As the important data basis of surrounding rock classification, rock mass structural information obtained by traditional image processing and feature extraction algorithms could not be quantitatively analyzed because of the uncertainty and geometric randomness of structural planes. In this paper, based on straight line detection, intelligent scissors, and morphological edge detection algorithms, the multiple interpretation system of rock mass image including linear bunching extraction, magnetic tracking extraction, and multiparameter characterization was researched and developed, and the actual distribution information and the related probability distribution model of structural planes could be obtained directly. On the basis of this, plenty of corresponding random rating-values meeting the probability distribution models of these evaluation indices were gained by Monte Carlo Simulation. The distribution probability affiliated with different rock mass grade was attained by inductive statistics, and the robust evaluation of surrounding rock classification could be carried out. Taking the robust results as learning samples, the response model of surrounding rock grade based on Gaussian process classification was established, making the evaluation of surrounding rock subclassification more rapid and robust.

中文翻译：

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基于岩体结构数字化表征的围岩分类高斯过程模型及其应用

作为围岩分类的重要数据基础，传统的图像处理和特征提取算法获得的岩体结构信息由于结构面的不确定性和几何随机性而无法进行定量分析。本文基于直线检测，智能剪刀和形态学边缘检测算法，研究和开发了岩体图像的线性解聚，磁跟踪提取和多参数表征的多解释系统，并给出了实际分布信息和信息。可以直接获得结构面的相关概率分布模型。基于此，蒙特卡罗模拟获得了大量满足这些评估指标概率分布模型的相应随机评分值。通过归纳统计，获得了与不同岩体等级有关的分布概率，并对围岩分类进行了稳健的评价。以鲁棒性结果为学习样本，建立了基于高斯过程分类的围岩坡度响应模型，使围岩亚类的评价更加快速，鲁棒。