• DocumentCode
    1854031
  • Title

    Using AR Model and BP Neural Network to Identify Microseism Signal

  • Author

    Chang-peng, Ji ; Li-li, Liu

  • Author_Institution
    Sch. of Electron. & Inf. Eng., Liaoning Tech. Univ., Huludao, China
  • fYear
    2010
  • fDate
    22-24 Jan. 2010
  • Firstpage
    134
  • Lastpage
    138
  • Abstract
    According to the characteristics of broad frequency and abundant spectral components of mine microseismic signal, we use AR model parameters and BP neural network to propose a method of filtering treatment for the signal and noise with different frequency ranges. We can use this method to separate noise and signal, and decompose different frequency band signals, so we can achieve the goal of filtering. The experimental results suggest that we can effectively remove the noise of microseismic abnormal signal by using AR model parameters and BP neural network, and this method can be used in the microseismic prediction and the pretreatment of microseismic signal.
  • Keywords
    autoregressive processes; backpropagation; filtering theory; geophysical signal processing; neural nets; seismic waves; seismology; signal denoising; AR model; BP neural network; filtering treatment method; frequency band signals; microseism signal identification; microseismic abnormal signal; Fluctuations; Frequency; Geology; Information filtering; Information filters; Interference; Neural networks; Predictive models; Signal analysis; Signal processing; AR model; BP neural network; identify; microseism signal;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Future Networks, 2010. ICFN '10. Second International Conference on
  • Conference_Location
    Sanya, Hainan
  • Print_ISBN
    978-0-7695-3940-9
  • Electronic_ISBN
    978-1-4244-5667-3
  • Type

    conf

  • DOI
    10.1109/ICFN.2010.26
  • Filename
    5431866