The Application of a Various Parameter Gradient Structure Tensor Parallel Method on small Fracture Detection

2017 ◽  
Author(s):  
Chao Feng* ◽  
Jianguo Pan ◽  
Duo nian Xu ◽  
Tuan yu Teng ◽  
Lu Yin
Keyword(s):  
Structure Tensor ◽  
Gradient Structure ◽  
Fracture Detection ◽  
Parallel Method ◽  
Parameter Gradient

Application of gradient structure tensor method in CBM fracture identification and sweet spot prediction

2019 ◽  
Vol 12 (20) ◽  
Author(s):  
Chengqiang Zhao ◽  
Yubang Zhou ◽  
Yong Li ◽  
Ping Zhao ◽  
Lingling Huang
Keyword(s):  
Structure Tensor ◽  
Gradient Structure ◽  
Sweet Spot ◽  
Tensor Method ◽  
Fracture Identification

Estimating the dip by constructing gradient structure tensor on instantaneous phase

2014 ◽  
Author(s):  
Xiaokai Wang* ◽  
Changchun Yang ◽  
Wenchao Chen ◽  
Jinghuai Gao ◽  
Zhenyu Zhu
Keyword(s):  
Structure Tensor ◽  
Gradient Structure ◽  
Instantaneous Phase

Adopting multispectral dip components for coherence and curvature attribute computations

2020 ◽  
Vol 39 (8) ◽  
pp. 593-596
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt
Keyword(s):  
Covariance Matrices ◽  
Structure Tensor ◽  
Gradient Structure ◽  
Gray Level ◽  
Coherent Noise ◽  
Spectral Components ◽  
Frequency Components ◽  
Occurrence Matrix

Although volumetric coherence is the most widely used geometric attribute, accurate estimates of volumetric dip are in some ways more important. Coherence, amplitude gradients, and gray-level co-occurrence matrix textures should be computed along structural dip. Curvature and aberrancy are computed from volumetric estimates of structural dip. Because of both differences in resolution and sensitivity to coherent noise, different frequency components may exhibit different dip. In recent years, improvements in coherence have been noticed where covariance matrices of individual spectral components are summed rather than summing the original broadband data. We extend the same concepts to compute multispectral dip estimates by using a gradient structure tensor algorithm. The results are sharper, less smeared images on the dip components. The higher-resolution dip estimates result in higher-resolution curvature and aberrancy estimates. Availability of sharper estimates of dip to guide coherence attribute results in more continuous, less noisy discontinuities.

scholarly journals Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor

2021 ◽  
Vol 13 (16) ◽  
pp. 3146
Author(s):  
Dong Chen ◽  
Jing Li ◽  
Shaoning Di ◽  
Jiju Peethambaran ◽  
Guiqiu Xiang ◽  
...  
Keyword(s):  
Critical Points ◽  
Multiple Scales ◽  
Point Clouds ◽  
Structure Tensor ◽  
Compact Representation ◽  
Gradient Structure ◽  
Individual Point ◽  
Building Facade ◽  
Definition Of ◽  
The Individual

This paper proposes a building façade contouring method from LiDAR (Light Detection and Ranging) scans and photogrammetric point clouds. To this end, we calculate the confidence property at multiple scales for an individual point cloud to measure the point cloud’s quality. The confidence property is utilized in the definition of the gradient for each point. We encode the individual point gradient structure tensor, whose eigenvalues reflect the gradient variations in the local neighborhood areas. The critical point clouds representing the building façade and rooftop (if, of course, such rooftops exist) contours are then extracted by jointly analyzing dual-thresholds of the gradient and gradient structure tensor. Based on the requirements of compact representation, the initial obtained critical points are finally downsampled, thereby achieving a tradeoff between the accurate geometry and abstract representation at a reasonable level. Various experiments using representative buildings in Semantic3D benchmark and other ubiquitous point clouds from ALS DublinCity and Dutch AHN3 datasets, MLS TerraMobilita/iQmulus 3D urban analysis benchmark, UAV-based photogrammetric dataset, and GeoSLAM ZEB-HORIZON scans have shown that the proposed method generates building contours that are accurate, lightweight, and robust to ubiquitous point clouds. Two comparison experiments also prove the superiority of the proposed method in terms of topological correctness, geometric accuracy, and representation compactness.

A method for reservoir heterogeneity prediction based on Gradient Structure Tensor

2014 ◽  
Author(s):  
Wang Enli ◽  
Yang Wuyang ◽  
Yan Gaohan ◽  
Yang Qing ◽  
Jiang Chunling
Keyword(s):  
Structure Tensor ◽  
Gradient Structure ◽  
Reservoir Heterogeneity
Sign in / Sign up

Export Citation Format

Share Document