Developing an Automatic Algorithm for Salt Dome Detection Based on Seismic Attributes and Mathematical Morphology Operators

Authors

1 MSc student, Faculty of Mining, Petroleum and Geophysics Engineering; Shahrood University of Technology, Shahrood, Iran

2 Assistant Professor, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology

3 Associate Professor, Faculty of Mining, Petroleum and Geophysics Engineering; Shahrood University of Technology, Shahrood, Iran

Abstract

This study addresses the critical challenge in seismic interpretation of accurately delineating salt dome boundaries, which is essential for hydrocarbon exploration and subsurface modeling. It introduces a novel, fully automated hybrid algorithm designed to detect these boundaries and construct corresponding 3D models directly from seismic data. The methodology integrates seismic attribute analysis with mathematical morphological operators. In the first step, attribute analysis is performed on the seismic data volume to detect the salt dome texture. Three seismic attributes, texture energy, texture entropy, and chaos have been examined. The attribute volume is then normalized, binarized, and processed through a sequence of morphological operations, including opening and hole-filling to produce a high-resolution refined final salt dome volume. The algorithm was successfully tested and validated on a 3D seismic dataset from the geologically complex Strait of Hormuz in Iran. The evaluation of the results showed that the proposed method not only performs with good accuracy, close to manual interpretation, but also achieves this result within a very reasonable and acceptable runtime of approximately 30 seconds. Furthermore, quantitative evaluation of the results indicated that among the seismic attributes used, the texture energy attribute exhibits the best performance with the highest average symmetric surface distance (ASSD) in identifying salt dome boundaries. This capability minimizes the reliance of the interpretation process on the interpreter's subjective judgment and enables efficient processing of large-scale seismic data in an industrial context.

Keywords

References

پاژنگ، س.، کدخدایی، ع.، زمانی، ب.، برگریزان، م. و یوسف پور، م، 1394، معرفی 17 گنبد نمکی مدفون و غیرمدفون براساس داده‌های لرزه‌ای در تنگه هرمز ( بلوک E)، پژوهش نفت، 25 (84)،150-160.
چمبری، ر.، روشندل کاهو، ا.، یوسفی، م. و سلیمانی منفرد، م.، 1397، شناسایی مرز گنبد نمکی با استفاده از تلفیق نشانگرهای لرزه‌ای در محیط GIS، پژوهش های ژئوفیزیک کاربردی، 4 (2)، 277-292.
روشندل کاهو، ا.، سلیمانی منفرد، م. و رداد، م.، 1400، شناسایی و مدل‌سازی گنبد نمکی در داده‌های لرزه‌ای با استفاده از گرادیان بافت سه‌بعدی، مجله ژئوفیزیک ایران، 15 (1)، 19-33.
 
Chopra, S., and Alexeev, V., 2006, Applications of texture attribute analysis to 3D seismic data, The Leading Edge, 25 (8), 934-940.
Chopra, S., and Marfurt, K. J., 2007, Seismic attributes for prospect identification and reservoir characterization, SEG Geophysical Developments Series No. 11.‏
Di, H., and AlRegib, G., 2017, Seismic multi-attribute classification for salt boundary detection-a comparison, 79th EAGE Conference and Exhibition.‏
Eichkitz, C. G., Amtmann, J., and Schreilechner, M. G., 2013, Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions, Computers & Geosciences, 60, 176-183.‏
Hale, D. 2013, Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images, Geophysics, 78(2), O33-O43.
Heimann, T., Van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G. and Bello, F., 2009, Comparison and evaluation of methods for liver segmentation from CT datasets, IEEE transactions on medical imaging, 28 (8), 1251-1265.
Huang, W., Wang, R., Zhang, D., Zhou, Y., Yang, W., and Chen, Y., 2017, Mathematical morphological filtering for linear noise attenuation of seismic data, Geophysics, 82 (6), V369-V384.‏
Jackson, M. P., and Hudec, M. R. 2017, Salt Tectonics: Principles and Practice, Cambridge University Press.
Jones, I. F., and Davison, I., 2014, Seismic imaging in and around salt bodies, Interpretation, 2 (4), SL1-SL20.‏
Khayer, K., Roshandel Kahoo, A., Soleimani Monfared, M. and Kavoosi, K., 2022, Combination of seismic attributes using graph-based methods to identify the salt dome boundary, Journal of Petroleum Science and Engineering, 215, 110625.
Lawal, A., Mayyala, Q., Zerguine, A., and Beghdadi, A. 2021, Salt dome detection using context-aware saliency, 28th European Signal Processing Conference (EUSIPCO), 1906-1910.‏
Liu, Z., Wheaton, D., and Wang, B., 2019, Seismic data denoising with mathematical morphological filters, SEG International Exposition and Annual Meeting.
Mousavi, J., Radad, M., Soleimani Monfared, M., and Roshandel Kahoo, A. 2022, Fault enhancement in seismic images by introducing a novel strategy integrating attributes and image analysis techniques, Pure and Applied Geophysics, 179 (5), 1645-1660.‏
Otsu, N., 1979, A threshold selection method from gray-level histograms. Automatica, 11, 285-296.
Schultz-Ela, D. D., Jackson, M. P., and Vendeville, B. C., 1993, Mechanics of active salt diapirism, Tectonophysics, 228 (3-4), 275-312.‏
Shih, F. Y., 2017, Image processing and mathematical morphology: fundamentals and applications. CRC press.
Smith, S. W., 1997, The Scientist & Engineer's Guide to Digital Signal Processing. California Technical Pub.
Soille, P., 1999, Morphological image analysis: principles and applications, Springer Berlin, Heidelberg.
Tavakolizadeh, N., and Bagheri, M., 2022, Multi-attribute selection for salt dome detection based on SVM and MLP machine learning techniques, Natural Resources Research, 31 (1), 353-370.‏
Wang, R., 2005, Noise-eliminated method by morphologic filtering in seismic data processing, Oil Geophysical Prospecting, 40 (3), 277.‏
Wang, Z., 2018, Computational seismic interpretation using geometric representation and tensor-based texture analysis, PhD. dissertation, Georgia Institute of Technology, Atlanta, USA.‏
Volume 12, Issue 2
September 2026
Pages 89-98

Files

Share

How to cite

Statistics