基于标量坐标的复杂地质表面模型隐式生成方法

doi:10.11720/wtyht.2025.2573

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Abstract

The construction and presentation of a geological modelprove to be ahot topic and challenge in research on 3D geological modelling. Given the large scale, involvement of complex surfaces, and insufficient geological constraints of geological body data, this study achieved the rapid construction of a large-scale geological surface model using the domain decomposition-based implicit generation method. Initially, implicit functionswere constructed by taking radial basis functions as the kernel functions.Then, the distribution functions of various domainswere solved in parallel usinganoverlapping domain decompositionmethod, reducing the spatiotemporalcost and accelerating the solving process.Subsequently, normal vectors were extracted to generate control pointsand formconstraints on surface fluctuation, thereby effectively controlling the model boundaries. The experimental results indicate that the method proposed in this study can significantly improve the efficiency associated with the solving of distribution functionswhile ensuring the high quality of the model. This study effectively solves the problem of balance between efficiency and precision in geological modeling and provides methodological support for the refinement of geological surfaces.

Key words： geological surface model implicit modelling radial basis function domain decomposition

Received: 27 December 2023 Published: 22 April 2025

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	Articles by authors
	Pei-Gang LIU
	Hao YUAN
	Kai-Xin XUE
	Zhao-Liang LI
	Zong-Min LI

Cite this article:

Pei-Gang LIU,Hao YUAN,Kai-Xin XUE, et al. Implicit generation of complex geological surface models based on scalar coordinates[J]. Geophysical and Geochemical Exploration, 2025, 49(2): 349-359.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.2573 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I2/349

Flowchart for model construction

Normal correction method

2D schematic of control points (red for control points inside the model, blue for control points outside the model)

Domain overlap
(ρ denotes domain overlap)

Longitudinal section

Cross section

Normal vectors for fixed viewpoints
(a~e represent the amplification effect of the normal vector pointing towards the external area of the model)

Schematic diagram of normal vector section with fixed viewpoint

Fig.7)
">

Corrected normal vector
(a~e represent the amplification effect after correcting the normal vector in the corresponding region of Fig.7)

Schematic diagram of the corrected normal vector section

Random selection of normal vectors
(a~e are the region amplification effects of random normal vector screening)

Schematic of a randomly selected section of a normal vector

Normal vector extremum selection
(a~e are the region amplification effects of normal vector extremum screening)

Schematic of normal vector extreme value selection cuts

Number of normal vectors in the region of random and extreme selection of normal vectors

Matrix complexity comparison

Run time comparison

Comparison of the results of random and extreme value selection of normal vectors
(a~c are locally enlarged areas)

Mean curvature of the model for different data volumes

Model effect for 1 000 data points

Model effect for 3 200 data points

Model effect for 5 000 data points

Model effect for 11 000 data points

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