TY - GEN
T1 - Improving the subjective labelling of interpretation of geological conditions ahead of the tunnel face
AU - Sapronova, A.
AU - Unterlas, P. J.
AU - Marcher, T.
AU - Hecht-Méndez, J.
AU - Dickmann, T.
N1 - Publisher Copyright:
© 2022 2nd EAGE Digitalization Conference and Exhibition. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127939686&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.202239058
DO - 10.3997/2214-4609.202239058
M3 - Conference paper
AN - SCOPUS:85127939686
T3 - 2nd EAGE Digitalization Conference and Exhibition
BT - 2nd EAGE Digitalization Conference and Exhibition
PB - European Association of Geoscientists and Engineers, EAGE
T2 - 2nd EAGE Digitalization Conference and Exhibition
Y2 - 23 March 2022 through 25 March 2022
ER -