Rockhead profile simulation using an improved generation method of conditional random field

Rockhead profile is an important part of geological profiles and can have significant impacts on some geotechnical engineering practice, and thus, it is necessary to establish a useful method to reverse the rockhead profile using site investigation results. As a general method to reflect the spatial distribution of geo-material properties based on field measurements, the conditional random field (CRF) was improved in this paper to simulate rockhead profiles. Besides, in geotechnical engineering practice, measurements are generally limited due to the limitations of budget and time so that the estimation of the mean value can have uncertainty to some extent. As the Bayesian theory can effectively combine the measurements and prior information to deal with uncertainty, CRF was implemented with the aid of the Bayesian framework in this study. More importantly, this simulation procedure is achieved as an analytical solution to avoid the time-consuming sampling work. The results show that the proposed method can provide a reasonable estimation about the rockhead depth at various locations against measurement data and as a result, the subjectivity in determining prior mean can be minimized. Finally, both the measurement data and selection of hyper-parameters in the proposed method can affect the simulated rockhead profiles, while the influence of the latter is less significant than that of the former. (C) 2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.

Automated summary

In this study, the conditional random field (CRF) was improved to simulate rockhead profiles. With the assistance of Bayesian theory, the proposed method utilizes measurement data and prior information to handle uncertainty. The method provides reasonable estimations of rockhead depth at various locations and reduces subjectivity in determining prior mean.