Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-957-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-957-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Feb 2024
Development and technical paper |
| 05 Feb 2024
| 05 Feb 2024
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
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Xuechuang Xu
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Luyuan Wang
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Xulei Wang
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
School of Geosciences and Info-Physics, Central South University, Lushan Nanlu 932, Yuelu district, Changsha 410012, China
Mark Jessell
The Centre for Exploration Targeting, School of Earth Sciences, University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Sydney, Australia
Vitaliy Ogarko
The Centre for Exploration Targeting, School of Earth Sciences, University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
ARC Industrial Transformation and Training Centre in Data Analytics for Resources and the Environment (DARE), Sydney, Australia
Zhibin Liu
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Yufei Zheng
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
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Cited articles
Avalos, S. and Ortiz, J. M.: Recursive Convolutional Neural Networks in a Multiple-Point Statistics Framework, Comput. Geosci., 141, 104522, https://doi.org/10.1016/j.cageo.2020.104522, 2020.
Batista, G. E. A. P., Prati, R. C., and Monard, M. C.: A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, Sigkdd Explor. Newsl., 6, 20–29, https://doi.org/10.1145/1007730.1007735, 2004.
Burrough, P. A., van Gaans, P. F. M., and Hootsmans, R.: Continuous classification in soil survey: Spatial correlation, confusion and boundaries, Geoderma, 77, 115–135, https://doi.org/10.1016/S0016-7061(97)00018-9, 1997.
Caers, J.: Modeling Uncertainty in the Earth Sciences, Wiley, https://doi.org/10.1002/9781119995920, 2011.
Calcagno, P., Chiles, J. P., Courrioux, G., and Guillen, A.: Geological modelling from field data and geological knowledge Part I. Modelling method coupling 3D potential-field interpolation and geological rules, Phys. Earth Planet. In., 171, 147–157, https://doi.org/10.1016/j.pepi.2008.06.013, 2008.
Caumon, G., Antoine, C., and Tertois, A.: Building 3D Geological Surfaces From Field Data Using Implicit Surfaces, Proceedings of the 27Th Gocad Meeting, Proceedings of the 27th Gocad Meeting, Nancy, 1–6, 2007a.
Caumon, G., Tertois, L. A., and Zhang, L.: Elements for Stochastic Structural Perturbation of Stratigraphic Models, European Association of Geoscientists & Engineers, https://doi.org/10.3997/2214-4609.201403041, 2007b.
Caumon, G., Gray, G., Antoine, C., and Titeux, M. O.: Three-Dimensional Implicit Stratigraphic Model Building From Remote Sensing Data on Tetrahedral Meshes: Theory and Application to a Regional Model of La Popa Basin, NE Mexico, IEEE T. Geosci. Remote, 51, 1613–1621, https://doi.org/10.1109/TGRS.2012.2207727, 2013.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.: Smote: Synthetic Minority Over-Sampling Technique, J. Artif. Int. Res., 16, 321–357, 2002.
Che, D. F., Wu, L. X., and Yin, Z. R.: 3D Spatial Modeling for Urban Surface and Subsurface Seamless Integration, 2009 IEEE International Geoscience and Remote Sensing Symposium, 1–5, 1694, https://doi.org/10.1109/IGARSS.2009.5417787, 2009.
Chen, G., Zhu, J., Qiang, M., and Gong, W.: Three-Dimensional Site Characterization with Borehole Data – a Case Study of Suzhou Area, Eng. Geol., 234, 65–82, https://doi.org/10.1016/j.enggeo.2017.12.019, 2018.
Cuomo, S., Galletti, A., Giunta, G., and Marcellino, L.: Reconstruction of Implicit Curves and Surfaces Via Rbf Interpolation, Appl. Numer. Math., 116, 157–171, https://doi.org/10.1016/j.apnum.2016.10.016, 2017.
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, 2006.
Giraud, J., Lindsay, M., Jessell, M., and Ogarko, V.: Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging, Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, 2020.
Guo, J. and Xu, X.: Semisupervised Deep Learning Neural Network Using Pseudolabels for Three-dimensional Urban Geological Modelling and Uncertainty Analysis from Borehole Data, Google Drive [video], https://drive.google.com/file/d/13VERDXM6YJmP7xMabQy3IjhCExuQSWzk/view?usp=sharing, last access: 13 December 2022.
Guo, J. and Xu, X.: 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, Zenodo [code, data set and video], https://doi.org/10.5281/zenodo.10604091, 2023.
Guo, J., Zhou, W., and Wu, L.: Implicit Three-Dimensional Geo-Modelling Based On Hrbf Surface, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W2, 63–66, https://doi.org/10.5194/isprs-archives-XLII-2-W2-63-2016, 2016.
