To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. W...To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.展开更多
To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,...To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.展开更多
基金supported by the National Natural Science Foundation of China (No. 41004054) Research Fund for the Doctoral Program of Higher Education of China (No. 20105122120002)Natural Science Key Project, Sichuan Provincial Department of Education (No. 092A011)
文摘To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.
基金This study was supported by the National Natural Science Foundation of China(No.61403035)Natural Science Foundation of Beijing Municipality(No.9152009)Science and Technology Innovation Ability Construction Project of Beijing Academy of Agriculture and Forestry Science(No.KJCX20170206).
文摘To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.
