Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure: A case study in Sweden

Abstract

Due to associated uncertainties, modelling the spatial distribution of depth to bedrock (DTB) is an important and challenging concern in many geo-engineering applications. The association between DTB, the safety and economy of design structures implies that generating more precise predictive models can be of vital interest. In the present study, the challenge of applying an optimally predictive three-dimensional (3D) spatial DTB model for an area in Stockholm, Sweden was addressed using an automated intelligent computing design procedure. The process was developed and programmed in both C++ and Python to track their performance in specified tasks and also to cover a wide variety of different internal characteristics and libraries. In comparison to the ordinary Kriging (OK) geostatistical tool, the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors. The results showed that in the absence of measured data, the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties, thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area. • Two automated deep learning intelligence models through C++ and Python were developed. • 3D pattern of subsurface geo-spatial DTB distributions for Stockholm, Sweden was presented. • The visualised predicted model using C++ showed more accurate results than Python and OK. • Flaws of OK in modelling the limited and heterogeneous distributed data were demonstrated. • The capacity of intelligence models for subsurface DTB characterizing was improved.