Advanced Processing of 2D Marine Reflection Seismic Data Using the Common Reflection Surface (CRS) Stack Method with K-L Filter Application

Pengolahan Lanjut Data Seismik Refleksi 2D Lait Menggunakan Metode Common Reflection Surface (CRS) Stack dengan Penerapan KL-Filter

Authors

  • Emir Dzakwan Kamal Zein Universitas Lampung
  • Syamsurijal Rasimeng Universitas Lampung
  • Egie Wijaksono LEMIGAS

DOI:

https://doi.org/10.20956/geocelebes.v7i2.22588

Keywords:

conventional stack, common reflection surface stack, K-L Filter

Abstract

Data processing using the seismic reflection method is an important stage in the exploration of natural resources and minerals. This research was conducted to determine the effective and efficient stacking and filtering methods in reconstructing the subsurface geological structure of the earth from the results of data processing using ProMAX software. The data processing method used is the conventional stack and the Common Reflection Surface (CRS) stack. Aperture values of 0 ms – 50 m and 3000 ms – 150 m in the CRS stack process produce the most optimum seismic sections. Both methods produce a different quality of seismic cross-section display. The 2D cross-section model from the conventional stack method looks noisier than the results from the CRS stack method. In addition, the reflector pattern on the cross-section of the results of the CRS stack method is clearer and visible with a relatively large amplitude compared to the results of the conventional stack method. To maximize the quality of data display, data enhancement is applied, which is the K-L filter. The eigenimages value of 0.10% on the K-L filter with a horizontal window width of 120 is used to reduce random noise. Thus, an increase in the S/N ratio will be obtained in the seismic data so that the 2D cross-sectional model of the seismic reflection method can approach the original conditions of the subsurface geological structure.

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References

Chen, Y., Zhang, M., Bai, M., & Chen, W. (2019). Improving the signal‐to‐noise ratio of seismological datasets by unsupervised machine learning. Seismological Research Letters, 90(4), 1552-1564. https://doi.org/10.1785/0220190028

Dani, I., & Sule, M. R. (2021). Pemodelan Seismik pada Struktur Geologi Kompleks menggunakan Metode Common Reflection Surface (CRS). Jurnal Geofisika Eksplorasi, 7(3), 164–177. https://doi.org/10.23960/jge.v7i3.135

Daruartati, H., Setyawan, A., & Kusuma, I. A. (2015). Aplikasi Metode Common Reflection Surface (Crs) Untuk Meningkatkan Hasil Stack Data Seismik Laut 2d Wilayah Perairan “Y”. Youngster Physics Journal, 4(4), 291–298. https://ejournal3.undip.ac.id/index.php/bfd/article/view/9407

Desai, A., Xu, Z., Gupta, M., Chandran, A., Vial-Aussavy, A., & Shrivastava, A. (2021). Raw nav-merge seismic data to subsurface properties with mlp based multi-modal information unscrambler. Advances in Neural Information Processing Systems, 34, 8740–8752. https://openreview.net/pdf?id=HLalhDvDwrQ

Garabito, G. (2021). Prestack seismic data interpolation and enhancement with common‐reflection‐surface–based migration and demigration. Geophysical Prospecting, 69(5), 913–925. https://doi.org/10.1111/1365-2478.13074

Hsu, K. (1990). Wave separation and feature extraction of acoustic well-logging waveforms using Karhunen-Loeve transformation. Geophysics, 55(2), 176–184. https://doi.org/10.1190/1.1442824

Hubral, P. (1983). Computing true amplitude reflections in a laterally inhomogeneous earth. Geophysics, 48(8), 1051–1062. https://doi.org/10.1190/1.1441528

Jäger, R. (1999). The common reflection surface stack: theory and application. MSc Thesis. University of Karlsruhe, Karlsruhe, Germany.

