[1]胡至华,袁 路,马东涛*,等.基于EEMD分形和LS-SVM的次声信号识别泥石流类型[J].山地学报,2020,(4):619-629.[doi:10.16089/j.cnki.1008-2786.000539]
　HU Zhihua,YUAN Lu,MA Dongtao*,et al.Identification of Debris Flow Types by Infrasound Signals Based on EEMD Fractal and LS-SVM[J].Mountain Research,2020,(4):619-629.[doi:10.16089/j.cnki.1008-2786.000539]
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基于EEMD分形和LS-SVM的次声信号识别泥石流类型()
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2020年第4期
页码:
619-629
栏目:
山地技术
出版日期:
2020-09-27

文章信息/Info

Title:
Identification of Debris Flow Types by Infrasound Signals Based on EEMD Fractal and LS-SVM
文章编号:
1008-2786-(2020)4-619-11
作者:
胡至华1; 2; 袁 路3; 马东涛1*; 胡雨豪4; 李 梅5
1.中国科学院、水利部成都山地灾害与环境研究所 中国科学院山地灾害与地表过程重点实验室,成都610041; 2.中国科学院大学,北京100049; 3.成都城投建设集团有限公司,成都610037; 4.中煤科工集团重庆设计研究院有限公司,重庆400016; 5.中国地质大学(北京),北京100083
Author(s):
HU Zhihua1; 2;  YUAN Lu3;  MA Dongtao1*;  HU Yuhao4;  LI Mei5
1. Institute of Mountain Hazards and Environment, CAS, Key Laboratory of Mountain Hazards and Earth Surface Process, CAS, Chengdu 610041, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. Construction Investment and Management Co. Ltd., CDCI, Chengdu 610037, China; 4. CCTEG Chongqing Engineering Co. Ltd, Chongqing 400016, China; 5. China University Of Geosciences, Beijing 100083, China
关键词:
泥石流;  类型;  次声;  EEMD分形;  STFT;  LS-SVM
Keywords:
debris flow;  type;  infrasound;  the Ensemble Empirical Mode Decomposition(EEMD)fractal;  the Short-Time Fourier Transform(STFT);  the Least Squares Support Vector Machines(LS-SVM)
分类号:
X43
DOI:
10.16089/j.cnki.1008-2786.000539
文献标志码:
A
摘要:
基于无接触的泥石流次声信号的有效波形特征提取识别不同类型的泥石流及预警泥石流规模、危害是国内外泥石流研究的新方向。本研究利用室内实验采集到的65次稀性、过渡性和粘性泥石流次声信号数据,采用集成经验模式分解(EEMD)对次声信号进行分解,提取本征模态函数(IMF)主分量,对比分析了原始信号和主分量信号STFT分布的时频特性差异,计算了主分量IMF盒维数值,并将其作为特征值输入最小二乘支持向量机(LS-SVM)分类器进行训练和分类,初步实现了基于次声分形特征指标识别泥石流类型。研究表明:(1)通过对EEMD重构的主IMF分量信号进行短时傅立叶变换(STFT)时频分析后,主分量信号具有优良的时频聚焦性能,提升了信号识别的准确性和精度;(2)稀性、过渡性和粘性泥石流的原始次声信号盒维数值分别为1.625、1.578和1.519,利用次声盒维数值可以识别泥石流的类型;(3)通过LS-LSVM模型训练测试,正确识别率达87%,其中稀性和过渡性泥石流为80%,粘性泥石流为100%。本研究利用次声特征指标无接触判识了泥石流类型,为次声自动识别、监测和预警泥石流灾害做了积极探索。
Abstract:
Infrasound signals produced by travelling debris flow are informative in recognizing debris flows. The waveform feature of the signals sampled by non-contact method can provide rich information in determining and pre-warning of debris flow types and magnitudes. In this study, by using signals collected at 65 debris flows with high and medium, as well as low viscosities, it extracted the principal components of the Intrinsic Mode Function(IMF)by the method of the Ensemble Empirical Mode Decomposition(EEMD), and compared the Short-Time Fourier Transform(STFT)distribution between the extracted signals and original signals. Then the box dimension of the principal components was input as eigenvalue in the Least Squares Support Vector Machines(LS-SVM)to train and classify the infrasound data for distinguishing debris flow types. It is found that:(1)The principal IMF component signal reconstructed by EEMD had excellent time-frequency focusing performance under STFT, which improved the accuracy and availability of signal recognition;(2)The original infrasound signals presented different fractal-box dimensions for debris flows with low, medium, and high viscosity, respectively of 1.625, 1.578 and 1.519. Through LS-SVM model training, the type recognition achieved accuracy of 87%. In conclusion, this research had realized the identification of debris flow types based on a non-contact infrasound signals and it will provide a novel way for monitoring and early warning of debris flow disasters.

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备注/Memo

备注/Memo:
收稿日期(Received date):2020-01-01; 改回日期(Accepted date):2020-07-13
基金项目(Foundation item):国家自然科学基金项目(41572347)。[National Natural Science Foundation of China(41572347)]
作者简介(Biography):胡至华(1994-),男,四川绵阳人,硕士研究生,主要研究方向:山地灾害防治工程与预警预报。[HU Zhihua(1994-), male, born in Mianyang, Sichuan province., M. Sc. candidate, research on geohazards prevention and monitoring] E-mail: zhihua6971@163.com
*通讯作者(Corresponding author):马东涛(1965-),男,博士,副研究员,主要研究方向:地质灾害基础理论研究和防灾减灾工程实践。[MA Dongtao(1965-), male, Ph.D., associate professor, specialized in geohazards prevention theory and engineering] E-mail: dtma@imde.ac.cn
更新日期/Last Update: 2020-07-30