An efficient model to estimate the soil profile and stratigraphic uncertainty quantification

Abstract

Comprehensive geological information is very important and needed in underground construction and underwater infrastructure. However, it is quite difficult to reveal the configuration of different soil features of underground with sparsely measured data. In this study, a new algorithm named General Regression-Markov Chain (GR-MC) was proposed by coupling the general regression neural network and the Markov chain method together to reproduce the characteristics of underground stratification based on rare borehole information. The general regression neural network method was introduced to evaluate the geological variation along the inclined orientation, while the Markov chain method was applied to examine the change in soil types along the depth. The spatial coordinates of the treated construction site were designed as the input variables, while a vector including the probability information of different soil types was set as the output. The proposed GR-MC method is very intuitive and can be efficiently implemented to predict the geological model using only the coordinates as inputs. Moreover, the proposed method can be smoothly extended into a three-dimensional case with low computational resources. Compared to the existing methods, the proposed GR-MC algorithm is computationally efficient and robust. Three cases from real practice were evaluated and the calculated results were consistent with the known borehole information. Besides, the stratigraphic uncertainty was quantified based on information entropy theory. The results can visually reveal the zones of the estimated soil profile with relatively large uncertainty. The practical application is that possible additional borehole locations were identified to reduce the stratigraphic uncertainty of a construction site.