Multi-labeled Regularized Marching Tetrahedra Method for Implicit Geological Modeling

Sun, Hao; Zhong, Deyun; Wu, Zhaohao; Wang, Liguan

doi:10.1007/s11004-023-10075-9

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…This algorithm possesses the capability to resolve issues related to overlap and blanking in geological modeling through a labeling process. The multi-label regularized tetrahedron method introduced by Sun and colleagues [31] can be effectively employed in the surface reconstruction of multi-geoidal bodies.…”

Section: Implicit Geological Modelingmentioning

confidence: 99%

Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

Liu,

Li,

2024

Applied Sciences

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Three-dimensional (3D) models provide the most intuitive representation of geological conditions. Traditional modeling methods heavily depend on technicians’ expertise and lack ease of updating. In this study, we introduce a deep learning-based method for 3D geological implicit modeling, leveraging a substantial dataset of geological drilling data. By applying resampling and normalization techniques, we standardize drilling data and significantly expand the dataset, making it suitable for training deep neural networks. Utilizing the characteristics of the sample data, we design and establish the network structure, loss function, and parameter configurations, resulting in the training of a deep neural network with high accuracy and robust generalization capability. Ultimately, we utilize the dataset generated from the network’s predictions to render and construct the 3D geological model. The research in this paper demonstrates the significant promise of deep neural networks in addressing geological challenges. The deep learning-based implicit 3D modeling method surpasses traditional approaches in terms of generalization, convenience, and adaptability.

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Section: Implicit Geological Modelingmentioning

confidence: 99%

Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

Liu,

Li,

2024

Applied Sciences

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“…Further research on the use of the marching tetrahedra method in the field of geological modeling can be found in [36,37].…”

Section: Y Zhou Et Al Clarify Inmentioning

confidence: 99%

A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives

Büscher,

Degel,

Oellerich

2024

Algorithms

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This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring.

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Towards automatic and rapid 3D geological modelling of urban sedimentary strata from a large amount of borehole data using a parallel solution of implicit equations

Wang,

Guo,

et al. 2023

Earth Sci Inform

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Multi-labeled Regularized Marching Tetrahedra Method for Implicit Geological Modeling

Cited by 4 publications

References 36 publications

Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives

Towards automatic and rapid 3D geological modelling of urban sedimentary strata from a large amount of borehole data using a parallel solution of implicit equations

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