Ground condition assessment based on Bayesian framework. Issue 5 (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Li-Feng; Yang, Song; Xie, Xiongyao; Qi, Bing; Cao, Zhensheng; Zhou, Biao; Zhai, Junli
• Is Part Of: IOP conference series. Volume 861:Issue 5(2021)
• Publication Date: 2021-10
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/861/5/052075
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Minimizing uncertainties is an important issue among the significant discussions pertaining to project design and planning. Usually, the uncertainties in subsurface projects arise from the unknown ground conditions that may cause the designer to fail to consider all potential issues prone to occur during the construction procedure. This paper presents a Bayesian framework to predict the ground conditions before excavation. The state transition probability is calculated by the difference between the simulation results and the observation data so that the model parameters gradually tend to a posterior distribution. Through numerical simulation, the algorithm is verified, and error analysis is carried out. Within the structure, the resistivity is mainly controlled by the structural characteristics, so the formation lithology has great uncertainty in the geological structure area. The inversion results of fracture degree and water content are accurate.