Improving seismic fault detection by super-attribute-based classification

2019 ◽  
Vol 7 (3) ◽  
pp. SE251-SE267 ◽  
Author(s):  
Haibin Di ◽  
Mohammod Amir Shafiq ◽  
Zhen Wang ◽  
Ghassan AlRegib
Keyword(s):  
Fault Detection ◽  
Seismic Data ◽  
Support Vector ◽  
3D Seismic ◽  
Subsurface Structure ◽  
Computationally Efficient ◽  
Seismic Fault ◽  
Conventional Methods ◽  
Fault Interpretation ◽  
3D Seismic Data

Fault interpretation is one of the routine processes used for subsurface structure mapping and reservoir characterization from 3D seismic data. Various techniques have been developed for computer-aided fault imaging in the past few decades; for example, the conventional methods of edge detection, curvature analysis, red-green-blue rendering, and the popular machine-learning methods such as the support vector machine (SVM), the multilayer perceptron (MLP), and the convolutional neural network (CNN). However, most of the conventional methods are performed at the sample level with the local reflection pattern ignored and are correspondingly sensitive to the coherent noises/processing artifacts present in seismic signals. The CNN has proven its efficiency in utilizing such local seismic patterns to assist seismic fault interpretation, but it is quite computationally intensive and often demands higher hardware configuration (e.g., graphics processing unit). We have developed an innovative scheme for improving seismic fault detection by integrating the computationally efficient SVM/MLP classification algorithms with local seismic attribute patterns, here denoted as the super-attribute-based classification. Its added values are verified through applications to the 3D seismic data set over the Great South Basin (GSB) in New Zealand, where the subsurface structure is dominated by polygonal faults. A good match is observed between the original seismic images and the detected lineaments, and the generated fault volume is tested usable to the existing advanced fault interpretation tools/modules, such as seeded picking and automatic extraction. It is concluded that the improved performance of our scheme results from its two components. First, the SVM/MLP classifier is computationally efficient in parsing as many seismic attributes as specified by interpreters and maximizing the contributions from each attribute, which helps minimize the negative effects from using a less useful or “wrong” attribute. Second, the use of super attributes incorporates local seismic patterns into training a fault classifier, which helps exclude the random noises and/or artifacts of distinct reflection patterns.

3D seismic fault detection using the Gaussian process regression, a study on synthetic and real 3D seismic data

2020 ◽  
Vol 195 ◽  
pp. 107746
Author(s):  
Maryam Noori ◽  
Hossein Hassani ◽  
Abdolrahim Javaherian ◽  
Hamidreza Amindavar
Keyword(s):  
Fault Detection ◽  
Gaussian Process ◽  
Seismic Data ◽  
Gaussian Process Regression ◽  
3D Seismic ◽  
Seismic Fault ◽  
3D Seismic Data

A comparison of classification techniques for seismic facies recognition

2015 ◽  
Vol 3 (4) ◽  
pp. SAE29-SAE58 ◽  
Author(s):  
Tao Zhao ◽  
Vikram Jayaram ◽  
Atish Roy ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Support Vector ◽  
3D Seismic ◽  
Generative Topographic Mapping ◽  
Learning Methods ◽  
Facies Classification ◽  
Data Volume ◽  
3D Seismic Data

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised learning methods also highlighted features that might otherwise be overlooked.

Seismic fault attribute estimation using a local fault model

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. O73-O80 ◽  
Author(s):  
Yihuai Lou ◽  
Bo Zhang ◽  
Ruiqi Wang ◽  
Tengfei Lin ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Fault Plane ◽  
3D Seismic ◽  
Seismic Amplitude ◽  
Seismic Fault ◽  
Analysis Window ◽  
Full Analysis ◽  
Fault Interpretation ◽  
Analysis Point ◽  
Staircase Artifacts

Faults in the subsurface can be an avenue of, or a barrier to, hydrocarbon flow and pressure communication. Manual interpretation of discontinuities on 3D seismic amplitude volume is the most common way to define faults within a reservoir. Unfortunately, 3D seismic fault interpretation can be a time-consuming and tedious task. Seismic attributes such as coherence help define faults, but suffer from “staircase” artifacts and nonfault-related stratigraphic discontinuities. We assume that each sample of the seismic data is located at a potential fault plane. The hypothesized fault divides the seismic data centered at the analysis sample into two subwindows. We then compute the coherence for the two subwindows and full analysis window. We repeat the process by rotating the hypothesized fault plane along a set of user-defined discrete fault dip and azimuth. We obtain almost the same coherence values for the subwindows and the full window if the analysis point is not located at a fault plane. The “best” fault plane results in maximum coherence for the subwindows and minimum coherence for the full window if the analysis point is located at a fault plane. To improve the continuity of the fault attributes, we finally smooth the fault probability attribute along the estimated fault plane. We illustrate the effectiveness of our workflow by applying it to a synthetic and two real seismic data. The results indicate that our workflow successfully produces a continuous fault attribute without staircase artifacts and stratigraphic discontinuities.

Most Extreme Curvature and its Application to Seismic Structural Interpretation

2014 ◽  
Vol 522-524 ◽  
pp. 1266-1269
Author(s):  
Zhi Hong Zheng ◽  
Jie Qing Tan ◽  
Kang Liu
Keyword(s):  
Fault Detection ◽  
Seismic Data ◽  
Negative Curvature ◽  
Fault Plane ◽  
Positive Curvature ◽  
Hydrocarbon Exploration ◽  
3D Seismic ◽  
Structural Interpretation ◽  
Structural Attribute ◽  
3D Seismic Data

Curvature, as a newly developing structural attribute, closely related to the bending of geologic body and received extensive attention of the researchers in recent years. Most positive curvature and most negative curvature in particular are widely used in fault detection and fracture prediction. In this paper, most extreme curvature was introduced into structural interpretation. Compared with the conventional curvature attributes, the advantage of most extreme curvature in fault detection was highlighted in this study. It not only clearly shown the property of fault, but also precisely indicated the location of fault plane and its variation along the strike by the dramatic change in curvature values. These findings contribute to better structural interpretation of 3D seismic data and are of great significance to hydrocarbon exploration.

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