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Microseismic signal denoising using simple bandpass filtering based on normal time–frequency transform

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In active underground mining environments, monitoring mine vibrations has important implications for both safety and productivity. Microseismic data processing is crucial for subsurface real-time monitoring during mineral mining processes. Microseismic events are difficult to detect due to their small magnitudes and low signal-to-noise ratios (SNRs). Useful microseismic signals are usually obscured by long-period microseisms, random noise and artificial strong noise. We propose a useful microseismic denoising algorithm based on the normal time–frequency transform (NTFT) to determine the instantaneous frequency, amplitude and phase information from useful microseismic signals. The energy difference in the time–frequency domain between useful microseismic signals and strong noise is small. Therefore, based on the different phase characteristics of microseismic signals and noise in the NTFT phase spectrum, noise can be filtered out by reconstructing the microseismic signals in useful real-time frequency bands. The proposed simple bandpass filtering (SBPF) method is advantageous because the denoising result does not produce phase shifts, energy leakage or artefacts. The only parameter of the proposed method that needs to be defined is the instantaneous cutoff frequency; thus, the denoising operation is simple. We use both synthetic and real data to demonstrate the feasibility of the method for denoising complicated microseismic datasets.
Czasopismo
Rocznik
Strony
2217--2232
Opis fizyczny
Bibliogr. 46 poz., rys., tab.
Twórcy
autor
  • State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
  • College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  • State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
autor
  • State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
Bibliografia
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  • 3. Battista B, Knapp C, McGee T, Goebel V (2007) Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data. Geophysics 72(2):H29–H37
  • 4. Cai S, Liu L, Wang G (2018) Short-term tidal level prediction using normal time-frequency transform. Ocean Eng 156(15):489–499
  • 5. Chai H, Huang H, Yan Z, Zhang X, Li Y, Gan P, Huang Y (2018) Multi-threshold wavelet packet-based method to attenuate noise from seismic signal. In: 7th international conference on informatics, environment, energy and applications, pp 212–216
  • 6. Chen Y (2016) Dip-separated structural filtering using seislet thresholding and adaptive empirical mode decomposition based dip filter. Geophys J Int 206(1):457–469
  • 7. Chen Y (2018) Non-stationary least-squares complex decomposition for microseismic noise attenuation. Geophys J Int 213(3):1572–1585
  • 8. Chen Y (2020) Automatic microseismic event picking via unsupervised machine learning. Geophys J Int 222(3):1750–1764
  • 9. Cheng S, Li S, Li L, Shi S, Zhou Z, Wang J (2018) Study on energy band characteristic of microseismic signals in water inrush channel. J Geophys Eng 15(5):1826–1834
  • 10. Cheng W, Liu L, Wang G (2021) A new method for estimating the correlation of seismic waveforms based on the NTFT. Geophys J Int 226(1):368–376
  • 11. Daubechies I, Lu J, Wu H (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30:243–261
  • 12. Gong J, Li Y, Wu N, Li M (2019) Automatic time picking of microseismic data based on shearlet-AIC algorithm. J Seismol 23(2):261–269
  • 13. Gu S, Zhang W, Jiang B, Hu C (2019) Case of rock burst danger and its prediction and prevention in tunneling and mining period at an irregular coal face. Geotech Geol Eng 37(4):2545–2564
  • 14. He H, Chen Y, Lan B (2021) Damage assessment for structure subjected to earthquake using wavelet packet decomposition and time-varying frequency. Structures 34:449–461
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  • 16. Huang W, Wang R, Zhang M, Chen Y, Yu J (2015) Random noise attenuation for 3D seismic data by modified multichannel singular spectrum analysis. In: 77th EAGE conference and exhibition 2015, European Association of Geoscientists & Engineers, (1): 1–5
  • 17. Huang W, Wang R, Yuan Y, Zhou Y, Chen Y (2016) Randomized-order multichannel singular spectrum analysis for simultaneously attenuating random and coherent noise. In: 86th annual international meeting SEG, pp 4777–4781
