International Journal of Geo-Energy最新文献

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Research on Drilling Test of Grouting Rock Mass Based on Cutting Strength 基于切割强度的注浆岩体钻孔试验研究

International Journal of Geo-Energy Pub Date : 2022-12-26 DOI: 10.58531/ijge103100002

Wansheng Chen, Hongke Gao, Hui Liu, Fei Chen, Yuxiang Cao, Fengling Ma, Chun Zhu, S. Cai, Jiajie Li

引用次数: 0

Study on Denoising Microseismic Signal Based on Autoencoder Convolutional Neural Networks 基于自编码器卷积神经网络的微地震信号去噪研究

International Journal of Geo-Energy Pub Date : 2022-12-26 DOI: 10.58531/ijge10310001

S.B. Tang, Fusheng Liu, Chun Zhu, Ximao Chen, Xingzhao Wang, Z. Wang, Leyu Chao, Yan Su, Li Zhao, Jiaming Li, Shun Ding, Muhuo Lai

{"title":"Study on Denoising Microseismic Signal Based on Autoencoder Convolutional Neural Networks","authors":"S.B. Tang, Fusheng Liu, Chun Zhu, Ximao Chen, Xingzhao Wang, Z. Wang, Leyu Chao, Yan Su, Li Zhao, Jiaming Li, Shun Ding, Muhuo Lai","doi":"10.58531/ijge10310001","DOIUrl":"https://doi.org/10.58531/ijge10310001","url":null,"abstract":"As a kind of dynamic real-time monitoring technology, microseismic monitoring technology has been widely used for rockburst warning. Due to the complexity of the actual monitoring environment, the monitoring signals often contain different types of noise, affecting the earning warning of rockburst. In this study, an Autoencoder Convolutional Neural Network denoising model based on deep learning has been proposed to denoising of the complex signals. The unsupervised adaptive training method is used to train the model, which only needs to set its initial parameters. The importance of an enhanced training dataset is illustrated by the comparison experiment. The results indicate that the training and verification shows well performance during training. The denoising efficiency of the proposed model is studied by the denoising of the synthetic noise-containing signals. Furthermore, the dataset from the water conveyance tunnel in the Hanjiang-to-Weihe River water diversion project (HJ-Project) in Shaanxi Province is taken as an engineering example to evolute the performance of the proposed model for practical project. The denoising performance of the model is analysed through the visual denoising results and evaluation index. The model can effectively denoise the complex noised signal which separate it into pure microseismic signal and noise signal, and improve the signal-to-noise ratio, which is benefit for arrive-time picking and source locating then improve the performance of early warning of rockburst.","PeriodicalId":124618,"journal":{"name":"International Journal of Geo-Energy","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130909982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0