1. Fault Interpretation Using 3D Hough Transform
Detection of faults plays an important role in the characterization of reservoir regions. We propose an automatic fault surface detection method using 3D Hough transform to improve the interpretation efficiency. We first highlight the likely fault points in seismic data by thresholding the corresponding discontinuity volumes. Then, we apply 3D Hough transform to detect the likely fault planes in seismic volumes. After filtering out the noisy planes, we apply the weighted plane fitting method to extract the smooth fault surfaces from the remaining fault planes. Experimental results show that the proposed method has the capability of detecting fault surfaces in real seismic data with high accuracy and fewer human interventions.
2. Fault Interpretation Using Color Blending
In the field of seismic interpretation, univariate databased maps are commonly used by interpreters, especially for fault detection. In these maps, the contrast between target regions and the background is one of the main factors that affect the accuracy of interpretation. Since univariate data-based maps are not capable of providing a high-contrast representation, to overcome this issue, we turn them into multivariate data-based representations using color blending. We blend neighboring time sections or frames that are viewed in the time direction of migrated seismic volumes as if they corresponded to the red, green, and blue channels of a color image. Furthermore, to extract more reliable structural information, we apply color transformations. Experimental results show that the proposed method improves the accuracy of fault detection by limiting the average distance between detected fault lines and the ground truth into one pixel.
- Z. Wang and G. AlRegib, “Automatic fault surface detection using 3D Hough Hough Transform”2014 SEG Annual Meeting, Denver, Colorado, Oct. 26-31, 2014. [PDF] [Poster/Lec] [Bib] [Code]
- Z. Wang, D. Temel and G. AlRegib, “Fault Detection Using Color Blending and Color Transformations” 2nd IEEE Global Conference on Signal and Information Processing, Atlanta, USA, Dec. 3-5, 2014. [PDF] [Poster/Lec] [Bib] [Code]