| 杜家佳,卜凡,杜国平,王永利,宋晓峰,杜建平.声呐渗流测量数据降噪的分类模型研究[J].声学技术,2021,40(3):429~434 |
| 声呐渗流测量数据降噪的分类模型研究 |
| Study on classification model of sonar seepage detection results |
| 投稿时间：2020-03-20 修订日期：2020-04-27 |
| DOI：10.16300/j.cnki.1000-3630.2021.03.021 |
| 中文关键词: 声呐分类 声呐渗流检测 渗流测井 水库渗漏 梯度提升树 ReliefF算法 |
| 英文关键词: sonar classification sonar seepage detection seepage logging reservoir leakage gradient boosting decision tree (GBDT) ReliefF algorithm |
| 基金项目:国家重点研发计划项目（2016YFC0401604）、国家自然科学基金（61941113）、南京市科技计划项目（201805036）、“十三五”装备领域基金（61403120501）。 |
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| 中文摘要: |
| 渗漏造成的一系列安全隐患已严重威胁到地下隐蔽工程的建设与正常运行。为了研发新的渗流测量手段与技术方法，减少控制渗漏事故的发生，提出了一种基于梯度提升树的声呐渗流检测结果分类模型。模型利用ReliefF算法选取贡献权重大的特征作为训练数据集，利用属性标注的数据集训练出区分水库渗流、井孔渗流与噪声的梯度提升树模型。实验结果表明，所提出的分类模型具有良好的识别性能，在训练集上实现了高达96.6%的准确度，且在实际使用中能够较为准确地识别噪声干扰。 |
| 英文摘要: |
| A series of potential safety hazards caused by leakage have seriously threatened the construction and normal operation of underground concealed works. In order to develop new seepage measurement methods to reduce the seepage control accidents, a classification model of sonar seepage detection results based on gradient boosting decision tree (GBDT) is proposed. The ReliefF algorithm is used to select the features with significant contribution weights as the training data set, and the expert annotated data are used to develop the gradient lifting tree model for distinguishing reservoir seepage, well seepage and noise. The experimental results show that the classification model proposed in this paper has good recognition performance, and the accuracy in the training set is as high as 96.6%. |
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