Guo, J., Wang, J., Wu, L., Liu, C., Li, C., Li, F., Lin, M., Jessell, M. W., Li, P., Dai, X., and Tang, J.: Explicit-Implicit-Integrated 3-D Geological Modelling Approach: A Case Study of the Xianyan Demolition Volcano (Fujian, China), Tectonophysics, 795, 228648, https://doi.org/10.1016/j.tecto.2020.228648, 2020.
Guo, J., Wang, Z., Li, C., Li, F., Jessell, M. W., Wu, L., and Wang, J.: Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data, Natural Resources Research, 31, 2347–2367, https://doi.org/10.1007/s11053-022-10071-6, 2022.
Guo, J. T., Wang, X. L., Wang, J. M., Dai, X. W., Wu, L. X., Li, C. L., Li, F. D., Liu, S. J., and Jessell, M. W.: Three-dimensional geological modeling and spatial analysis from geotechnical borehole data using an implicit surface and marching tetrahedra algorithm, Eng. Geol., 284, 106047, https://doi.org/10.1016/j.enggeo.2021.106047, 2021.
Hellman, K., Ronczka, M., Günther, T., Wennermark, M., Rücker, C., and Dahlin, T.: Structurally Coupled Inversion of Ert and Refraction Seismic Data Combined with Cluster-Based Model Integration, J. Appl. Geophys., 143, 169–181, https://doi.org/10.1016/j.jappgeo.2017.06.008, 2017.
Hillier, M., Wellmann, F., Brodaric, B., de Kemp, E., and Schetselaar, E.: Three-Dimensional Structural Geological Modeling Using Graph Neural Networks, Math. Geosci., 53, 1725–1749, https://doi.org/10.1007/s11004-021-09945-x, 2021.
Hillier, M., Wellmann, F., de Kemp, E. A., Brodaric, B., Schetselaar, E., and Bédard, K.: GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling, Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, 2023.
Hillier, M. J., Schetselaar, E. M., de Kemp, E. A., and Perron, G.: Three-Dimensional Modelling of Geological Surfaces Using Generalized Interpolation with Radial Basis Functions, Math. Geosci., 46, 931–953, https://doi.org/10.1007/s11004-014-9540-3, 2014.
Houlding, S. W.: Geological Interpretation and Modeling. In S. W. Houlding (Ed.), 3D Geoscience Modeling: Computer Techniques for Geological Characterization, Springer Berlin Heidelberg, 113–129, https://doi.org/10.1007/978-3-642-79012-6_7, 1994.
Høyer, A. S., Klint, K. E. S., Fiandaca, G., Maurya, P. K., Christiansen, A. V., Balbarini, N., Bjerg, P. L., Hansen, T. B., and Møller, I.: Development of a High-Resolution 3D Geological Model for Landfill Leachate Risk Assessment, Eng. Geol., 249, 45–59, https://doi.org/10.1016/j.enggeo.2018.12.015, 2019.
Huang, X. R., Dai, Y., Xu, Y. G., and Tang, J.: Seismic Inversion Experiments Based on Deep Learning Algorithm Using Different Datasets, Journal of Soutwest Petroleum University (Science & Technology Edition), 42, 16–25, 2020.
Jessell, M.: Three-Dimensional Geological Modelling of Potential-Field Data, Comput. Geosci., 27, 455–465. https://doi.org/10.1016/S0098-3004(00)00142-4, 2001.
Jessell, M., Guo, J., Li, Y., Lindsay, M., Scalzo, R., Giraud, J., Pirot, G., Cripps, E., and Ogarko, V.: Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications, Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022, 2022.
Jia, R., Lv, Y., Wang, G., Carranza, E., Chen, Y., Wei, C., and Zhang, Z.: A Stacking Methodology of Machine Learning for 3D Geological Modeling with Geological-Geophysical Datasets, Laochang Sn Camp, Gejiu (China), Comput. Geosci., 151, 104754, https://doi.org/10.1016/j.cageo.2021.104754, 2021.
Laloy, E., Herault, R., Lee, J., Jacques, D., and Linde, N.: Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network, Adv. Water Resour., 110, 387–405, https://doi.org/10.1016/j.advwatres.2017.09.029, 2017.
Liu, H., Chen, S. Z., Hou, M. Q., and He, L.: Improved inverse distance weighting method application considering spatial autocorrelation in 3D geological modeling, Earth Sci. Inform., 13, 619–632, https://doi.org/10.1007/s12145-019-00436-6, 2020.
