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ABSTRACT
Channels are one exploratory object in the oil and gas industry, and detailed channel studies can help identify the sedimentation processes in an area. In addition, channels are a drilling hazard in deep-water sedimentary basins. However, manual interpretation of channels is time-consuming and needs skill and experience. Therefore, in this paper, using morphological filters, we present an algorithm for automatically extracting the channel edges from seismic data. We used a morphological gradient algorithm with an ellipsoidal adaptive structuring element to detect edges. Morphological gradient operators highlight the image edges within the neighbourhood of the structuring element. The adaptive structuring element at each point of the data align with the edges and contours at that point, and so can be more effective in edge detection. Eigenvalue decomposition of the gradient structure tensor gives an estimate of the orientation and anisotropy rate of objects within the data. Hence, parameters of the adaptive structuring element are obtained from the eigenvalues of a 3D gradient structure tensor. Finally, the proposed algorithm is applied to synthetic and real seismic data containing a channel, and the results are compared with other well-known edge detection methods such as Canny, Sobel, semblance and negative curvature. The proposed algorithm was able to correctly identify edge locations, and also outperforms other methods in the presence of noise.
Acknowledgements
The authors thank the suppliers of DIPimage and especially Professor Cris Luengo Hendriks for his valuable guidance.
Disclosure statement
No potential conflict of interest was reported by the authors.