Skip to main content

Advertisement

SpringerLink
Log in

An improved method of local mean decomposition with adaptive noise and its application to microseismic signal processing in rock engineering

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

The processing and interpretation of microseismic (MS) signals are the basis for obtaining source information of MS events in rock engineering. However, MS signal processing is subjective and time-consuming, in which different strategies have to be empirically adopted to cope with different issues. To facilitate MS signal analysis, this study proposes an entirely data-driven method based on local mean decomposition (LMD), which has been applied in various signal processing fields. Through some numerical experiments, the superiorities (e.g., smaller reconstruction errors and fewer sifting iterations) of the proposed method over other methods are demonstrated, and the proposed method is identified as an effective filtering and frequency analysis tool to process recorded MS signals. Our analysis results show that the proposed method can effectively remove interfering noise existing in MS signals collected from the Lianghekou hydropower station during underground rock excavation. Moreover, the proposed method can be employed to analyze the frequency revolution features of MS signals before and after rock mass failure. In addition, it is found that MS signal frequency migrates to a low-frequency range when approaching rock failure, which can be regarded as an early warning indicator of rock instability. The proposed method is believed to facilitate MS monitoring and offer a new clue to conduct MS signal processing and interpretation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Brune JN (1970) Tectonic stress and the spectra of seismic shear waves from earthquakes. J Geophys Res 75(26):4997–5009

    Article  Google Scholar 

  • Burlini L, Toro GD (2008) Volcanic symphony in the lab. Science 322(5899):207–208

    Article  Google Scholar 

  • Cai M, Kaiser PK, Martin CD (2001) Quantification of rock mass damage in underground excavations from microseismic event monitoring. Int J Rock Mech Min 38(8):1135–1145

    Article  Google Scholar 

  • Colominas MA, Schlotthauer G, Torres ME (2014) Improved complete Ensemble EMD: a suitable tool for biomedical signal processing. Biomed Signal Proces 14(1):19–29

  • Dai F, Jiang P, Xu N, Chen W, Tan YF (2018) Focal mechanism determination for microseismic events and its application to the left bank slope of the Baihetan hydropower station in China. Environ Earth Sci 77(7):268

    Article  Google Scholar 

  • Dong LJ, Wesseloo J, Potvin Y, Li XB (2016) Discriminant models of blasts and seismic events in mine seismology. Int J Rock Mech Min 86:282–291

    Article  Google Scholar 

  • Dong LJ, Sun DY, Li XB, Ma J, Zhang LY, Tong XJ (2018) Interval non-probabilistic reliability of surrounding jointed rockmass considering microseismic loads in mining tunnels. Tunn Undergr Sp Tech 81:326–335

    Article  Google Scholar 

  • Ebrahimian Z, Ahmadi M, Sadri S, Li BQ, Moradian O (2019) Wavelet analysis of acoustic emissions associated with cracking in rocks. Eng Fract Mech 217:106516

  • Eisner L, Gei D, Hallo M, Opršal I, Ali MY (2013) The peak frequency of direct waves for microseismic events. Geophysics 78(6):A45–A49

    Article  Google Scholar 

  • Feng GL, Feng XT, Chen BR, Xiao YX, Jiang Q (2014) Sectional velocity model for microseismic source location in tunnels. Tunn Undergr Sp Tech 45:73–83

    Article  Google Scholar 

  • Feng GL, Feng XT, Chen BR, Xiao YX (2015a) Microseismic sequences associated with rockbursts in the tunnels of the Jinping II hydropower station. Int J Rock Mech Min 80:89–100

    Article  Google Scholar 

  • Feng GL, Feng XT, Chen BR, Xiao YX, Yu Y (2015b) A microseismic method for dynamic warning of rockburst development processes in tunnels. Rock Mech Rock Eng 48(5):2061–2076

    Article  Google Scholar 

  • Gómez JL, Velis DR (2016) A simple method inspired by Empirical Mode Decomposition for denoising seismic data. Geophysics 81(6):403–413

    Article  Google Scholar 

  • Haskell NA (2013) Total energy and energy spectral density of elastic wave radiation from propagating faults. B Seismol Soc Am 54(6A):1811–1841