Jang, S., & Lee, D. (2022). Application of Reverse Time Migration to Faults Imaging in Rakhine Basin, Myanmar. Geofluids, 2022(1968793), 13 pages. https://doi.org/10.1155/2022/1968793

Liu, B., & Liu, Q. (2020). Random noise reduction using SVD in the frequency domain. Journal of Petroleum Exploration and Production Technology, 10, 3081–3089. https://doi.org/10.1007/s13202-020-00938-w

Mandal, B., Sen, M. K., Vaidya, V. R., & Mann, J. (2014). Deep seismic image enhancement with the common reflection surface (CRS) stack method: evidence from the Aravalli–Delhi fold belt of northwestern India. Geophysical Journal International, 196(2), 902–917. https://doi.org/10.1093/gji/ggt402

Pahlavanloo, A., Soleimani Monfared, M., & Gallo, C. (2017). Improving seismic image in complex structures by new solving strategies in the CO-CRS and the CO-CDS methods. Iranian Journal of Geophysics, 10(5), 42–56. https://dorl.net/dor/20.1001.1.20080336.1396.10.5.5.2

Prabowo, A., Junursyah, G. M. L., & Hidayat, W. (2021). Analisis Kualitas Data Magnetotelurik Berdasarkan Parameter Koherensi Pada Daerah Bandung, Jawa Barat. Jurnal Mineral, Energi, dan Lingkungan, 4(2), 78–84. https://doi.org/10.31315/jmel.v4i2.3679

Pussak, M., Bauer, K., Stiller, M., & Bujakowski, W. (2014). Improved 3D seismic attribute mapping by CRS stacking instead of NMO stacking: Application to a geothermal reservoir in the Polish Basin. Journal of Applied Geophysics, 103, 186–198. https://doi.org/10.1016/j.jappgeo.2014.01.020

Rad, P. B., & Macelloni, L. (2020). Improving 3D water column seismic imaging using the Common Reflection Surface method. Journal of Applied Geophysics, 179, 104072. https://doi.org/10.1016/j.jappgeo.2020.104072

Sharma, A., Singh, A. K., & Kumar, P. (2018). Combining haar wavelet and Karhunen-Loeve transform for robust and imperceptible data hiding using digital images. Journal of Intelligent Systems, 27(1), 91-103. https://doi.org/10.1515/jisys-2017-0032

Shukla, K., & Jaiswal, P. (2017). Wavefield-based regularization of multicomponent seismic data. SEG Technical Program Expanded Abstracts 2017 (pp. 2575-2579). Society of Exploration Geophysicists. https://doi.org/10.1190/segam2017-17794904.1

Wang, D., Gao, J., Liu, N., & Jiang, X. (2020). Structure-oriented DTGV regularization for random noise attenuation in seismic data. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1757–1771. https://doi.org/10.1109/TGRS.2020.3001141

Zaharov, V. V., Farahi, R. H., Snyder, P. J., Davison, B. H., & Passian, A. (2014). Karhunen–Loève treatment to remove noise and facilitate data analysis in sensing, spectroscopy and other applications. Analyst, 139(22), 5927–5935. https://doi.org/10.1039/C4AN01300J

Zhu, L., Liu, E., & McClellan, J. H. (2015). Seismic data denoising through multiscale and sparsity-promoting dictionary learning. Geophysics, 80(6), WD45-WD57. https://doi.org/10.1190/geo2015-0047.1

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Published

2023-11-02

How to Cite

Zein, E. D. K., Rasimeng, S., & Wijaksono, E. (2023). Advanced Processing of 2D Marine Reflection Seismic Data Using the Common Reflection Surface (CRS) Stack Method with K-L Filter Application: Pengolahan Lanjut Data Seismik Refleksi 2D Lait Menggunakan Metode Common Reflection Surface (CRS) Stack dengan Penerapan KL-Filter. JURNAL GEOCELEBES, 7(2), 168–175. https://doi.org/10.20956/geocelebes.v7i2.22588

Issue

Vol. 7 No. 2: October 2023

Section

Articles

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