基于图像融合特征的水工岩体完整性评价方法

Rock mass integrity evaluation method in hydraulic engineering based on ensemble features of core images

水利工程多建设在地质条件复杂多变的高山峡谷,因此坝址及库区的岩体完整性评价对于工程建设具有重要意义。但岩体完整性评价常采用手工作业的方式,经济成本和时间成本较高。针对这一问题,本研究采用知识迁移的方法实现岩心图像特征的深度融合,进而提出基于权重支持向量机(WeightedSupportVectorMachine,WSVM)的岩体质量评价模型,最终实现水工岩体完整性的智能评价。通过对比基于单一深度模型特征和深度融合特征的水工岩体完整性智能评价模型可以发现,深度特征融合能有效提升模型性能,准确率提升5%以上;另一方面,本文也对比了WSVM和SVM及其他机器学习模型,证明了WSVM模型在水工岩体完整性智能评价中的有效性。本文提出的方法能在一定程度上实现水工岩体完整性评价的自动化和智能化分析,为水工地质勘察和水利工程建设提供新的方法。

Hydraulic engineering projects are mostly located in regions of high mountains and deep gorges with complicated geological conditions.Evaluation of the integrity of rock mass in the dam site and reservoir area is significant to the construction,but traditional evaluation,often completed manually,is laborious and of high cost.In this study,a knowledge transfer method is applied to achieve a deep feature ensemble of rock core images;then an intelligent evaluation model is developed for hydraulic rock mass integrity based on Weighted Support Vector Machine(WSVM).We compare the evaluation results obtained using a single deep model and a deep feature ensemble method,and find that the latter improves model performance better,raising the accuracy by more than 5%.We also compare WSVM with SVM and other machine learning methods.The results prove that WSVM is more effective in the intelligent evaluation of hydraulic rock mass integrity.It realizes automatic and intelligent analysis of the integrity evaluation to a certain extent,and provides a new method for geological survey and hydraulic construction.