    Article  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London, Ser A 454(1971):903–995

    Article  Google Scholar 

  • Huang NE, Wu Z (2008) A review on Hilbert-Hung transform method and its applications to geophysical studies. Rev Geophys 46(2):1–23

    Article  Google Scholar 

  • Hu L, Feng XT, Xiao YX, Wang R, Feng GL, Yao ZB, Niu WJ, Zhang W (2020) Effects of structural planes on rockburst position with respect to tunnel cross-sections: a case study involving a railway tunnel in China. B Eng Geol Environ 79:1061–1081

    Article  Google Scholar 

  • Jia RS, Zhao TB, Sun HM, Xiang-Hong Y (2015) Micro-seismic signal denoising method based on empirical mode decomposition and independent component analysis. Chinese J Geophys-Ch 58(3):1013–1023

  • Jiang RC, Dai F, Liu Y, Wei MD (2020) An automatic classification method for microseismic events and blasts during rock excavation of underground caverns. Tunn Undergr Sp Tech 101:103425

  • Jiang RC, Dai F, Liu Y, Li A (2021a) Fast marching method for microseismic source location in cavern-containing rockmass: performance analysis and engineering application. Engineering. https://doi.org/10.1016/j.eng.2020.10.019

    Article  Google Scholar 

  • Jiang RC, Dai F, Liu Y, Li A (2021b) A novel method for automatic identification of rock fracture signals in microseismic monitoring. Measurement 175:109129

  • Li B, Xu NW, Dai F, Gu GK, Ke W (2020a) Microseismic monitoring and stability analysis for the large-scale underground caverns at the Wudongde hydropower station. B Eng Geol Environ 79:3559–3573

  • Li A, Liu Y, Dai F (2020b) Continuum analysis of the structurally controlled displacements for large-scale underground caverns in bedded rock masses. Tunn Undergr Sp Tech 97:103288

  • Li XL, Li ZH, Wang EY, Feng JJ, Chen L, Li N, Kong XG (2016a) Extraction of microseismic waveforms characteristics prior to rock burst using Hilbert-Huang transform. Measurement 91:101–113

    Article  Google Scholar 

  • Li XL, Li ZH, Wang EY, Feng JJ, Chen L, Li N, Zou QL (2016b) Microseismic signal spectra, energy characteristics, and fractal features prior to rock burst: a case study from the Qianqiu Coal Mine. China J Earthq Eng 21(6):891–911

    Article  Google Scholar 

  • Li XL, Li ZH, Wang EY, Feng JJ, Kong XG, Chen L, Li BL, Li N (2016c) Analysis of natural mineral earthquake and blast based on Hilbert-Huang transform (HHT). J Appl Geophys 128:79–86

    Article  Google Scholar 

  • Liu Y, Dai F (2018) A damage constitutive model for intermittent jointed rocks under cyclic uniaxial compression. Int J Rock Mech Min 103:289–301

    Article  Google Scholar 

  • Liu Y, Dai F (2021) A review of experimental and theoretical research on the deformation and failure behavior of rocks subjected to cyclic loading. J Rock Mech Geotech. https://doi.org/10.1016/j.jrmge.2021.03.012

  • Liu Z, Jin Y, Zuo MJ, Feng Z (2016) Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis. Mech Syst Signal Pr 95:468–487

    Article  Google Scholar 

  • Lu CP, Dou LM, Zhang N, Xue JH, Wang XN, Liu H, Zhang JW (2013) Microseismic frequency-spectrum evolutionary rule of rockburst triggered by roof fall. Int J Rock Mech Min 64:6–16

    Article  Google Scholar 

  • Lu CP, Liu GJ, Liu Y, Zhang N, Xue JH, Zhang L (2015) Microseismic multi-parameter characteristics of rockburst hazard induced by hard roof fall and high stress concentration. Int J Rock Mech Min 76:18–32

    Article  Google Scholar 

  • Madariaga R (1976) Dynamics of an expanding circular fault. Bulletin of the Seismological Society of America 66(3):639–666

