Engineering Geology

Engineering Geology

Application of 2D multistage median filter for reducing random seismic noise

Authors
Ehsan Pegah
Behrang Feiz Aghaei
Kharazmi University
10.22034/JEG.2024.18.3.1019163
Abstract
Random noise reduction has always been one of the most important issues in seismic data processing. This study investigates one of the most effective random noise reduction methods, the 2D multi-stage median filter. This filter is applied to seismic data by applying a series of 1D median filters in different directions and then selecting the output value corresponding to the center of the 2D window. By applying a 2D multi-stage median filter to both synthetic and real data, it is shown that the filter can effectively attenuate random spike-like noise in both pre-stack and post-stack data. Similarly, based on spectral analysis, it is shown that this filter does a good job of reducing the level of high frequency random noise in both synthetic and real data. In this study, a 2D median filter is applied to synthetic data containing random noise with a density of 10%. Since increasing the filter length can damage useful signals in addition to attenuating random noise, it is important to specify an appropriate filter length. For synthetic data, the error ratio plot shows that a filter length of 9 points is appropriate for the first stage. In the second stage, a 2D median filter with a length of 7 points was applied to the output of the first stage filter. The effect of this filter on random noise suppression can then be observed by spectral analysis. In addition, median filters of 7 points and 5 points were applied to the pre-stack and post-stack real data, respectively. The effect and efficiency of this filter is assessed by examining the resulting difference plots, sections and spectral analysis.
Keywords
Random noise
Seismic data
Attenuation of random spike-like noise
2-D multistage median filter
Al-Yahya, K. M. (1991). Application of the partial Karhunen-Loeve transform to suppress random noise in seismic sections. Geophysical Prospecting, 39, 77-93.
Fomel, S. (2006). Towards the seislet transform. 76th Annual International Meeting, SEG, Expanded Abstracts, 2847-2851.
Fomel, S., & Liu, Y. (2008). Seismic data analysis with one dimensional seislet frame: 78th Annual International Meeting, SEG, Expanded Abstracts, 2581-2585.
Gulunay, N. (2000). Noncausal spatial prediction filtering for random noise reduction on 3-D poststack data. Geophysics, 66, 342-349.
Hemon, C.H., & Mace, D. (1978). Use of the Karhunen-Loeve transformation in seismic data processing. Geophysical Prospecting, 26, 600-626.
Jones, I. F., & Levy, S. (1987). Signal-to-noise ratio enhancement in multichannel seismic data via the Karhunen-Loeve transform. Geophysical Prospecting, 35, 12-32.
Karsli, H., Dondurur, D., & Cifci, G. (2006). Application of complex trace analysis to seismic data for random-noise suppression and temporal resolution improvement. Geophysics, 71, V79-V86.
Li, W., Wang, C., & Ma, Z. (2000). Random noise attenuation using prestack dominant-component reconstruction. Oil Geophysical Prospecting, 35, 244–256.
Liu, C., Liu, Y., Yang, B., Wang, D., & Sun, J. (2006). A 2D multistage median filter to reduce random seismic noise. Geophysics, 71, V105-V110.
Neelamani, R., Baumstein, A, I., & Gillard, D, G. (2008). Coherent and random noise attenuation using the curvelet transform. The Leading Edge, 27, 240-248.
Pegah, E., Gu, X. Q., & Liu, H. (2024). Fabric anisotropy of granular soils and its dependency on grading and particles specifications. Acta Geotechnica. https://doi.org/10.1007/s11440-024-02357-1
Pegah, E., & Liu, H. (2020). Evaluating the overconsolidation ratios and peak friction angles of granular soil deposits using noninvasive seismic surveying. Acta Geotechnica, 15, 3193-3209.
Pegah, E., Liu, H., & Gholami, A. (2021). Estimating drained cross-anisotropic elastic parameters in saturated clays using the undrained properties. Engineering Geology, 293, 106340.
Pegah, E., Liu, H., Gu, X. Q., & Gholami, A. (2022). A semi-analytical approach for efficient calculation of drained cross-anisotropic elastic moduli in saturated granular soils from undrained attributes. Computers and Geotechnics, 148, 104794.
Pegah, E., Mahmoodi, M., & Siavashpoo, A. (2016). Evaluation of seismic hazard and site geodynamic properties by using geophysical methods in the north of Iran. Journal of Civil Engineering and Construction, 5 (2), 126-142.
Sheriff, R. E., & Geldart, L. P. (1995). Exploration Seismology, 2nd Edition. Cambridge University Press, New York.
Talaee Firozjaee, F., Pegah, E., & Misaghi, A. (2024). Fabric anisotropy in sands and assessing its dependence on grading and surface appearance parameters. Kharazmi Journal of Earth Sciences, 10 (1), 73-90.
Ulrych, T. J., Sacchi, M. D., & Freire, S. L. M. (1999). Eigenimage processing of seismic sections in Covariance Analysis of Seismic Signal Processing. Geophysical Developments Series, Society of Exploration Geophysicists, 241-274.
VISTA 2D/3D Seismic Data Processing software, version 5.1., GEDCO, Calgary, Canada.
Wang, W. (2000). Coherent signal prediction using mid-value correlative filtering. Oil Geophysical Prospecting, 35, 273–282.
Wang, X. (1992). Adaptive multistage median filter. IEEE Transactions on Signal Processing, 40, 1015-1017.
Zhang, R., & Ulrych, T. J. (2003). Multiple suppression based on the migration operator and a hyperbolic median filter. 73rd Annual International Meeting, SEG, Expanded Abstracts, 1949–1952.