A parametric 3D geological modeling method considering stratigraphic interface topology optimization and coding expert knowledge

Lyu, Mingming; Ren, Bingyu; Wu, Binping; Tong, Dawei; Ge, Shicong; Han, Shuyang

doi:10.1016/j.enggeo.2021.106300

Cited by 24 publications

(6 citation statements)

References 95 publications

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“…Also, here, a wide range of functions have been proposed [5]. An important distinction lies in the representation of the interfaces, either in the form of explicit representations, for example, using NURBS or T-Splines [6,14], or as implicit representations [9,12]. See Wellmann and Caumon [5] for more details and the advantages and disadvantages of the different methods.…”

Section: Probabilistic Geological Model and Tbm Measurementmentioning

confidence: 99%

Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

Wellmann

Amann

Varga³

et al. 2022

Geomechanics and Tunnelling

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Geological models are commonly used to predict the position of relevant geological features, such as rock types or faults in the subsurface. These models can contain significant uncertainties, as the geological input parameters are often not perfectly known. Predictions of geological features, for example, on the level of a tunnel during an excavation process, are therefore uncertain. This work shows how these uncertainties can be estimated using probabilistic concepts. Furthermore, an approach is presented to automatically adjust the geological model predictions using measurements of the tunnel boring machine (TBM) operation. To this aim, the geological forward model is combined with a measurement model and both are integrated in a probabilistic machine learning framework. This integration enables a Bayesian inference process using computational methods, enabling an update of the parameters of the geological and measurement models. Based on the inferred parameter distributions, the model predictions on the tunnel level are subsequently updated. The application of the concept in a simple schematic application shows that such a combination can accurately and precisely predict features ahead of the operation within the limits of the model capabilities. In future work, the methods need to be tested with a real case study to evaluate the accuracy of predictions using real‐world TBM data and more complex geological models. The framework presented here could directly be extended to this purpose.

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Section: Probabilistic Geological Model and Tbm Measurementmentioning

confidence: 99%

Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

Wellmann

Amann

Varga³

et al. 2022

Geomechanics and Tunnelling

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“…Three-dimensional (3D) urban geological models are digital representations of subsurface strata and their associated features (Houlding, 1994). In recent years, the utilization of 3D geological models has expanded across various geological fields, such as mineral exploration (Zhang et al, 2021), geological storage (Thanh et al, 2019), groundwater resource estimation (Thibaut et al, 2021), geological disaster early warning generation (Høyer et al, 2019;Livani et al, 2022), and engineering geological condition evaluation (Chen et al, 2018;Guo et al, 2021;Lyu et al, 2021;Marzán et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The explicit modelling approach can be used to directly delineate geological formations and interpret tectonics based on borehole data. Explicit 3D geological modelling methods are widely used in the 3D modelling of mines and regional geological structures, and they include the interactive 3D forward modelling method (Yang et al, 2011), the generalized tri-prism (GTP) modelling method (Wu, 2004;Che et al, 2009), and the parametric surface method (Lyu et al, 2021). However, these approaches rely heavily on the expertise of geologists and often prove time-consuming and labour-intensive when dealing with large-scale borehole data.…”

Section: Introductionmentioning

confidence: 99%

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

Guo,

Xu,

Wang

et al. 2024

Geosci. Model Dev.

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Abstract. 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.

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“…Information model technology aims to digitally represent and simulate three-dimensional characteristics, including the structure of anchor engineering, the distribution of anchor components, and the geological conditions of a construction site [1], [2]. Through a three-dimensional model, designers can gain a more intuitive understanding of the structural composition and spatial layout of anchor engineering, facilitating effective problem identification, solution generation, and calculation of various project indicators [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Information Modeling Based on Incomplete Data for Anchor Engineering

Songan,

Wei,

Liang

et al. 2023

IEEE Access

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Intelligent design is a trend in geotechnical engineering. However, a middle link between demand identification and intelligent design is missing in anchor engineering, resulting in lower efficiency in terms of the interaction between the anchor components, geological body and modeling information. To address this challenge, this paper proposes a method for modeling a three-dimensional anchor engineering information model based on sparse boreholes. This method includes three parts: modeling the geological body, integrated modeling of the anchor components and the geological body, and multiple information assignments based on the anchor model. The feasibility was validated in actual engineering scenarios by using neural networks and rendering engines. The results showed that the prediction accuracy of the strata improved by 17.69%, the modeling efficiency increased by double, and engineering information was fit to the model well. This paper has the application potential to achieve intelligent design in anchor engineering.

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A parametric 3D geological modeling method considering stratigraphic interface topology optimization and coding expert knowledge

Cited by 24 publications

References 95 publications

Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

Automated geological model updates during TBM operation – An approach based on probabilistic machine learning concepts

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

Three-Dimensional Information Modeling Based on Incomplete Data for Anchor Engineering

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