Processing ambient noise using wavelet analysis tools: the Iraqi Seismological broadband Network Data at Iraqi Meteorological Organization and seismology (IMOS)
Keywords:Ambient noise, continuous wavelet transform, denoising, IMOS, wavelet transform
The main objective of this research is to reduce the expected error between the original signal polluted by the implicit and estimated noise. The dominance of some levels of the surrounding earthquakes in general on the seismic energy resulting from human activities (i.e. “cultural") which some generators, cars and machines emit at rather close distances, directly affected the records recently recorded in the Iraqi Seismic Network at the Iraqi Meteorological Organization and seismology. Various seismic signal-processing techniques have been applied to obtain high-quality records. Wavelet analysis techniques have proven effective in increasing the accuracy of seismic records and greatly improving the signal-to-noise ratio. More than one method was applied to the local seismic group recorded by the Iraqi Seismic Network with different characteristics, and although there were minimal changes in the waveform of interest, encouraging results were obtained. The outcomes seem that ways compared with other denoising methods, using the Continuous Wavelet Transform has many natural translation-invariant and time-frequency properties and improve considerably the results compared to those using traditional filters. In addition, the distortion problem can be reduced using a more appropriate mother wavelet and decomposition levels. Whereas the other methods used in this field (traditional filters), Discrete Wavelet Packet Transform and 1-D Wavelet Analysis Tool can remove noise and improve seismic resolution but are less effective than Continuous Wavelet Transform.
Beenamol, M., Prabavathy, S., and Mohanalin, J. (2012). Wavelet based seismic signal de-noising using Shannon and Tsallis entropy. Computers and Mathematics with Applications, 64(11), 3580-3593.
Bormann, P., Klinge, K., and Wendt, S. (2009). Data analysis and seismogram interpretation. In New manual of seismological observatory practice (NMSOP) (Pp. 1-102). DeutschesGeoForschungsZentrum GFZ.
Bormann, P., and Wielandt, E. (2013). Seismic signals and noise. In New Manual of Seismological Observatory Practice 2 (NMSOP2) (Pp. 1-62). Deutsches GeoForschungsZentrum GFZ.
Douglas, A., (1997). Bandpass filtering to reduce noise on seismograms: Is there a better way?: Bulletin of Seismological Society of America, 87, 770–777.
Graps, A. (1995). An introduction to wavelets. IEEE computational science and engineering, 2(2), pp. 50-61. https://doi.org/10.1109/99.388960.
Havskov, J., and Ottemoller, L. (2010). Routine Data Processing in Earthquake Seismology. University of Bergen. Springer (p. 351).
Jassim, S. Z., & Goff, J. C. (Eds.). (2006). Geology of Iraq. DOLIN, sro, distributed by Geological Society of London (Pp. 341).
Li Ying, Zhang Shuzhen, and Xu Kangsheng (2006). Application of wavelet noise reduction method in seismic signal processing [J]. Journal of northwest seismology, 1(28), pp 2159-162.
Mallat, S. A. (1999). Wavelet Tour of Signal Processing; Elsevier: Amsterdam, the Netherland. Pp. 620.
Meyer, Y. and Ryan, R (1993). Wavelets, Algorithms and Applications, Society for Industrial and Applied Mathematics Philadelphia: Philadelphia, PA, USA. Pp. 142.
Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J. M. (Eds.). (2013). Wavelets and their Applications. John Wiley and Sons. Pp. 330. https://www.wiley.com/en-iq/Wavelets+and+their+Applications-p-9781118613597.
Mousavi, S. M., and Langston, C. A. (2017). Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data. Geophysics, 82(4), V211-V227.
Polikar, R. (1996). The wavelet tutorial, part iii. IOWA State University, USA. http://users.rowan.edu/~polikar/WTpart4.html.
Sifuzzaman, M., Islam, M. R., and Ali, M. Z. (2009). Application of wavelet transform and its advantages compared to Fourier transform. Journal of Physical Sciences, 13, pp121-134. http://inet.vidyasagar.ac.in:8080/jspui/handle/123456789/779.
Sokos, E. N., and J. Zahradník (2008). ISOLA a Fortran code and a Matlab GUI to perform multiple-point source inversion of seismic data. Computers and Geosciences, 34, 967-977. https://doi.org/10.1016/j.cageo.2007.07.005.
Sripath, D. (2003). Efficient Implementations of Discrete Wavelet Transforms using FPGAs. Electronic Theses, Florida State University. Treatises and Dissertations. http://purl.flvc.org/fsu/fd/FSU_migr_etd-1599.
Wang, Y., He, Z., Zi, Y. (2010). Enhancement of signaldenoising and multiple fault signatures detecting inrotating machinery using dual-tree complex wavelettransform. Mechanical Systems and Signal Processing, 24(1), pp119-137.
Yang, Y., Liu, C., and Langston, C. A. (2020). Processing seismic ambient noise data with the continuous wavelet transform to obtain reliable empirical Green's functions. Geophysical Journal International. 222(2), pp1224-1235. https://doi.org/10.1093/gji/ggaa243.
Zheng, Z., Lu Xiushan., and Li Kexing. (2007). Construction of a class of wavelet basis functions and its application in measurement data processing [J]. Surveying and mapping science, 9(11).