Fuzzy inference system design for multiple attenuation with quantitative validation criteria using Auto Correlation Energy Ratio (ACER)
Zarei, M., Hashemi, H. and Bagheri, M., 2023. Fuzzy inference system design for multiple attenuation with quantitative validation criteria using Auto Correlation Energy Ratio (ACER). Journal of Seismic Exploration, 32: 205-227. Many methods for multiple attenuation are based on numerous signal properties for years. Each multiple attenuation technique has advantages and disadvantages and is effective for a particular type of multiples. Recently, fuzzy logic has shown wide application in seismic processing and interpretation. A method for multiple attenuation using Radon transform by fuzzy inference system is introduced to run the multiple attenuations step adaptively and automatically. We applied an intelligent adaptive approach based on fuzzy logic to attenuate multiples in each super common midpoint gather automatically and find good results compared to the manual defined mute function in the radon domain. Applying the new method to synthetic and real data has shown the power of the proposed method for multiple attenuations in the area with substantial two-way travel time differences that occurred due to the different water depths. A quantitative validation criterion named Auto Correlation Energy Ratio (ACER) is presented to guarantee that the final result in multiple attenuations using the new proposed approach is correct.
- Aminzadeh, F., 1991. Expert Systems in Exploration. SEG, Tulsa, OK.
- 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.
- Bezdek, J.C., Ehrlich, R. and Full, W., 1984. FCM - The fuzzy C-means clusteringalgorithm.
- Comput. Geosci., 10: 191-203.
- Chongjin, Z., Peng, Y. and Jun. G., 2020. Integrated interpretation of multi-geophysical
- inversion results using guided fuzzy C-means clustering. Internat. J. Earth Sci.
- Geophys., 6: 035.
- Coppens, F., 1991. A rule-based system for the determination of seismic velocities,.
- Expert Syst. Explor.: 33-58.
- Crucelis, L. and Milagrosa, A., 2009. Automatic first break picking in VSP data using
- fuzzy logic systems. 11th Internat. Congr. Brazil. Geophys. Soc., Salvador,
- Bahia, Brazil, 24-28 August.
- Finol, J.D. and Jing. X., 2002. Permeability prediction in shaly formations: The fuzzy
- modeling approach. Geophysics, 67: 817-829.
- Gao, L., Jiang, Z.Y. and Min, F., 2019. First-arrival travel times picking through sliding
- windows and fuzzy C-means. Mathematics, 7: 221.
- Guillaume, S., 2001. Designing fuzzy inference systems from data: an interpretabilityoriented
- review. IEEE Transact. Fuzzy Syst., 9: 426-443.
- Hadiloo, 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. Geopersia, 8: 27-
- Hajian, A., Kimiaeefar, R. and Siahkoohi, H., 2016. Random noise attenuation in
- reflection seismic data using Adaptive Neuro-fuzzy Interference System
- (ANFIS). Extended Abstr., 78th EAGE Conf., Vienna.
- Hampson, D., 1986. Inverse velocity stacking for multiple elimination. Canad. J. Explor.
- Geophys., 22: 44-55.
- Hashemi, H., 2018. Seismic pattern recognition: automation of processing and
- interpretation. Proc. 18th Iran. Geophys. Conf., Tehran: 1226-1228.
- Hashemi, H., Javaherian, A. and Babuska, R, 2008. A semi-supervised method to detect
- seismic random noise with fuzzy GK clustering. J. Geophys. Engineer., 5:
- Janakiraman, K.K. and Konno, M., 2002. Cross‐borehole geological interpretation model
- based on a fuzzy neural network and geotomography. Geophysics, 67: 1177-
- Kneib, G. and Bardan, V., 1994. Targeted multiple attenuation. Extended Abstr., 56th
- EAGE Conf., Vienna: H034.
- Li, T., Du, Y. and Yuan, Y., 2019. Use of variable fuzzy clustering to quantify the
- vulnerability of a power grid to earthquake damage. Sustainability, 11: 5633.
- Li, Y. and Sun, J., 2016. 3D magnetization inversion using fuzzy C-means clustering
- with application to geology differentiation. Geophysics, 81(5): J61-J78.
- Mayne, W.H., 1962. Common reflection point horizontal data stacking techniques.
- Geophysics, 17: 927-938.
- Peacock, K. and Treitel, S., 1969. Predictive deconvolution - Theory and practice.
- Geophysics, 34: 155-169.
- Schneider, W., Prince, E. and Giles, B., 1965. A new data processing technique for
- multiple attenuation exploiting differential moveout. Geophysics, 30: 348-
- Sheng, C., Zhen, Z. and Jun, G., 2014. A marine case analysis of multiple suppression.
- Internat. Geophys. Conf., Beijing, April: 261-264.
- Singh, A., Sharma, S.P., Akca, I. and Baranwal, V.C., 2018. Fuzzy constrained Lp-norm
- inversion of direct current resistivity data. Geophysics, 83(1): E11-E24.
- Sun, J. and Li, Y., 2015. Multi-domain petrophysically constrained inversion and geology
- differentiation using guided fuzzy C-means clustering. Geophysics, 80(1):
- ID1-ID18.
- Verschuur, D.J., Berkhout, A.J. and Wapenaar, C.P.A., 1992. Adaptive surface-related
- multiple elimination. Geophysics, 57: 1166-1177.
- Xiao, C., Bancroft, J., Brown, R.J. and Cao, Z., 2003. Multiple Suppression: a literature
- review, CREWES Research Rep., 15.
- Yilmaz, Ö., 1989. Velocity-stack processing. Geophys. Prosp., 37: 357-382.
- Yuza, N.H., Nainggolan, T.B. and Manik, H., 2020. Multiple attenuation methods in
- short-offset 2D marine seismic data: a case study in Cendrawasih Bay. IOP
- Conf. Series Earth Environment. Sci.: 429.
- Zadeh, L., 1965. Fuzzy sets. Informat. Control, 8: 338-353.
- Zarei, M. and Hashemi, H., 2019. Edge detector Radon transform for seismic multiple
- attenuation - 2nd Conf. Arab. J. Geosci., Tunisia, November.
- Zarei, M. and Hashemi, H., 2021. Primary-multiple separation technique based on image
- Radon transform. Arab. J. Geosci., 14: 462.