    Google Scholar 

  • Ohnaka M (1976) A physical basis for earthquakes based on the elastic rebound model. B Seismol Soc Am 66(2):433–451

    Google Scholar 

  • Peng J, Wong LNY, Liu G, Teh CI (2019) Influence of initial micro-crack damage on strength and micro-cracking behavior of an intrusive crystalline rock. B Eng Geol Environ 78:2957–2971

  • Santis FD, Contrucci I, Kinscher J, Bernard P, Renaud V, Gunzburger Y (2019) Impact of geological heterogeneities on induced-seismicity in a deep sublevel stoping mine. Pure Appl Geophys 176:697–717

    Article  Google Scholar 

  • Shang XY, Li XB, Morales-Esteban A, Dong LJ (2018) Enhancing micro-seismic P-phase arrival picking: EMD—cosine function based denoising with application to the AIC picker. J Appl Geophys 150:332–337

    Article  Google Scholar 

  • Shang XY, Li XB, Peng K, Dong LJ, Wang ZW (2015) Feature extraction and classification of mine microseism and blast based on EMD-SVD (in Chinese). Chinese Journal of Geotechnical Engineering 38(10):1849–1858

    Google Scholar 

  • Smith JS (2005) The local mean decomposition and its application to EEG perception data. J R Soc Interface 2(5):443–454

    Article  Google Scholar 

  • Tary JB, Herrera RH, Han J, Han JJ, Baan M (2014) Spectral estimation-what is new? What is next? Rev Geophys 52(4):723–749

    Article  Google Scholar 

  • Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode mecomposition with adaptive noise. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE: 4144–4147

  • Wu ZH, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41

    Article  Google Scholar 

  • Wang Y, He Z, Zi Y (2009) A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis. Meas Sci Technol 20(2):025704

  • Wang Y, He Z, Zi Y (2010) A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. J Vib Acoust 132(2):613–624

    Google Scholar 

  • Wang L, Liu Z, Miao Q, Zhang X (2018a) Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings. Mech Syst Signal Pr 106:24–39

    Article  Google Scholar 

  • Wang Z, Ning J, Ren H (2018b) Frequency characteristics of the released stress wave by propagating cracks in brittle materials. Theor Appl Fract Mec 96:72–82

    Article  Google Scholar 

  • Xiao YX, Feng XT, Hudson JA, Chen BR, Feng GL, Liu JP (2016) ISRM suggested method for in situ microseismic monitoring of the fracturing process in rock masses. Rock Mech Rock Eng 49(1):343–369

    Article  Google Scholar 

  • Xu Y, Dai F (2018) Dynamic response and failure mechanism of brittle rocks under combined compression-shear loading experiments. Rock Mech Rock Eng 51:747–764

    Article  Google Scholar 

  • Yang Y, Cheng J, Zhang K (2012) An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems. Measurement 45(3):561–570

    Article  Google Scholar 

  • Yeh JR, Shieh JS, Huang NE (2010) Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv Adapt Data Anal 2(2):135–156

    Article  Google Scholar 

  • Yu J, Zhang Z (2017) Research on the seismic signal denoising with the LMD and EMD method. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

  • Zhang XP, Wong LNY, Wang SJ (2015) Effects of the ratio of flaw size to specimen size on cracking behavior. B Eng Geol Environ 74(1):181–193

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Prof. Nuwen Xu at Sichuan University for his contributions to offering microseismic monitoring and other field data.

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu, 610065, Sichuan, China

    Ruochen Jiang & Mingdong Wei

Authors
  1. Ruochen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingdong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingdong Wei.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2206 KB)

Appendix

Appendix

In the Supplementary Material 1, a series of numerical experiments are conducted to analyze and compare the performance of the proposed method with that of representative EMD and LMD variants.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, R., Wei, M. An improved method of local mean decomposition with adaptive noise and its application to microseismic signal processing in rock engineering. Bull Eng Geol Environ 80, 6877–6895 (2021). https://doi.org/10.1007/s10064-021-02338-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-021-02338-8

Keywords

Search

Navigation