Liu, Z., Zhang, Z., Zhou, C., Ming, W., and Du, Z.: An Adaptive Inverse-Distance Weighting Interpolation Method Considering Spatial Differentiation in 3D Geological Modeling, Geosciences, 11, 51, https://doi.org/10.3390/geosciences11020051, 2021.
Livani, M., Scrocca, D., Gaudiosi, I., Mancini, M., Cavinato, G. P., de Franco, R., Caielli, G., Vignaroli, G., Romi, A., and Moscatelli, M.: A Geology-Based 3D Velocity Model of the Amatrice Basin (Central Italy), Eng. Geol., 306, 106741, https://doi.org/10.1016/j.enggeo.2022.106741, 2022.
Lysdahl, A. K., Christensen, C. W., Pfaffhuber, A. A., Vöge, M., Andresen, L., Skurdal, G. H., and Panzner, M.: Integrated Bedrock Model Combining Airborne Geophysics and Sparse Drillings Based On an Artificial Neural Network, Eng. Geol., 297, 106484, https://doi.org/10.1016/j.enggeo.2021.106484, 2022.
Lyu, M., Ren, B., Wu, B., Tong, D., Ge, S., and Han, S.: A Parametric 3D Geological Modeling Method Considering Stratigraphic Interface Topology Optimization and Coding Expert Knowledge, Eng. Geol., 293, 106300, https://doi.org/10.1016/j.enggeo.2021.106300, 2021.
Mallet, J. L.: Discrete Modeling for Natural Objects, Math. Geol., 29, 199–219, https://doi.org/10.1007/BF02769628, 1997.
Manchuk, J. G. and Deutsch, C. V.: Boundary Modeling with Moving Least Squares, Comput. Geosci., 126, 96–106, https://doi.org/10.1016/j.cageo.2019.02.006, 2019.
Martin, R. and Boisvert, J. B.: Iterative Refinement of Implicit Boundary Models for Improved Geological Feature Reproduction, Comput. Geosci., 109, 1–15, https://doi.org/10.1016/j.cageo.2017.07.003, 2017.
Marzan, I., Martí, D., Lobo, A., Alcalde, J., Ruiz, M., Alvarez-Marron, J., and Carbonell, R.: Joint Interpretation of Geophysical Data: Applying Machine Learning to the Modeling of an Evaporitic Sequence in Villar De Cañas (Spain), Eng. Geol., 288, 106126, https://doi.org/10.1016/j.enggeo.2021.106126, 2021.
Olivier, R. and Hanqiang, C.: Nearest Neighbor Value Interpolation, International Journal of Advanced Computer Science & Application, 3, 25–30, 2012.
Pakyuz-Charrier, E., Giraud, J., Ogarko, V., Lindsay, M., and Jessell, M.: Drillhole Uncertainty Propagation for Three-Dimensional Geological Modeling Using Monte Carlo, Tectonophysics, 747–748, 16–39, https://doi.org/10.1016/j.tecto.2018.09.005, 2018.
Ran, X. J. and Xue, L. F.: The research of method and system of regional three-dimensional geological modeling, Doctor Thesis, Jilin University, 2020.
Ray, P., Manach, Y. L., Riou, B., and Houle, T. T.: Statistical evaluation of a biomarker, Anesthesiology, 112, 1023–1040, https://doi.org/10.1097/ALN.0b013e3181d47604, 2010.
Shi, T., Zhong, D., and Wang, L.: Geological Modeling Method Based On the Normal Dynamic Estimation of Sparse Point Clouds, Mathematics, 9, 1819, https://doi.org/10.3390/math9151819, 2021.
Skala, V.: Rbf Interpolation with Csrbf of Large Data Sets, Proced. Comput. Sci., 108, 2433–2437, https://doi.org/10.1016/j.procs.2017.05.081, 2017.
Sun, H., Zhong, D., Wu, Z., and Wang, L.: Multi-Labeled Regularized Marching Tetrahedra Method for Implicit Geological Modeling, Math. Geosci., https://doi.org/10.1007/s11004-023-10075-9, 2023.
Thanh, H. V., Sugai, Y., Nguele, R., and Sasaki, K.: Integrated Workflow in 3D Geological Model Construction for Evaluation of Co2 Storage Capacity of a Fractured Basement Reservoir in Cuu Long Basin, Vietnam, Int. J. Greenh. Gas Con., 90, 102826, https://doi.org/10.1016/j.ijggc.2019.102826, 2019.
Thibaut, R., Laloy, E., and Hermans, T.: A New Framework for Experimental Design Using Bayesian Evidential Learning: The Case of Wellhead Protection Area, J. Hydrol., 603, 126903, https://doi.org/10.1016/j.jhydrol.2021.126903, 2021.
