the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GeoPDNN: A Semisupervised Deep Learning Neural Network Using Pseudolabels for Three-dimensional Urban Geological Modelling and Uncertainty Analysis from Borehole Data
Xuechuang Xu
Xulei Wang
Mark Jessell
Vitaliy Ogarko
Zhibin Liu
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)
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CEC1: 'Comment on gmd-2023-11', Juan Antonio Añel, 06 May 2023
reply
Dear authors,
Checking your manuscript, I have seen that the input data for your work is in .mdb format. This is a proprietary format, which can be opened only using proprietary software. This issue precludes the replicability of your work. For example, I can not check the data, as I do not have the necessary software for it, and although I could have access to it, it is not free software, which is against the principles of scientific reproducibility.
Therefore, please, we would thank you if you could share your input data in an open ISO format that is accessible to anyone and without the need to use specific software. This could be .dat, .csv, .ods, etc.
Regards,
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2023-11-CEC1 -
AC1: 'Reply on CEC1', Jiateng Guo, 06 May 2023
reply
Dear Juan,
Thanks for your suggestion. Now, I have shared the input data in an open ISO format (.csv) accessible to anyone via the original data sets link: https://doi.org/10.5281/zenodo.7535214
Best regards,
Xuechuang and Jiateng
Citation: https://doi.org/10.5194/gmd-2023-11-AC1
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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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