Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Gang Yang, Min Zeng, Xiaohong Lin, Songbai Li, Haoxiang Yang, Lingyan Shen
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引用次数: 0

Abstract

Different geographical locations have different time series and types of earthquake early warning data of hydropower stations, and the packet loss rate in data sharing is high. In this regard, a real-time sharing algorithm of earthquake early warning data of hydropower stations based on deep learning is proposed. The compressed sensing method is used to collect the seismic data of the hydropower station, and the dictionary learning algorithm based on ordered parallel atomic updating is introduced to improve the compressed sensing process and to sparse the seismic data of the hydropower station. Combining FCOS and DNN, the seismic velocity spectrum is picked up from the collected seismic data and used as the input of the convolutional neural network. The real-time sharing of earthquake early warning data is realized using the CDMA1x network and TCP data transmission protocol. Experiments show that the algorithm can accurately pick up the regional seismic velocity spectrum of hydropower stations, the packet loss rate of earthquake early warning data transmission is low, and the sharing results contain a variety of information, which can provide a variety of data for people who need information and has strong practicability.

Abstract Image

基于深度学习的水电站地震预警数据实时共享算法
不同地理位置的水电站地震预警数据具有不同的时间序列和类型,数据共享时丢包率较高。为此,提出了一种基于深度学习的水电站地震预警数据实时共享算法。采用压缩感知方法采集水电站地震数据,引入基于有序并行原子更新的字典学习算法,改进压缩感知过程,稀疏水电站地震数据。结合 FCOS 和 DNN,从采集到的地震数据中提取地震速度谱,并将其作为卷积神经网络的输入。利用 CDMA1x 网络和 TCP 数据传输协议实现了地震预警数据的实时共享。实验表明,该算法能准确拾取水电站区域地震速度谱,地震预警数据传输丢包率低,共享结果包含多种信息,能为需要信息的人提供多种数据,具有很强的实用性。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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