Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application
Layer-Constrained Triangulated Irregular Network Algorithm Based on Ground Penetrating Radar Data and Its Application

In this paper, a layer-constrained triangulated irregular network (LC-TIN) algorithm is proposed for three-dimensional (3D) modelling, and applied to construct a 3D model for geological disease information based on ground penetrating radar (GPR) data. Compared with the traditional TIN algorithm, the LC-TIN algorithm introduced a layer constraint to the discrete data points during the 3D modelling process, and it can dynamically construct networks from layer to layer and implement 3D modelling for arbitrary shapes with high precision. The experimental results validated this method, the proposed algorithm not only can maintain the rationality of triangulation network, but also can obtain a good generation speed. In addition, the algorithm is also introduced to our self-developed 3D visualization platform, which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.
In this paper, a layer-constrained triangulated irregular network (LC-TIN) algorithm is proposed for three-dimensional (3D) modelling, and applied to construct a 3D model for geological disease information based on ground penetrating radar (GPR) data. Compared with the traditional TIN algorithm, the LC-TIN algorithm introduced a layer constraint to the discrete data points during the 3D modelling process, and it can dynamically construct networks from layer to layer and implement 3D modelling for arbitrary shapes with high precision. The experimental results validated this method, the proposed algorithm not only can maintain the rationality of triangulation network, but also can obtain a good generation speed. In addition, the algorithm is also introduced to our self-developed 3D visualization platform, which utilized GPR data to model geological diseases. Therefore the feasibility of the algorithm is verified in the practical application.