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  1.  31
    A Comparison of Classification Techniques for Seismic Facies Recognition.Tao Zhao, Vikram Jayaram, Atish Roy & Kurt J. Marfurt - 2015 - Interpretation: SEG 3 (4):SAE29-SAE58.
    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 (...)
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  2.  5
    Characterizing a Mississippian Tripolitic Chert Reservoir Using 3D Unsupervised and Supervised Multiattribute Seismic Facies Analysis: An Example From Osage County, Oklahoma.Atish Roy, Benjamin L. Dowdell & Kurt J. Marfurt - 2013 - Interpretation: SEG 1 (2):SB109-SB124.
    Seismic interpretation is based on the identification of reflector configuration and continuity, with coherent reflectors having a distinct amplitude, frequency, and phase. Skilled interpreters may classify reflector configurations as parallel, converging, truncated, or hummocky, and use their expertise to identify stratigraphic packages and unconformities. In principal, a given pattern can be explicitly defined as a combination of waveform and reflector configuration properties, although such “clustering” is often done subconsciously. Computer-assisted classification of seismic attribute volumes builds on the same concepts. Seismic (...)
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  3.  6
    Generative Topographic Mapping for Seismic Facies Estimation of a Carbonate Wash, Veracruz Basin, Southern Mexico.Atish Roy, Araceli S. Romero-Peláez, Tim J. Kwiatkowski & Kurt J. Marfurt - 2014 - Interpretation: SEG 2 (1):SA31-SA47.
    Seismic facies estimation is a critical component in understanding the stratigraphy and lithology of hydrocarbon reservoirs. With the adoption of 3D technology and increasing survey size, manual techniques of facies classification have become increasingly time consuming. Besides, the numbers of seismic attributes have increased dramatically, providing increasingly accurate measurements of reflector morphology. However, these seismic attributes add multiple “dimensions” to the data greatly expanding the amount of data to be analyzed. Principal component analysis and self-organizing maps are popular techniques to (...)
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  4.  17
    Introduction to Special Section: Pattern Recognition and Machine Learning.Vikram Jayaram, Per Age Avseth, Kostia Azbel, Theirry Coléou, Deepak Devegowda, Paul de Groot, Dengliang Gao, Kurt Marfurt, Marcilio Matos, Tapan Mukerji, Manuel Poupon, Atish Roy, Brian Russell, Brad Wallet & Vikas Kumar - 2015 - Interpretation: SEG 3 (4):SAEi-SAEii.
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  5.  1
    Introduction to Special Section: Insights to Digital Oilfield Data Using Artificial Intelligence and Big Data Analytics.Vikram Jayaram, Atish Roy, Bill Barna, Deepak Devegowda, Jacqueline Floyd, Pradeepkumar Ashok, Aria Abubakar, Anisha Kaul & Emmanuel Schnetzler - 2019 - Interpretation 7 (3):SFi-SFi.
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