高亏格坝基地质体Kmeans-ERT自动建模研究

Automatic modeling of high genus geological bodies at dam foundation based on Kmeans-ERT

高亏格地质体是多处镂空的复杂地质构造,如坝基的互层、破碎带等。基于地层面的地质建模方法在处理高亏格地质体时存在自动化程度低、主观误差较大等缺点;基于体元的建模方法虽然可以实现自动化,但存在数据冗余的不足,且难以适应水利水电工程钻孔数据分布不均、地质体局部突变等特点。针对上述问题,提出一种基于栅格体元的高亏格坝基地质体K均值-极端随机树(Kmeans-ERT)自动建模算法。首先,针对高亏格地质体的随机性和突变性,采用鲁棒性较强的极端随机树算法构建分类模型;其次,采用K-means算法对地层样本进行聚类,根据聚类结果动态调整分裂阈值;最后,提出边缘检测算法识别模型边界,进而隐藏内部体元,实现模型轻量化。工程应用表明,所提出模型可以实现坝基高亏格地质体的自动建模,平均准确率相较支持向量分类(SVC)、K近邻算法(KNN)、随机森林、深度森林和BP神经网络分别提高17.4%、19.1%、4.7%、6.5%和17.1%,模型内存缩减率达69.3%;与人工建模方法和其余自动建模算法相比,所提出模型在精度和效率上具有优势。

Geological bodies with high genus are complex structures that feature a variety of cavities,such as the interbedded strata and fracture zones at dam foundation.In handling these structures,the common geological modeling methods based on surface reconstruction have poor performance in automation and low accuracy,while methods using volume element representation can realize automatic modeling,but at a cost of redundant voxel data.Besides,previous algorithms can hardly suit the unevenly distributed borehole data or the multivariate shapes of high genus strata in hydropower engineering.This paper develops an automatic geological voxel modeling method based on the K-means-modified extremely randomized trees(Kmeans-ERT).First,to classify the ambiguous and complex high genus strata,ERT is selected as the base prediction model because of its robustness.Then,the K-means algorithm is adopted to modify ERT by adding a clustering analysis progress at each node to calculate dynamically the distribution of random split values.Moreover,a boundary recognition algorithm is constructed to optimize the model by hiding interior voxels.Engineering application shows our new model can automatically reconstruct high genus strata.Compared to SVC,KNN,random forest,deep forest,and BP neural network,the model improves the average accuracy by 17.4%,19.1%,4.7%,6.5%and 17.1%respectively,and it sees a 69.3%decrease in memory cost.This verifies our new method has accuracy and efficiency superior to manual geological modeling or other automatic algorithms.