An unsupervised machine learning algorithm approach using K-Means Clustering for optimizing Surface Wave Filtering in seismic reflection data
DOI:
https://doi.org/10.21580/jnsmr.v10i1.22399Keywords:
Unsupervised machine learning, Seismic data, K-Means clustering, Filter surface wavesAbstract
Surface waves often cause significant noise in seismic data, complicating the interpretation of subsurface structures. Traditional filtering methods, such as FK filtering, usually struggle with non-stationary noise and require extensive manual parameter tuning. This study explores the effectiveness of using K-means clustering, incorporating attributes such as amplitude, frequency, and phase to filter surface waves from seismic data. Synthetic seismic data were first generated to test the proposed method, ensuring its robustness before application to real field data. Attributes were extracted from each seismic trace, including instantaneous amplitude, frequency, and phase. These attributes were used as input parameters for the K-means clustering algorithm. The identified clusters corresponding to surface waves were then used to filter these waves from the seismic data. The K-Means clustering effectively differentiated surface waves from reflected waves in both synthetic and real seismic datasets. The method demonstrated that by including phase as an attribute, alongside amplitude and frequency, the accuracy of surface wave detection and filtering significantly improved. The synthetic data showed a clear separation of wave types, validating the method. When applied to real field data, the approach consistently removed surface waves, clarity of seismic reflections crucial for subsurface analysis.Downloads
References
Aki, K., & Richards, P. G. (2022). Quantitative Seismology. University Science Books.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Chen, J., Gao, H., & Liu, B. (2022). Enhanced FK Filtering Techniques for Seismic Reflection Data. Geophysical Journal International, 229(2), 875–889.
Chen, Q., & Sidney, S. (2018). A Comparative Study of Seismic Noise Attenuation Techniques. Geophysical Prospecting, 66(4), 1007–1020.
Clayton, R. W., & Ammon, C. J. (2003). Fundamentals of Seismology. Cambridge University Press.
Dziewonski, A. M., & Anderson, D. L. (1981). Preliminary reference Earth model (PREM). Physics of the Earth and Planetary Interiors, 25(4), 297–356.
Futterman, W. I. (1962). Dispersive body waves. Journal of Geophysical Research, 67(13), 5279–5291.
Gao, Y., Chen, L., & Zhang, H. (2021). Notch Filtering Advancements in Seismic Data Interpretation. Interpretation, 9(1), 55–64.
Hall, C. M. (2004). The Solid Earth: An Introduction to Global Geophysics. Cambridge University Press.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.
Jones, I. F., Ibbotson, K., Grimshaw, M., & Plasterie, P. (1998). 3-D prestack depth migration and velocity model building. In Acta Geophysica (Issue 7). The Leading Edge.
Kennett, B. L. N. (2001). he Seismic Wavefield: Volume I, Introduction and Theoretical Development. Cambridge University Press.
Khalaf, A., Zeng, X., & Hu, H. (2020). Surface Wave Attenuation in Land Seismic Data: Challenges and Advances. Interpretation, 8(2), 183–194.
Kramer, S. L. (2021). Geotechnical Earthquake Engineering. Pearson.
Lay, T., & Wallace, T. C. (2015). Modern Global Seismology. Academic Press.
Lee, W., Kim, S., & Park, J. (2021). Phase-Based Clustering for Seismic Data Analysis. Journal of Seismology and Earthquake Engineering, 23(3), 145–158.
Li, J., Wong, Y., Zhao, Q., & Kankanhalli, M. S. (2019). Learning to Learn from Noisy Labeled Data. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5046–5054. https://github.com/LiJunnan1992/MLNT
Liu, P., Liu, S., & Zhao, J. (2022). Curvelet-Based Seismic Data Denoising Methods. Geophysics, 87(2), 123–135.
Liu, S., Zhang, J., & Huang, Y. (2019). ). Surface wave noise attenuation using convolutional neural networks. Journal of Applied Geophysics, 168, 104–116.
Liu, Y., & Fomel, S. (2018). Seismic Data Interpolation Using Weighted Similarity Adaptive Filtering. Geophysics, 83(3), 183–192.
