Preprints
https://doi.org/10.5194/gmd-2023-11
https://doi.org/10.5194/gmd-2023-11
Submitted as: development and technical paper
 | 
20 Apr 2023
Submitted as: development and technical paper |  | 20 Apr 2023
Status: this preprint is currently under review for the journal GMD.

GeoPDNN: A Semisupervised Deep Learning Neural Network Using Pseudolabels for Three-dimensional Urban Geological Modelling and Uncertainty Analysis from Borehole Data

Jiateng Guo, Xuechuang Xu, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng

Abstract. Boreholes are one of the main tools for high-precision urban geology exploration and large-scale geological investigations. At present, machine learning based 3D geological modelling methods for borehole data have difficulty building a finer and more complex model and analysing the modelling results with uncertainty. In this paper, a semisupervised learning algorithm using pseudolabels for 3D geological modelling from borehole data is proposed. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang, and the modelling results are compared with implicit surface modelling and traditional machine learning modelling methods. Finally, an uncertainty analysis of the model is made. The results show that the method effectively expands the sample space, the modelling results perform well in terms of spatial morphology and geological semantics, and the proposed modelling method can achieve good modelling results for more complex geological regions.

Jiateng Guo et al.

Status: open (until 03 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2023-11', Juan Antonio Añel, 06 May 2023 reply
    • AC1: 'Reply on CEC1', Jiateng Guo, 06 May 2023 reply

Jiateng Guo et al.

Data sets

Research borehole data Jiateng Guo and Xuechuang Xu https://doi.org/10.5281/zenodo.7535214

Model code and software

GeoPDNN 1.0 Jiateng Guo and Xuechuang Xu https://doi.org/10.5281/zenodo.7839508

Video supplement

Semisupervised Deep Learning Neural Network Using Pseudolabels for Three-dimensional Urban Geological Modelling and Uncertainty Analysis from Borehole Data Jiateng Guo and Xuechuang Xu https://drive.google.com/file/d/13VERDXM6YJmP7xMabQy3IjhCExuQSWzk/view?usp=sharing

Jiateng Guo et al.

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Short summary
This study proposes a semisupervised learning algorithm using pseudolabels for 3D geological modelling from borehole data. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.