Two-dimensional prediction of the interface of geological formations: A comparative study

Qi, Xiaohui, Wang, Hao, Chu, Jian and Chiam, Kiefer (2022) Two-dimensional prediction of the interface of geological formations: A comparative study. Tunnelling and Underground Space Technology, 121. p. 104329. ISSN 0886-7798

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Official URL: https://doi.org/10.1016/j.tust.2021.104329

Abstract

The location of the interface of geological formations is an important piece of information for tunneling construction. As site investigation data are usually limited, the uncertainties in locating geological interfaces for the sections between boreholes can be large and challenging to estimate. A suitable geostatistical method is thus needed for spatial prediction of the geological interfaces. In this paper, the performance of three commonly used spatial prediction methods, namely the multivariate adaptive spline regression (MARS), conditional random field (CRF) method, and thin-plate spline interpolation (TPSI) methods, are evaluated for two-dimensional cases using the boreholes data from three sites in Singapore. The prediction accuracies, patterns of the predicted surfaces, and prediction uncertainties obtained from the three methods are compared. A zonation is also proposed to improve the prediction accuracy of the MARS method. The results indicate that the MARS method can show the spatial trend of the geological interface more clearly than the other two methods. The TPSI method produces undesirable oscillations of the surface of geological interfaces and the CRF method may underestimate the extreme values of the geological interface elevations. In general, the prediction accuracy of the MARS method is similar to that of the CRF method, but higher than that of the TPSI method. For cases with very limited data in geologically complex areas, the MARS may have larger errors than the CRF method. However, the accuracy of the former can be significantly improved if a reasonable zonation is performed.

Item Type: Article
Additional Information: Funding information: Singapore Ministry of National Development and the National Research Foundation, Prime Minister’s Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (Award No. L2NICCFP2-2015-1).
Uncontrolled Keywords: Geological interface, Rockhead, Spatial prediction, Multivariate adaptive regression spline, Bayesian-based conditional random field, Thin-plate spline interpolation
Subjects: H300 Mechanical Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 05 Jan 2022 09:01
Last Modified: 05 Jan 2022 09:15
URI: http://nrl.northumbria.ac.uk/id/eprint/48075

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