Artificial intelligence to model bedrock depth uncertainty

Abstract: The estimation of bedrock level for soil and rock engineering is a challenge associated to many uncertainties. Nowadays, this estimation is performed by geotechnical or geophysics investigations. These methods are expensive techniques, that normally are not fully used because of limited budget. Hence, the bedrock levels in between investigations are roughly estimated and the uncertainty is almost unknown. Machine learning (ML) is an artificial intelligence technique that uses algorithms and statistical models to predict determined tasks. These mathematical models are built dividing the data between training, testing and validation samples so the algorithm improve automatically based on passed experiences. This thesis explores the possibility of applying ML to estimate the bedrock levels and tries to find a suitable algorithm for the prediction and estimation of the uncertainties. Many diferent algorithms were tested during the process and the accuracy level was analysed comparing with the input data and also with interpolation methods, like Kriging. The results show that Kriging method is capable of predicting the bedrock surface with considerably good accuracy. However, when is necessary to estimate the prediction interval (PI), Kriging presents a high standard deviation. The machine learning presents a bedrock surface almost as smooth as Kriging with better results for PI. The Bagging regressor with decision tree was the algorithm more capable of predicting an accurate bedrock surface and narrow PI.