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

Articles in Press

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

10.22044/jrag.2026.17088.1377

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.

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Articles in Press, Accepted Manuscript
Available Online from 12 April 2026

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