基于改进残差网络的陷落柱识别方法

A method for identifying karst collapse columns based on an improved residual network

矿区地质条件复杂,高效、准确的陷落柱识别至关重要。在传统方法中,解释陷落柱最常采用的是人机交互解释方法,但随着勘探规模的扩大、生成数据的不断积累,传统的人工解释陷落柱已满足不了实际生产需要。为了提高陷落柱的识别精度,提出了CINet(Collapse Column Identification Network)网络模型,把自动识别陷落柱的方法看作二分类问题,在深度学习算法残差网络的基础上对残差模块进行改进,构建出新的网络模型CINet,引入平衡交叉熵损失,解决数据中陷落柱与非陷落柱比例高度不平衡问题,使模型朝着正确的方向收敛。通过模型的预测结果与实际数据比对表明,相比于传统的机器学习和残差网络模型,CINet网络模型可以从原始数据中学习到更加详细的特征信息,提高陷落柱的识别精度,F1评分可以达到91.10%,实现了陷落柱快速精准识别,对预防地质灾害的发生具有较好的指导作用。

Coal mining areas have complex geological conditions,and it is critical to predict and identify karst collapse columns in these areas efficiently and accurately.Human-computer interaction is the most frequently employed to interpret karst collapse columns in traditional methods.However,with an increase in the exploration scale and the constant accumulation of generated data,the traditional manual interpretation of karst collapse columns cannot meet the demand for actual coal mining.To improve the identification accuracy of karst collapse columns,this study proposes a Collapse Column Identification Network(CINet)model,which takes the automatic recognition of karst collapse columns as binary classification.The new CINet model is constructed by improving the residual module through the deep learning of residual networks.Moreover,the balanced-cross entropy loss is introduced into the new model to solve the highly imbalanced proportions of data on karst collapse columns and non-karst collapse columns.In this manner,the network model can converge in the correct direction.As shown by the comparison between the results predicted using the CINet model and the actual data,the CINet model can learn more detailed information on features of karst collapse columns from the original data than the traditional machine learning and residual network models,thus improving the identification accuracy.With an F1 of up to 91.10%,the CINet model allows for the rapid and accurate identification of karst collapse columns.This study can serve as a guide for the prevention of geological disasters in coal mining areas.