Geotechnical ground information prediction using machine learning

Abstract

A method of training a machine learning model to estimate geophysical information at a corresponding location is disclosed. The method comprises obtaining target data comprising geotechnical information and corresponding first locations and input data comprising geophysical information for corresponding second locations within a pre-defined region of each first location. The input data is applied to the machine learning model to obtain an output of the machine learning model and the parameters of the machine learning model are adjusted to reduce an error between the output of the machine learning model and the target data. A method of predicting geotechnical information using the trained model and a method of visualising predicted geotechnical information are also disclosed. The disclosure includes corresponding systems, computer readable media and machine learning models. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

Dependencies are inferred from the claim text.