Fast 3D Visualization of Massive Geological Data Based on Clustering Index Fusion

Abstract

With the development of $3D$ visualization technology, the amount of geological data information is increasing, and the interactive display of big data faces severe challenges. Because traditional volume rendering methods cannot entirely load large-scale data into the memory owing to hardware limitations, a visualization method based on variational deep embedding clustering fusion Hilbert R-tree is proposed to solve slow display and stuttering issues when rendering massive geological data. By constructing an efficient data index structure, deep clustering algorithms and space-filling curves can be integrated into the data structure to improve the indexing efficiency. In addition, this method combines time forecasting, data scheduling, and loading modules to improve the accuracy and real-time data display rate, thereby improving the stability of $3D$ visualization of large-scale geological data. This method uses real geological data as the experimental dataset, comparing and analyzing the existing index structure and time-series prediction method. The experimental results indicate that when comparing the index of the variational deep embedded clustering-Hilbert R-tree ( $VDEC-HRT$ ) with that of the K-means Hilbert R-tree ( $KHRT$ ), the time required is reduced by 55.67%, the viewpoint prediction correctness of the proposed method is improved by 22.7% compared with Lagrange interpolation algorithm. And the overall rendering performance and quality of the system achieve the expected results. Ours experiments prove the feasibility and effectiveness of the proposed scheme in the visualization of large-scale geological data.

Keywords

• D visualization of massive data%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> 3<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">D</italic> visualization of massive data
• deep learning
• Hilbert R-tree
• deep clustering
• time-series forecasting
• view frustum culling