  • 18. Huang W, Wang R, Chen X, Zhou Y, Chen Y, You J (2017) Low-frequency noise attenuation of seismic data using mathematical morphological filtering. In: SEG technical program expanded abstracts, pp 5011–5016
  • 19. Iqbal N (2022) DeepSeg: deep segmental denoising neural network for seismic data. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2022.3205421
  • 20. Iqbal N, Zerguine A, Kaka S, Al-Shuhail A (2016) Automated SVD filtering of time-frequency distribution for enhancing the SNR of microseismic/microquake events. J Geophys Eng 13(6):964–973
  • 21. Iqbal N, Al-Shuhail A, Kaka S, Liu E, Raj A, McClellan J (2017) Iterative interferometry-based method for picking microseismic events. J Appl Geophys 140:52–61
  • 22. Iqbal N, Liu E, McClellan J, Al-Shuhail A, Kaka S, Zerguine A (2018) Detection and denoising of microseismic events using time–frequency representation and tensor decomposition. IEEE Access 6:22993–23006
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  • 24. Li P, Feng X, Feng G, Xiao Y, Chen B (2019a) Rockburst and microseismic characteristics around lithological interfaces under different excavation directions in deep tunnels. Eng Geol 260:105209
  • 25. Li S, Cheng S, Li L, Shi S, Zhang M (2019b) Identification and location method of microseismic event based on improved STA/LTA algorithm and Four-Cell-Square-Array in plane algorithm. Int J Geomech 19(7):04019067
  • 26. Li L, Tan J, Schwarz B, Staněk F, Poiata N, Shi P, Diekmann L, Eisner L, Gajewski D (2020) Recent advances and challenges of waveform-based seismic location methods at multiple scales. Rev Geophys 58:1–47
  • 27. Liu R (2021) Research on feature fusion method of mine microseismic signal based on unsupervised learning. Shock Vib 2021:1–12
  • 28. Liu L, Hsu H (2012) Inversion and normalization of time-frequency transform. Appl Math Inf Sci 6(1S):67–74
  • 29. Liu L, Hsu H, Grafarend E (2007) Normal Morlet wavelet transform and its application to the Earth’s polar motion. J Geophys Res. https://doi.org/10.1029/2006JB004895
  • 30. Liu W, Cao S, Chen Y (2016) Applications of variational mode decomposition in seismic time-frequency analysis. Geophysics 81(5):V365–V378
  • 31. Liu E, Zhu L, Raj A, McClellan J, Al-Shuhail A, Kaka S, Iqbal N (2017) Microseismic events enhancement and detection in sensor arrays using autocorrelation-based filtering. Geophys Prospect 65(6):1496–1509
  • 32. Liu N, Yang Y, Li Z, Gao J, Pan S (2020) Seismic signal de-noising using time–frequency peak filtering based on empirical wavelet transform. Acta Geophys 68(2):1–10
  • 33. Long Y, Lin J, Li B, Wang H, Chen Z (2019) Fast-AIC method for automatic first arrivals picking of microseismic event with multitrace energy stacking envelope summation. IEEE Geosci Remote Sens Lett 17(10):1832–1836
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  • 37. Naghadeh D, Morley C (2016) Wavelet extraction and local seismic phase correction using normalized first-order statistics. J Seism Explor 25(2):163–176
  • 38. Othman A, Iqbal N, Hanafy S, Waheed U (2021) Automated event detection and denoising method for passive seismic data using residual deep convolutional neural networks. IEEE Trans Geosci Remote Sens 60:1–11
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  • 40. Song F, Kuleli HS, Toksöz MN, Ay E, Zhang H (2010) An improved method for hydrofracture-induced microseismic event detection and phase picking. Geophysics 75(6):A47–A52
  • 41. Vaezi Y, Baan M (2015) Comparison of the STA/LTA and power spectral density methods for microseismic event detection. Geophys J Int 203(3):1896–1908
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  • 43. Yang H, Zhou P, Fang N, Zhu G, Xu W, Su J, Meng F, Chu R (2020) A shallow shock: the 25 February 2019 ML 4.9 earthquake in the Weiyuan shale gas field in Sichuan, China. Seismol Res Lett 91(6):3182–3194
  • 44. Zhang X, Jia R, Lu X, Peng Y, Zhao W (2018) Identification of blasting vibration and coal-rock fracturing microseismic signals. Appl Geophys 15(2):280–289
  • 45. Zhu L, Rivera L (2002) A note on the dynamic and static displacements from a point source in multilayered media. Geophys J Int 148(3):619–627
  • 46. Zuo L, Sun H, Mao C, Liu Y, Jia R (2019) Noise suppression method of microseismic signal based on complementary ensemble empirical mode decomposition and wavelet packet threshold. IEEE Access 7:176504–176513
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-106f7c21-26b4-40f2-92e0-24a09bfddb36
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