Reduksi bising seismik secara adaptif menggunakan filter Wiener

Adaptive seismic noise reduction using Wiener filter

*Sesar Prabu Dwi Sriyanto  -  Manado Geophysics Station, Indonesia
Received: 4 Aug 2019; Revised: 10 Oct 2019; Accepted: 17 Oct 2019; Published: 31 Jan 2020; Available online: 5 Nov 2019.
Fulltext |
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Seismic noise disrupts the earthquake observation system due to the frequency and amplitude of seismic noise similar to the earthquake signal. The filter process is one of the methods that can be used to reduce seismic noise. In this study, the Wiener filter algorithm was designed with the Decision-Directed method for Apriori SNR estimation. This filter was chosen because it is adaptive, so it can adjust to environmental conditions without requiring manual parameter settings. The data used are earthquake signals that occur in the Palu area, Central Sulawesi, which are recorded on PKA29 temporary seismic station from February 3 to April 28, 2015. After each signal data has been filtered, then it is evaluated by calculating SNR differences before and after filtering, the signal's dominant frequency, and the cross-correlation of the signal before and after filtering. As a result, the Wiener filter is able to reduce the noise content in earthquake signals according to noisy frequencies before earthquake signals. The impact is that SNR has increased with an average of 8.056 dB. In addition, this filter is also able to maintain the shape of earthquake signals. This is indicated by the normalization value of the cross-correlation between signals before and after the filter which ranges from 0.703 to 1.00.
Keywords: seismic noise; earthquake signal; Wiener filter; decision-directed
Funding: Manado Geophysics Station, Indonesia

Article Metrics:

  1. K. Munro, “Automatic event detection and picking of P-wave arrivals,” CREWES Research Report, vol. 16, 2004
  2. L. Küperkoch, T. Meier, and T. Diehl, “Automated event and phase identification,” in IASPEI New Manual of Seismological Observatory Practice (NMSOP). Postdam: GeoForschungsZentrum, 2011, p.1-5, 23-27
  3. L. Kuperkoch, “Automated recognition, phase arrival time estimation, and location of local and regional earthquakes,” Dissertation, University of Bochum, Bochum, Germany, 2010
  4. -, “Scautopick.” SeiscomP3. https://www.seisco (accessed 23 April 2019)
  5. J. Havskov and L. Ottemöller, Routine data processing in earthquake seismology, New York: Springer, 2010
  6. T. V. Eck and L. Ahlbom, “Automatic event detection applied to single channel seismic records,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, Paris, France, May 1982, pp. 1894-1897. doi: 10.1109/ICASSP.1982.1171824
  7. J. F. Claerbout, Fundamentals of geophysical data processing. New York: McGraw-Hill, 1976
  8. S. M. Mousavi and C. A. Langston, “Adaptive noise estimation and suppression for improving microseismic event detection,” Journal of Applied Geophysics, vol. 132, pp. 116-124, 2016. doi: 10.1016/j.jappgeo.2016.06.008
  9. N. Wiener, Extrapolation, interpolation, and smoothing of stationary time series. Massachusetts: The MIT Press, 1964
  10. J. Liu, D. Ying, and P. Zhou, “Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients,” Medical Engineering & Physics, vol. 36, no. 12, pp. 1711-1715, 2014. doi: 10.1016/j.medengphy.2014.09.008
  11. J. F. Claerbout, “Detection of P-waves from weak sources at great distances,” Geophysics, vol. 29, no. 2, pp.197-211, 1964. doi: 10.1190/1.1439350
  12. A. Douglas, “Bandpass filtering to reduce noise on seismograms : Is there a better way?,“ Bulletin of the Seismological Society of America, vol. 87, no. 4, pp. 770-777, 1997
  13. J. Wang, J. Schweitzer, F. Tilmann, R. S. White, H. Soosalu, “Application of the multichannel Wiener filter to regional event detection using NORSAR seismic-array data,”, Bulletin of the Seismological Society of America, vol. 101, no. 6, pp. 2887-2896, 2011. doi: 10.1785/0120110003
  14. S. M. Mousavi and C. A. Langston, “Hybrid seismic denoising using higher-order statistics and improved wavelet block thresholding,” Bulletin of the Seismological Society of America, vol. 106, no. 4, pp. 1380-1393, 2016. doi: 10.1785/0120150345
  15. Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator,” IEEE Transaction on Acoustics, Speech, and Signal Processing, vol. 32, no. 6, pp. 1109-1121, 1984. doi: 10.1109/TASSP.1984.1164453
  16. P. Scalart and J. Vieira, “Speech enhancement based on a priori signal to noise estimation,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, Atlanta, USA, May 1996, pp. 629-632. doi: 10.1109/ICASSP.1996.543199
  17. K. U. Afegbua and F. O. Ezomo, “A preliminary investigation of the signal-to-noise ratio of Toro and Nsukka stations in Nigeria,” International Journal of Physical Science, vol. 8, no. 16, pp. 707-716, 2013. doi: 10.5897/IJPS2013.3827
  18. E. O. Brigham, The fast fourier transform FFT and its applications. New Jersey: Prentice-Hall Inc., 1988
  19. D. E. McNamara and R. P. Buland, “Ambient noise level in the continental United States,” Bulletin of the Seismological Society of America, vol. 94, no. 4, pp. 1517-1527, 2004. doi: 10.1785/012003001
  20. W. Wang, S. Ni, Y. Chen, and H. Kanamori, “Magnitude estimation for early warning applications using the initial part of P waves: A case study on the 2008 Wenchuan sequence,” Geophysical Research Letters, vol. 36, no. 16, pp. 1-6, 2009. doi: 10.1029/2009GL038678

Last update: 2021-03-05 17:15:27

No citation recorded.

Last update: 2021-03-05 17:15:28

No citation recorded.