Interactive Fault Extraction in 3-D Seismic Data Using the Hough Transform and Tracking Vectors

Wang, Zhen; AlRegib, Ghassan

doi:10.1109/tci.2016.2626998

Cited by 24 publications

(15 citation statements)

References 20 publications

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“…For example, Jacquemin and Mallet (2005) propose using the cascaded Hough transform to roughly detect fault surfaces in seismic volumes. To improve the local accuracy of detected faults, Wang and AlRegib (2017) propose delineating faults using line-like features extracted by the Hough transform. Figure 2 illustrates the diagram of the proposed method, in which authors first highlight likely fault points by applying a hard threshold on the discontinuity map and then extract line-like fault features (yellow line segments) using the Hough transform.…”

Section: Fault Detectionmentioning

confidence: 99%

“…For example, instantaneous attributes (Taner et al, 1979) are derived from the Hilbert transform, and spectral attributes (Sinha et al, 2005) are the results of multiresolution analysis. Since edge, texture, and shape information are important to both images from nature and seismic images, edge detectors (Amin and Deriche, 2015), texture descriptors (Shafiq et al, 2017), and the Hough transform (Wang and AlRegib, 2017), although initially designed for use with natural images, have shown strong capability in identifying geologic structures in seismic images.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2. Diagram of the fault-detection method proposed byWang and AlRegib (2017), which utilizes the Hough transform to extract fault features and remove false ones using geologic constraints.Downloaded 06/13/18 to 163.185.148.245. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/…”

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Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

et al. 2018

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As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as visual cues of geologic structures, such as texture and geometry. With the dramatic increase in size of seismic data acquired for hydrocarbon exploration, structural interpretation has become more time consuming and labor intensive. By treating seismic data as images rather than signal traces, researchers have been able to utilize advanced image-processing and machine-learning techniques to assist interpretation directly. In this paper, we mainly focus on the interpretation of two important geologic structures, faults and salt domes, and summarize interpretation workflows based on typical or advanced image-processing and machine-learning algorithms. In recent years, increasing computational power and the massive amount of available data have led to the rise of deep learning. Deep-learning models that simulate the human brain's biological neural networks can achieve state-of-the-art accuracy and even exceed human-level performance on numerous applications. The convolutional neural network — a form of deep-learning model that is effective in analyzing visual imagery — has been applied in fault and salt dome interpretation. At the end of this review, we provide insight and discussion on the future of structural interpretation.

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Section: Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

et al. 2018

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“…However, these techniques have resulted in the striking growth of acquired seismic data, which in turn are causing manual interpretation extremely time consuming and labor intensive. To aid geophysicists in the interpretation process, researchers have proposed several fully-and semi-automated fault delineation methods based on edge detection, texture, spectral decomposition, seismic attributes, Hough transform, visual saliency, and different image processing techniques (Gibson et al, 2003;Silva et al, 2005;Jacquemin and Mallet, 2005;Pepper and Bejarano, 2005;Cohen et al, 2006;AlBinHassan et al, 2006;Barbato, 2012;Aarre et al, 2012;Zhang et al, 2014;Hale, 2013;Lawal et al, 2016;Wu and Hale, 2016;Wang and AlRegib, 2016). However, to the best of our knowledge, such methods often fail when applied to the automated delineation of listric faults.…”

Section: Introductionmentioning

confidence: 99%

A Texture-based Approach for Automated Detection of Listric Faults

Shafiq¹,

Di²,

AlRegib³

2017

Proceedings

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Classical interpretation of listric faults is usually performed manually by the geophysicist, which is not only time consuming and labor intensive but also susceptible to interpreter bias. In this paper, we propose a texture-based interpretation workflow for the automated delineation of major listric faults in a 3D migrated seismic volume. In the first step, we compute three-dimensional gradient of texture (3D-GoT), which describes the texture dissimilarity between neighboring cubes around each voxel in a seismic volume across time or depth, crossline, and inline directions. In the second step, we calculate an adaptive global threshold and apply it to the 3D-GoT map to obtain a binary map, which highlights the probable boundary regions of the listric faults. Finally, we apply post-processing to the obtained binary maps, which include morphological opening and curve fitting, to yield a delineated listric fault surface within the migrated seismic volume. The experimental results on a real seismic dataset from the Stratton field in the Texas Gulf coast show the effectiveness of the proposed workflow, indicating its great potential for assisting the structural interpretation.

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“…Wang et al (2014) performed color transformations on semblance maps, followed by a skeletonization of the highlighted fault regions. Recently the same authors proposed the combination of the Hough Transform and tracking vector to extract faults from binarized coherence maps (Wang and AlRegib, 2017). Those methods generally fall into two categories: faults are well extracted but many artifacts remain, or the result is clean, but not all faults are detected Di and Gao, 2017).…”

Section: Training Validation and Testmentioning

confidence: 99%

Modeling of Geobodies: Ai for Seismic Fault Detection and All-Quadrilateral Mesh Generation

POCHET¹

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Interactive Fault Extraction in 3-D Seismic Data Using the Hough Transform and Tracking Vectors

Cited by 24 publications

References 20 publications

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

A Texture-based Approach for Automated Detection of Listric Faults

Modeling of Geobodies: Ai for Seismic Fault Detection and All-Quadrilateral Mesh Generation

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