基于改进LeNet5卷积神经网络的微震监测波形识别与过程解释
Waveform recognition and process interpretation of microseismic monitoring based on an improved LeNet5 convolutional neural network
李佳明 1唐世斌 1翁方文 2李焜耀 2要华伟 3何青源4
作者信息
- 1. State Key Laboratory of Coastal and Offshore Engineering(Dalian University of Technology),Dalian 116024,China
- 2. Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure,CCCC Second Harbor Engineering Company Ltd.,Wuhan 430000,China
- 3. Shanxi Coking Coal Xishan Coal and Electricity Group,Taiyuan 030053,China
- 4. State Key Laboratory of Coal Resources and Safe Mining(China University of Mining and Technology),Xuzhou 221116,China
- 折叠
摘要
在微震大数据时代背景下,开发高精度、可解释、适应性强的波形自动分类算法变得越来越重要.针对现有网络波形识别和分类的不足,基于LeNet框架提出了一种适用于微震监测波形识别的改进模型.应用改进后的模型对引汉济渭工程8个月内出现的13种微震监测信号进行了研究.结果表明,改进模型中最佳框架的精度为0.98,比原模型提高了0.10.所有改进模型的平均精确度、召回率和F1值分别提高了0.11、0.12和0.12.同时,改进后的模型可以对整个波形的识别过程可视化.在某些信号类别中,改进的模型主要通过关注背景信息而不是波形来分类,为微震监测工程中信号的智能分类提供了参考.
Abstract
The development of high-precision and interpretable automatic waveform classification algorithms with strong adaptability is becoming increasingly significant under the background of the big data era of microseismicity. Considering the deficiency of the existing network in waveform recognition and classification, an improved model which is suitable for microseismic (MS) monitoring waveform recognition was proposed in this study based on the LeNet framework. The improved model was applied to investigate thirteen kinds of MS monitoring signals that appear within 8 months of the Hanjiang-to-Weihe River Diversion Project. The results show that the accuracy of the best framework in the improved model is 0.98, which is 0.1 higher than original model. The average precision, recall and F1 values of all improved models increased by 0.11, 0.12 and 0.12, respectively. Meanwhile, the improved model can visualize the entire waveform recognition process. A novel observation is that in some signal categories, the improved model mainly classified by focusing on the background information instead of the waveforms. It provides a reference for the intelligent classification of signals in MS monitoring engineering.
关键词
微震监测/波形分类/改进LeNet/可解释性机器学习Key words
microseismic monitoring/waveform classification/improved LeNet/interpretable machine learning引用本文复制引用
基金项目
National Natural Science Foundation of China(51874065)
National Natural Science Foundation of China(U1903112)
National Natural Science Foundation of China(41941018)
出版年
2023
NETL
NSTL
维普
万方数据