Data fusion of geotechnical and geophysical data for three-dimensional subsoil schematisations

Abstract

Subsoil schematisations are a paramount activity for the execution of infrastructure earthworks. Currently, subsoil schematisations are laborious, and mostly based on correlation and interpolation of available geotechnical and geophysical in-situ data. Geotechnical in-situ testing tends to be accurate but merely provides local information, while geophysical investigations are often performed to image the subsurface, and provide valuable insight for areas. Yet, there is not always a clear way to reflect geophysical properties to soil schematisation. This paper presents a data fusion methodology to perform subsoil schematisation and parametrisation. This methodology makes use of available geotechnical, geological and geophysical data sets, and combines them by means of machine learning algorithms (Neural Networks and Random Forest). The data fusion method is applied on two case studies. The first case study concerns a large area of a flood defence line, and the second case study concerns a small polder area next to a dike. The accuracy of the data fusion algorithm is assessed by comparing its results against a validation data set, that has not been exposed to the data fusion algorithm. The performance of the two algorithms and of the parameters that govern each algorithm is discussed. The results show that both Neural Networks and Random Forest are suitable to perform subsoil schematisations. The analyses show that the Random Forest leads to a lower error on the validation data set. However, Random Forest fails to predict the occurrence of thin clay layers in the second case study, while Neural Networks are successful at it. The data fusion methodology shows the potential to enhance the subsoil schematisation procedure, by increasing the schematisation spatial resolution.