Volumetric dips and azimuth of pre‐stack seismic data using the gradient structure tensor

2010 ◽  
Author(s):  
Pascal Klein ◽  
Loic Richard
Keyword(s):  
Seismic Data ◽  
Structure Tensor ◽  
Gradient Structure

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.

scholarly journals Seismic data filtering using non-local means algorithm based on structure tensor

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Shuai Yang ◽  
Anqing Chen ◽  
Hongde Chen
Keyword(s):  
Seismic Data ◽  
Weighted Average ◽  
Structure Tensor ◽  
Control Factor ◽  
Local Contrast ◽  
Structure Information ◽  
Geometry Structure ◽  
Local Means ◽  
Seismic Image ◽  
Non Local

AbstractNon-Local means algorithm is a new and effective filtering method. It calculates weights of all similar neighborhoods’ center points relative to filtering point within searching range by Gaussian weighted Euclidean distance between neighborhoods, then gets filtering point’s value by weighted average to complete the filtering operation. In this paper, geometric distance of neighborhood’s center point is taken into account in the distance measure calculation, making the non-local means algorithm more reasonable. Furthermore, in order to better protect the geometry structure information of seismic data, we introduce structure tensor that can depict the local geometrical features of seismic data. The coherence measure, which reflects image local contrast, is extracted from the structure tensor, is integrated into the non-local means algorithm to participate in the weight calculation, the control factor of geometry structure similarity is added to form a non-local means filtering algorithm based on structure tensor. The experimental results prove that the algorithm can effectively restrain noise, with strong anti-noise and amplitude preservation effect, improving PSNR and protecting structure information of seismic image. The method has been successfully applied in seismic data processing, indicating that it is a new and effective technique to conduct the structure-preserved filtering of seismic data.

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