Repair of Geological Models Based on Multiple Material Marching Cubes

Li, Benyu; Zhong, Deyun; Wang, Liguan

doi:10.3390/math9182207

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Presently, the primary aim of implicit modeling is the generation and simulation of geological boundaries, accomplished through automatic construction employing techniques like radial basis functions, variational Gaussian processes, and other methodologies [27][28][29]. To address concerns regarding overlap and gaps that arise in the modeling process, Li and colleagues [30] introduced a reconstruction algorithm based on the labeling of voxel points. This algorithm possesses the capability to resolve issues related to overlap and blanking in geological modeling through a labeling process.…”

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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rupMC: a ray-unit parallel marching cubes algorithm on CPU/GPU heterogeneous architectures

Yang,

Yun,

Guan

et al. 2024

International Journal of Digital Earth

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Finite element modelling of complex 3D image data with quantification and analysis

Chakkour

2024

Oxford Open Materials Science

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The purpose of this study was to examine how to model aggregated material microstructure and its meshing volumic generation that is provided by any data from 3D tomographic image data. The accurate reconstruction of 3D geometry structures from tomographic images is a powerful method in various application areas of materials science. The resulting mesh can be voxelized or conforming based on volumetric tetrahedral meshing. We investigate this creation depending on improving multiple materials marching cubes algorithm (M3C) with smoothing and remeshing algorithms. Then, a strategy for generating good-quality meshing and its robustness is presented, and this is performed with numerical tests. The novelty of this study is to generate a conforming mesh from complicated topology structures, particularly, when the interfaces of bi-materials are connected. This leads to a reduction in the node count in the generated mesh. The influence of some parameters involved in this algorithm is explored during different levels of meshing. In this work, the numerical homogenization approach from various spherical inclusions in the two-phase system using the algorithm M3C is considered to estimate the effective elastic properties. We created the framework with all the associated information, such as inputs in the format .inp files, to make it possible to run it over the Abaqus solver. Then, the Abaqus model based on the finite element method (FEM) was executed in this case for various material microstructures such as polycrystalline, composite, and fiber. We show the main workflow for providing desired results by visualizing the FEM analysis. We also demonstrate the capabilities of meshing methodology in the solver for these material models. The validation of the local mechanical environment from FEM with loading scenarios is achieved to predict displacements and deformations. Mechanical compression tests are performed to investigate the compressive behavior. Finally, stress-strain curves provided a comparison between simulations and experimental data for materials, and a good agreement is obtained.

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Repair of Geological Models Based on Multiple Material Marching Cubes

Cited by 4 publications

References 34 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

rupMC: a ray-unit parallel marching cubes algorithm on CPU/GPU heterogeneous architectures

Finite element modelling of complex 3D image data with quantification and analysis

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