpretation of geological conditions ahead of the tunnel face

Geological prognosis during tunnelling work is a fundamental task in order to gain knowledge about the rock mass condition ahead of the face, improve the initial geological model available and help for a more efficient and safer tunnel excavation. Tunnel seismic prediction has established as a reliable methodology for predicting the rock mass condition ahead of the face. The quality of the final results or seismic model are conditioned by the quality of the recorded seismic data, data processing and the interpretation of output, that is mainly conditioned to the user's expertise. The goal of this work is to use machine learning methods to create a new way of classifying seismic data as unaffected by human interpretations as possible. In this work, we propose a model where a cascading ensemble of machine learning classifiers is used to analyse the seismic data and available geological documentation at the underground construction site to predict geological conditions. We show that machine learning methods' application eliminates subjective perceptions in prediction, and the proposed ensemble approach improves the accuracy of the geological conditions forecast.