Implicit modeling of complex orebody with constraints of geological rules

Zhong, Deyun; Wang, Liguan; Bi, Lin; Jia, Mingtao

doi:10.1016/s1003-6326(19)65145-9

Cited by 28 publications

(15 citation statements)

References 14 publications

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“…The appropriate combination operations or modeling sequence should be specified according to the corresponding geological rule. The RBF-based methods were implemented to interpolate the raw drillhole data [40] and the user-defined contour data [41] for each sub-field. Several examples of orebody implicit modeling were studied to analyze the combination constraints of multiple fields.…”

Section: Case Studiesmentioning

confidence: 99%

Combination Constraints of Multiple Fields for Implicit Modeling of Ore Bodies

Zhong

Wang

2021

Applied Sciences

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In this paper, we introduce combination constraints for modeling ore bodies based on multiple implicit fields interpolation. The basic idea of the method is to define a multi-labeled implicit function that combines different sub-implicit fields by the combination operations, including intersection, union and difference operators. The contribution of this paper resides in the application of combination of more general implicit fields with combination rules for the implicit modeling of ore bodies, such that the geologist can construct constraints honoring geological relationships more flexibly. To improve the efficiency of implicit surface reconstruction, a pruning strategy is used to avoid unnecessary calculations based on the hierarchical bounding box of the operation tree. Different RBF-based methods are utilized to study the implicit modeling cases of ore bodies. The experimental results of several datasets show that the combination constraints are useful to reconstruct implicit surfaces for ore bodies with mineralization rules involving multiple fields.

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Section: Case Studiesmentioning

confidence: 99%

Combination Constraints of Multiple Fields for Implicit Modeling of Ore Bodies

Zhong

Wang

2021

Applied Sciences

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“…The main ideas of the implicit modeling method [33] of ore body based on geological sampling data are as follows. First, the method uses discretization to convert corresponding types of geological data into interpolation constraints at a certain sampling interval.…”

Section: Implicit Modelingmentioning

confidence: 99%

Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

2021

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The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.

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“…The lack of data support in the sparse domain makes it difficult to reconstruct the continuous trend between contours. Sim- ilar to the GRBF interpolant [5], besides constructing the constraints automatically, some interactive constraints can be appended to change the interpolation trend manually, as shown in Figure 3. These user inputs guide the interpolant toward more satisfactory results in different applications.…”

Section: Interactive Constraintsmentioning

confidence: 99%

“…There are many applications for the RBF-based methods with extended constraints in surface reconstruction. Similar to the generalized radial basis functions interpolant (GRBF), based on the interpolation constraints, some interactive constraints [5], including the trend line and the constraint line, can be constructed to control the local trend of shape interactively. It is useful when the method interpolates sparse data that satisfies all the constraints but exhibits an undesirable trend of shape.…”

Section: Introductionmentioning

confidence: 99%

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Extended Hermite Radial Basis Functions for Sparse Contours Interpolation

Zhong

Wang

2020

IEEE Access

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In this paper, we present an extended Hermite radial basis functions interpolant for surface reconstruction of sparse contours that allows for shape control with interactive constraints. Similar to the differential operator, the difference operator is used to construct gradient constraint and tangent constraint. Based on the theory of Hermite-Birkhoff interpolation, to construct more flexible geometry constraints, we incorporate the differential operator and difference operator to construct interpolation conditions in the interpolant. We construct some constraint rules to control the local trend of shape interactively. It is useful when the method interpolates sparse data that satisfies all the constraints but exhibits an undesirable trend of shape. For example, the interactive constraints can be used to fix holes or intersections for geometrically valid meshes. Regarding the geometry domain as a signed distance field of implicit function, we implement a constraint-based approach to interpolate the contours using the interpolant. The improved method can flexibly handle both parallel and non-parallel sparse contours. The numerical results of real geological and medical data show the robustness and performance of the extended Hermite radial basis functions interpolant. INDEX TERMS Hermite radial basis functions, radial basis functions, contours interpolation, signed distance field, geometry constraints.

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Implicit modeling of complex orebody with constraints of geological rules

Cited by 28 publications

References 14 publications

Combination Constraints of Multiple Fields for Implicit Modeling of Ore Bodies

Combination Constraints of Multiple Fields for Implicit Modeling of Ore Bodies

Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

Extended Hermite Radial Basis Functions for Sparse Contours Interpolation

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