Li, Y., & Feng, H. (2019). Advanced phase-matching filtering techniques for seismic waves. Geophysical journal international. Geophysical Journal International, 217(1), 145–156.
Li, Z., & Zhou, S. (2017). Advanced FK filtering methods in seismic data processing. Geophysics, 82(4).
Margrave, G. F. (2002). Methods of Seismic Data Processing. University of Calgary.
Moro, G. D. (2014). Surface Wave Analysis for Near Surface Applications. Elsevier.
Nanda, N. C. (2016). Seismic Data Interpretation and Evaluation for Hydrocarbon Exploration and Production. Springer.
Shearer, P. M. (2019). Introduction to Seismology (3rd ed.). Cambridge University Press.
Simm, R., & Bacon, M. (2022). Seismic Amplitude: An Interpreter’s Handbook. Cambridge University Press.
Smith, J. R., & Johnson, L. (2020). Advancements in Seismic Data Filtering Techniques. Journal of Applied Geophysics, 175.
Socco, L. V., Strobbia, C., & Boiero, D. (2010). Surface Wave Analysis for Near Surface Applications. SEG Books.
Stein, S., & Wysession, M. E. (2020). An Introduction to Seismology, Earthquakes, and Earth Structure. Wiley-Blackwell.
Sun, Z., Wang, R., & Xie, Y. (2020). Wavelet-Based Denoising Techniques in Seismic Data Processing. Journal of Applied Geophysics, 185.
Taner, M. T., Koehler, F., & Sheriff, R. E. (1979). Complex seismic trace analysis. Geophysics, 44(6), 1041–1063.
Tsvankin, I. (2012). Seismic Signatures and Analysis of Reflection Data in Anisotropic Media (3rd ed.). Society of Exploration Geophysicists.
Udías, A. (1999). Principles of Seismology. Cambridge University Press.
Wang, X., Gao, Y., Chen, C., Yuan, H., & Yuan, S. (2022). Intelligent velocity picking and uncertainty analysis based on the Gaussian mixture model. Acta Geophysica, 70, 2659–2673.
Wang, Y., Li, X., & Zhao, D. (2020). FK filtering in seismic reflection data. Journal of Applied Geophysics, 180.
Wang, Y., Li, X., & Zhao, D. (2021). Suppressing Surface Waves in Seismic Data Using Adaptive Filtering Techniques. Journal of Applied Geophysics.
Wiens, D. A. (2003). Seismology: Earth Structure and the Physical Properties of the Earth. Academic Press.
Wu, J., Liu, Y., & Chen, X. (2020). A machine learning approach to separate surface waves from body waves in seismic data. Geophysics, 85(5).
Wu, X., Liu, Y., & Zhang, W. (2018). Phase-Matching Filtering in Complex Seismic Data Analysis. Journal of Seismology, 22(2), 331–345.
Xia, J. (2018). Advances in Near-surface Seismology and Ground-penetrating Radar (Vol. 15). SEG Books.
Xia, K., Hilterman, F., & Hu, H. (2018). Unsupervised machine learning algorithm for detecting and outlining surface waves on seismic shot gathers. Journal of Applied Geophysics, 157, 73–86.
Xie, J., Liu, Q., & Zheng, Y. (2021). Wavelet-Based Approaches for Surface Wave Noise Attenuation. Geophysical Journal International, 225(3).
Yang, Q., & Wu, X. (2018). Targeted Notch Filtering for Seismic Wave Analysis. Geophysics, 83(6), 377–388.
Yang, X., Li, W., & Wang, Q. (2022). Phase-matching filtering approaches for enhancing seismic data quality. Interpretation, 10(2), 177–188.
Zhang, J., Li, W., & Yang, L. (2019). Notch Filtering Approaches for Surface Wave Suppression in Seismic Data. Journal of Seismology, 23(3), 645–656.
Zhang, J., Zhang, R., & Liu, B. (2021). Deep learning-based surface wave suppression in seismic data. Geophysical Journal International, 225(3).
Downloads
Published
Issue
Section
License
Copyright
The copyright of the received article shall be assigned to the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to published articles. Authors are allowed to use their articles for any legal purposes deemed necessary without written permission from the journal, but with an acknowledgment to this journal of initial publication.
Licensing
