Detection in seismic data using curvelet transform and tensor-based elliptical adaptive structuring elements

• Published in: Journal of applied geophysics 2020-01, Vol.172, p.103881, Article 103881
• Main Authors: Boustani, Bahareh, Javaherian, Abdolrahim, Nabi-Bidhendi, Majid, Torabi, Siyavash, Amindavar, Hamid Reza
• Format: Article
• Language: English
• Subjects: Channel edge; Curvelet transform; Elliptical adaptive structuring element; Gradient structure tensor; Morphological gradient; Top-hat
• ISSN: 0926-9851; 1879-1859
• DOI: 10.1016/j.jappgeo.2019.103881

Channels are one of the vital issues in the exploration of oil and gas. They can be considered as reservoirs if they are filled with porous and permeable material and placed in a suitable position. A detailed study of the channels can help identify the sedimentation processes of the area, the intensity, and direction of the sea currents. Manual interpretation of the channels is difficult and requires skill. Therefore, in this paper, the adaptive curvelet and morphological gradient algorithm (ACMG) is used for the automatic interpretation of the channels. First, the morphological gradient is applied to extract the edges of the channels; then, the curvelet transform is used to increase the signal-to-noise ratio. The morphological top-hat operator extracts the local maxima of curvelet sub-bands. In this workflow, we applied an elliptical adaptive structuring element (EASE) based on Gradient structure tensor (GST). For the construction of the 2D GST, the horizontal and vertical gradients of the image were calculated by the first-order Gaussian derivative. The eigenvalue decomposition of each structure tensor can provide an estimate of the direction and anisotropy rate of the image objects. Hence, parameters of the elliptical structuring element are obtained by eigenvalue decomposition of the GST. To evaluate ACMG, we compared the final output with the results of the non-adaptive curvelet and morphological gradient algorithm (CMG). Also, we compared it with the common edge-detectors such as Canny, Sobel, Laplacian of Gaussian, and similarity attribute. The comparison shows that both CMG and ACMG well extracted the edge of the channels with the higher signal-to-noise ratio. •Channels can have reservoir potential; also can be a drilling hazard.•We used the curvelet transform and the morphological gradient in channel edge detection.•Morphological gradient algorithm detects channel edges.•In post processing, curvelet transform is used to improve the signal-to-noise ratio.•We used elliptical adaptive structuring element based on local structure tensor.