Fuzzy Partitioning of Radon Domain for Estimation of Water Reverberation Energy

Document Type : Research Article

Authors

1 Ph.D. Student, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran

2 Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran

Abstract

The radon transform has a wide application in seismic processing for each project in different areas. Multiple attenuation is mostly summarized in the use of radon analysis in practice, especially in marine data processing. The definition of mute function is the major challenge in parabolic radon transform. In this paper, a method for segmentation of the radon transform by fuzzy inference system is introduced to separate energy parts in the radon domain. We applied a fuzzy inference system based on the property of energy distribution and its attribute in the radon domain. The result of clustering is the partitioning of the radon domain in three major classes: 1- random noise, 2- multiple, and 3- primary and multiple. The result of applying the new method on real data has shown the applicability of the new method for separation of multiple class from other classes that can assist the processor to define the mute function in the absence of other events in the radon domain.

Keywords

Main Subjects

References

Abedi, M.M., Riahi, M.A. and Stovas, A. 2018, Three-parameter RadonTransform based on VTI generalized moveout approximation. 80th EAGE meeting, Copenhagen, Denmark, Expanded Abstracts, WeB 09.
Abedi, M.M., Riahi, M.A. and Stovas, A. 2019, Three-parameter Radon transform in layered transversely isotropic media, Geophysical Prospecting, 67, 395–407.
Akerberg, P., Hampson, G., Rickett, J., Martin, H. and Cole, J., 2008, Simultaneous source separation by sparse Radon transform, SEG Technical Program Expanded Abstracts 2008.
Aminzadeh, F. and Wilkinson, D., 2004, Soft computing for qualitative and quantitative seismic object and reservoir property prediction Part 2: Fuzzy Logic Applications. First Break. 22, 69-78.
Chongjin, Z., Peng, Y. and Jun, G., 2020, Integrated Interpretation of Multi-Geophysical Inversion Results using Guided Fuzzy C-Means Clustering. International Journal of Earth Science and Geophysics 6:035.
Dunn, J. C., 1973, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics 3, 32-57.
Gholami, A. and Zand, T., 2018, Three-parameter Radon transform based on shifted hyperbolas. Geophysics 83, 1–37.
Hadiloo, S., Mirzaei, S., Hashemi, H. and Beiranvand, B., 2018, Comparison between unsupervised and supervised fuzzy clustering method in interactive mode to obtain the best result for extract subtle patterns from seismic facies maps
Hampson D., 1986, Inverse velocity stacking for multiple elimination. Can J Explor Geophys 22, 44–55.
Hashemi, H., Javaherian, A. and Babuska, R., 2008, A Semi-Supervised Method To Detect Seismic Random Noise With Fuzzy GK Clustering. Journal of Geophysics and Engineering. 5, 457.
Hokstad, K. and Sollie, R., 2006, 3D surface-related multiple elimination using parabolic sparse inversion. Geophysics 71, V145–V152.
Ibrahim, A. and Sacchi, M., 2014, Simultaneous source separation using a robust Radon transform: Geophysics 79, V1–V11.
Jang, J.S.R., Sun, C.T. and Mizutani, E., 1997, Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Inc.
Mamdani, E. H. and Assilian, S., 1975, An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, 7(1), 1-13.
Sacchi, M. and Ulrych, T., 1995, High-resolution velocity gathers and offset space recon-struction: Geophysics, 60, 1169–1177.
Seher T., 2017, A high-resolution apex-shifted hyperbolic Radon transform and its application to multiple attenuation. SEG Technical Program Expanded Abstracts 2017, 4742-4746.
Sugeno, M. and Kang, G.T., 1988, Structure identification of fuzzy model,Fuzzy Sets and Systems, 28(1), 15-33.
Takagi, T. and Sugeno, M., 1985, Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. Systems, Man, and Cybernet. 15, 116-132.
Trad, D., 2003, apex shifted radon transform,CSEG.
Trad, D., Siliqi, R., Poole, G. and Boelle, J. 2012, Fast and robust deblending using apex shifted Radon transform. 82nd Annual international meeting, SEG, Expanded Abstracts.
Wang, H., 2016, Automatic stacking velocity estimation using sparse pseudoorthogonal radon transform. Paper presented at the 2016 SEG International Exposition and Annual Meeting, Dallas, Texas, October 2016.
Zadeh, L., 1965, Fuzzy sets V Inf. Control 8 338–53.
Zarei, M. and Hashemi, H., 2019, Edge Detector Radon Transform for Seismic Multiple Attenuation, 2nd conference of the Arabian journal of geoscience (CAJG), 25-28 Nov 2019, Sousse, Tunisia.
Zarei, M. and Hashemi, H., 2021, Primary-multiple separation technique based on imageradon transform, Arabian Journal of Geosciences, 14, 462.
Zhang, Q., Wang, H., Chen, W. and Huang, G, 2021, A local radon transform for seismic random noise attenuation, Journal of Applied Geophysics, Volume 186, 104264.
Journal of the Earth and Space Physics
Volume 47, Issue 4
online publication date: 5 February 2022
February 2022
Pages 125-132

Files

Share

How to cite

Statistics