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  1.  6
    A Comparative Study of Texture Attributes for Characterizing Subsurface Structures in Seismic Volumes.Zhiling Long, Yazeed Alaudah, Muhammad Ali Qureshi, Yuting Hu, Zhen Wang, Motaz Alfarraj, Ghassan AlRegib, Asjad Amin, Mohamed Deriche, Suhail Al-Dharrab & Haibin Di - 2018 - Interpretation: SEG 6 (4):T1055-T1066.
    We have explored how to computationally characterize subsurface geologic structures presented in seismic volumes using texture attributes. For this purpose, we conduct a comparative study of typical texture attributes presented in the image processing literature. We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i.e., seismic volume labeling. For this application, a data volume is automatically segmented into various structures, each assigned with its corresponding label. If the labels are assigned with (...)
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  2.  10
    3D Structural-Orientation Vector Guided Autotracking for Weak Seismic Reflections: A New Tool for Shale Reservoir Visualization and Interpretation.Haibin Di, Dengliang Gao & Ghassan AlRegib - 2018 - Interpretation: SEG 6 (4):SN47-SN56.
    Recognizing and tracking weak reflections, which are characterized by low amplitude, low signal-to-noise ratio, and low degree of lateral continuity, is a long-time issue in 3D seismic interpretation and reservoir characterization. The problem is particularly acute with unconventional, fractured shale reservoirs, in which the impedance contrast is low and/or reservoir beds are below the tuning thickness. To improve the performance of interpreting weak reflections associated with shale reservoirs, we have developed a new workflow for weak-reflection tracking guided by a robust (...)
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  3.  9
    A Texture-Based Interpretation Workflow with Application to Delineating Salt Domes.Muhammad Amir Shafiq, Zhen Wang, Ghassan AlRegib, Asjad Amin & Mohamed Deriche - 2017 - Interpretation: SEG 5 (3):SJ1-SJ19.
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  4.  6
    Improving Seismic Fault Detection by a Super-Attribute-Based Classification.Haibin Di, Muhammad Amir Shafiq, Zhen Wang & Ghassan AlRegib - forthcoming - Interpretation:1-56.
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  5.  5
    A Machine-Learning Benchmark for Facies Classification.Yazeed Alaudah, Patrycja Michałowicz, Motaz Alfarraj & Ghassan AlRegib - 2019 - Interpretation 7 (3):SE175-SE187.
    The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely, the absence of large publicly available annotated data sets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not (...)
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  6.  7
    Reflector Dip Estimates Based on Seismic Waveform Curvature/Flexure Analysis.Haibin Di & Ghassan AlRegib - 2019 - Interpretation 7 (2):SC1-SC9.
    Reliable estimation of the reflector dip serves as a fundamental and essential tool for subsurface structure interpretation from 3D seismic surveys. We have developed a new method for accurate dip estimation, which consists of two major components. First, the curvature/flexure concept is adapted from the traditional reflector geometry analysis to work for the seismic waveforms, denoted as the waveform curvature and waveform flexure, respectively. Physically, both of them are capable of measuring the most and least apparent variation of the local (...)
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  7.  1
    Introduction to Special Section: Machine Learning in Seismic Data Analysis.Haibin Di, Tao Zhao, Vikram Jayaram, Xinming Wu, Lei Huang, Ghassan AlRegib, Jun Cao, Mauricio Araya-Polo, Satinder Chopra, Saleh Al-Dossary, Fangyu Li, Erwan Gloaguen, Youzuo Lin, Anne Solberg & Hongliu Zeng - 2019 - Interpretation 7 (3):SEi-SEii.
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  8.  2
    Semisupervised Sequence Modeling for Elastic Impedance Inversion.Motaz Alfarraj & Ghassan AlRegib - 2019 - Interpretation 7 (3):SE237-SE249.
    Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints — the lack of which might lead to undesirable results. To overcome this issue, we have developed a semisupervised sequence modeling framework based on recurrent neural networks for elastic impedance inversion from multiangle seismic data. Specifically, seismic traces and elastic impedance traces are modeled as a time series. Then, a neural-network-based (...)
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  9.  11
    Automated Salt-Dome Detection Using an Attribute Ranking Framework with a Dictionary-Based Classifier.Asjad Amin, Mohamed Deriche, Muhammad Amir Shafiq, Zhen Wang & Ghassan AlRegib - 2017 - Interpretation: SEG 5 (3):SJ61-SJ79.
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  10. Improving Seismic Fault Detection by Super-Attribute-Based Classification.Haibin Di, Mohammod Amir Shafiq, Zhen Wang & Ghassan AlRegib - 2019 - Interpretation 7 (3):SE251-SE267.
    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, the multilayer perceptron, and the convolutional neural network. However, most of the conventional methods are performed at the sample level with the local reflection (...)
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