Titos, M., Bueno, A., Garcia, L., and Benitez, C.: A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events, IEEE J. Sel. Top. Appl. Earth Obs., 11, 1533–1544, https://doi.org/10.1109/JSTARS.2018.2803198, 2018.
Wang, G. and Huang, L.: 3D Geological Modeling for Mineral Resource Assessment of the Tongshan Cu Deposit, Heilongjiang Province, China, Geosci. Front., 3, 483–491, https://doi.org/10.1016/j.gsf.2011.12.012, 2012.
Wang, J. M., Zhao, H., Bi, L. and Wang, L. G.: Implicit 3D Modeling of Ore Body from Geological Boreholes Data Using Hermite Radial Basis Functions, Minerals, 8, 443, https://doi.org/10.3390/min8100443, 2018.
Whiteley, J. S., Watlet, A., Uhlemann, S., Wilkinson, P., Boyd, J. P., Jordan, C., Kendall, J. M., and Chambers, J. E.: Rapid Characterisation of Landslide Heterogeneity Using Unsupervised Classification of Electrical Resistivity and Seismic Refraction Surveys, Eng. Geol., 290, 106189, https://doi.org/10.1016/j.enggeo.2021.106189, 2021.
Wu, L. X.: Topological relations embodied in a generalized tri-prism (GTP) model for a 3D geoscience modeling system, Comput. Geosci., 30, 405–418, https://doi.org/10.1016/j.cageo.2003.06.005, 2004.
Xiong, Z., Guo, J., Xia, Y., Lu, H., Wang, M., and Shi, S.: A 3D Multi-Scale Geology Modeling Method for Tunnel Engineering Risk Assessment, Tunn. Undergr. Sp. Tech., 73, 71–81, https://doi.org/10.1016/j.tust.2017.12.003, 2018.
Xu, S. T. and Zhou, Y. Z.: Artificial intelligence identification of ore minerals under microscope based on deep learning algorithm, Acta Petrol. Sin., 34, 3244–3252, 2018.
Yang, Y. S., Li, Y. Y., Liu, T. Y., Zhan, Y. L., and Feng, J.: Interactive 3D forward modeling of total field surface and three-component borehole magnetic data for the Daye iron-ore deposit (Central China), J. Appl. Geophys., 75, 254–263, https://doi.org/10.1016/j.jappgeo.2011.07.010, 2011.
Zhang, T. F., Tilke, P., Dupont, E., Zhu, L.C., Liang, L., and Bailey, W.: Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks, Pet. Sci., 16, 541–549, https://doi.org/10.1007/s12182-019-0328-4, 2019.
Zhang, X. Y., Ye, P., Wang, S., and Du, M.: Geological entity recognition method based on Deep Belief Networks, Acta Petrol. Sin., 34, 343–351, 2018.
Zhang, Z., Wang, G., Liu, C., Cheng, L., and Sha, D.: Bagging-Based Positive-Unlabeled Learning Algorithm with Bayesian Hyperparameter Optimization for Three-Dimensional Mineral Potential Mapping, Comput. Geosci., 154, https://doi.org/10.1016/j.cageo.2021.104817, 2021.
Zhang, Z., Wang, G., Carranza, E. J. M., Yang, S., Zhao, K., Yang, W., and Sha, D.: Three-Dimensional Pseudo-Lithologic Modeling Via Adaptive Feature Weighted K-Means Algorithm From Multi-Source Geophysical Datasets, Qingchengzi Pb–Zn–Ag–Au District, China, Natural Resources Research, 31, 2163–2179, https://doi.org/10.1007/s11053-021-09927-0, 2022.
Zhang, Z., Wang, G., Carranza, E. J. M., Liu, C., Li, J., Fu, C., Liu, X., Chen, C., Fan, J., and Dong, Y.: An Integrated Machine Learning Framework with Uncertainty Quantification for Three-Dimensional Lithological Modeling From Multi-Source Geophysical Data and Drilling Data, Eng. Geol., 324, 107255, https://doi.org/10.1016/j.enggeo.2023.107255, 2023.
Zhong, D. Y., Wang, L. G., Bi, L., and Jia, M. T.: Implicit Modeling of Complex Orebody with Constraints of Geological Rules, T. Nonfer. Metal. Soc., 29, 2392–2399, https://doi.org/10.1016/S1003-6326(19)65145-9, 2019.
Zhong, D. Y., Wang, L. G., and Wang, J. M.: Combination Constraints of Multiple Fields for Implicit Modeling of Ore Bodies, Appl. Sci., 11, 1321, https://doi.org/10.3390/app11031321, 2021.
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. 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.
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological...
