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Microseismic event denoising via adaptive directional vector median filters

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We present a novel denoising scheme via Radon transform-based adaptive vector directional median filters named adaptive directional vector median filter (AD-VMF) to suppress noise for microseismic downhole dataset. AD-VMF contains three major steps for microseismic downhole data processing: (i) applying Radon transform on the microseismic data to obtain the parameters of the waves, (ii) performing S-transform to determine the parameters for filters, and (iii) applying the parameters for vector median filter (VMF) to denoise the data. The steps (i) and (ii) can realize the automatic direction detection. The proposed algorithm is tested with synthetic and field datasets that were recorded with a vertical array of receivers. The P-wave and S-wave direct arrivals are properly denoised for poor signal-to-noise ratio (SNR) records. In the simulation case, we also evaluate the performance with mean square error (MSE) in terms of signal-to-noise ratio (SNR). The result shows that the distortion of the proposed method is very low; the SNR is even less than dB.
Czasopismo
Rocznik
Strony
47--54
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
autor
  • State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Beijing, China
autor
  • College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, China
autor
  • College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing, China
autor
  • Anhui University of Science and Technology, Huainan, China
Bibliografia
  • 1. Astola J, Haavisto P, Neuvo Y (1990) Vector median filter. Proc IEEE 78:678–689. doi:10.1109/5.54807
  • 2. Deans SR (1984) Book-review—the radon transform and some of its applications. Science 223(1):3–4
  • 3. Forghani-Arani F, Willis M, Haines S, Batzle M, Behura J, Davidson M (2013) An effective noise-suppression technique for surface microseismic data. Geophysics 78(6):KS85–KS95. doi:10.1190/GEO2012-0502.1
  • 4. Han J, van der Baan M (2015) Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding. Geophysics 80(6):69–80. doi:10.1190/geo2014-0423.1
  • 5. Jones GA, Kendall J-M, Bastow I, Raymer DG, Wuestefeld A (2014) Characterization of fractures and faults: a multi-component passive microseismic study from the Ekofisk reservoir. Geophys Prospect 62:779–796. doi:10.1111/1365-2478.12139
  • 6. Jushnir A, Varypaev A, Dricker I, Rozhkov M, Rozhkov N (2014) Passive surface microseismic monitoring as a statistical problem: location of weak microseismic signals in the presence of strongly correlated noise. Geophys Prospect 62:819–833. doi:10.1111/1365-2478.12124
  • 7. Kwiatek G, Bohnhoff M, Dresen G, Schulze A, Schulte T, Zimmermann G, Huenges E (2010) Microseismicity induced during fluid-injection: a case study from the geothermal site at Groß Schönebeck, north German Basin. Acta Geophys 58(6):995–1020. doi:10.2478/s11600-010-0032-7
  • 8. Kwietniak A (2015) Detection of the long period long duration (LPLD) events in time- and frequency-domain. Acta Geophys 63(1):201–213. doi:10.2478/s11600-014-0262-1
  • 9. Liu Y, Luo Y, and Wang Y (2009) Vector median filter and its applications in geophysics. In: 79th Annual International SEG Meeting, Expanded Abstracts, pp 3342–3347, doi: 10.1190/1.3255554
  • 10. Liu Y (2013) Noise reduction by vector median filtering. Geophysics 78(3):V79–V86. doi:10.1190/geo2012-0232.1
  • 11. Sabbione JI, Sacchi MD, Velis DR (2015) Radon transform-based microseismic event detection and signal-to-noise ratio enhancement. J Appl Geophys 113:51–63. Doi 10.1016/j.jappgeo.2014.12.008
  • 12. Stockwell RG (2007) A basis for efficient representation of the S-transform. Digit Signal Process 17:371–393. doi:10.1016/j.dsp.2006.04.006
  • 13. Usher PJ, Angus DA, Verdon JP (2013) Influence of a velocity model and source frequency on microseismic waveforms: some implications for microseismic locations. Geophys Prospect 61(s1):334–345. doi:10.1111/j.1365-2478.2012.01120.x
  • 14. Velis D, Sabbione JI, Sacchi MD (2015) Fast and automatic microseismic phase-arrival detection and denoising by pattern recognition and reduced-rank filtering. Geophysics 80(6):25–38. doi:10.1190/geo2014-0561.1
  • 15. Vera Rodriguez I, Bonar D, Sacchi M (2012) Microseismic data denoising using a 3C group sparsity constrained time-frequency transform. Geophysics 77(2):V21–V29. doi:10.1190/geo2011-0260.1
  • 16. Zheng J, Peng SP, Liu M, Liang Z (2013) A novel seismic wavelet estimation method. J Appl Geophys 90:92–95. doi:10.1016/j.jappgeo.2013.01.007
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cad0d9c9-346a-482f-85aa-5af847059646
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