基于地平面上受约束的连续集群的3D点云石质物质识别
Binqing Gan1, Ran Jing2, Yanlin Shao1
1School of Geosciences, Yangtze University, Wuhan, 430100, China.
Scientific reports
|October 7, 2025
概括
一个新的战略局限连续聚类 (SCCC) 框架改进了使用3D激光扫描进行地质突起分析. 这种方法提高了岩石学识别的准确性,特别是在复杂的区域,优于现有的技术.
科学领域:
- 地质科学 地质科学
- 计算地质学的计算地质学
- 遥感 遥感 遥感 遥感
背景情况:
- 从3D激光扫描数据中自动化石质学识别在边界模糊和复杂的平分层学领域面临挑战.
- 现有的方法在过渡区和复杂的地质构造中难以准确.
研究的目的:
- 开发和验证一种新的框架 - - 战略地理上受约束的连续聚类 (SCCC),用于在地质浮标中高精度地质学识别.
- 在具有挑战性的地质环境中提高分类准确性和边界划分.
主要方法:
- SCCC框架将横向连续性的沉积物学原理与动态密度值等级集群算法相结合.
- 一个补丁级特征聚合模块使用几何共变矩阵和光谱分布构建了一个多式特征空间.
- 石质学区分是使用随机森林分类器进行的.
主要成果:
- 在Qingshuihe形成的外露数据集上,SCCC实现了94.64%的整体准确性,94.58%的F1得分和90.87%的平均交叉与联合.
- 这种方法显著超过了传统的机器学习和深度学习方法的 26.22-68.36%.
- 平面图制约提高了计算效率,减少了83.3%的内存使用量和85.7%的处理时间.
结论:
- SCCC框架为智能地质勘探提供了一个高精度和可解释的途径.
- 它在识别岩石结构和划定边界方面表现出卓越的表现,特别是在过渡区和薄型交叉床上.
- 地质原理与计算模型的整合增强了外分析的稳定性和效率.
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