Academic Journal
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
| Title: | GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data |
|---|---|
| Authors: | J. Guo, X. Xu, L. Wang, X. Wang, L. Wu, M. Jessell, V. Ogarko, Z. Liu, Y. Zheng |
| Source: | Geoscientific Model Development, Vol 17, Pp 957-973 (2024) |
| Publisher Information: | Copernicus Publications, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Geology |
| Subject Terms: | Geology, QE1-996.5 |
| More Details: | Borehole data are essential for conducting precise urban geological surveys and large-scale geological investigations. Traditionally, explicit modelling and implicit modelling have been the primary methods for visualizing borehole data and constructing 3D geological models. However, explicit modelling requires substantial manual labour, while implicit modelling faces problems related to uncertainty analysis. Recently, machine learning approaches have emerged as effective solutions for addressing these issues in 3D geological modelling. Nevertheless, the use of machine learning methods for constructing 3D geological models is often limited by insufficient training data. In this paper, we propose the semi-supervised deep learning using pseudo-labels (SDLP) algorithm to overcome the issue of insufficient training data. Specifically, we construct the pseudo-labels in the training dataset using the triangular irregular network (TIN) method. A 3D geological model is constructed using borehole data obtained from a real building engineering project in Shenyang, Liaoning Province, NE China. Then, we compare the results of the 3D geological model constructed based on SDLP with those constructed by a support vector machine (SVM) method and an implicit Hermite radial basis function (HRBF) modelling method. Compared to the 3D geological models constructed using the HRBF algorithm and the SVM algorithm, the 3D geological model constructed based on the SDLP algorithm better conforms to the sedimentation patterns of the region. The findings demonstrate that our proposed method effectively resolves the issues of insufficient training data when using machine learning methods and the inability to perform uncertainty analysis when using the implicit method. In conclusion, the semi-supervised deep learning method with pseudo-labelling proposed in this paper provides a solution for 3D geological modelling in engineering project areas with borehole data. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1991-959X 1991-9603 |
| Relation: | https://gmd.copernicus.org/articles/17/957/2024/gmd-17-957-2024.pdf; https://doaj.org/toc/1991-959X; https://doaj.org/toc/1991-9603 |
| DOI: | 10.5194/gmd-17-957-2024 |
| Access URL: | https://doaj.org/article/4f2acf363057426fb96d067477a3c050 |
| Accession Number: | edsdoj.4f2acf363057426fb96d067477a3c050 |
| Database: | Directory of Open Access Journals |
| Full text is not displayed to guests. | Login for full access. |
| ISSN: | 1991959X 19919603 |
|---|---|
| DOI: | 10.5194/gmd-17-957-2024 |
| Published in: | Geoscientific Model Development |
| Language: | English |