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  1.  25
    Attribute Expression of Fault-Controlled Karst — Fort Worth Basin, Texas: A Tutorial.Jie Qi, Bo Zhang, Huailai Zhou & Kurt Marfurt - 2014 - Interpretation: SEG 2 (3):SF91-SF110.
    Much of seismic interpretation is based on pattern recognition, such that experienced interpreters are able to extract subtle geologic features that a new interpreter may easily overlook. Seismic pattern recognition is based on the identification of changes in amplitude, phase, frequency, dip, continuity, and reflector configuration. Seismic attributes, which providing quantitative measures that can be subsequently used in risk analysis and data mining, partially automate the pattern recognition problem by extracting key statistical, geometric, or kinematic components of the 3D seismic (...)
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  2.  39
    Seismic Attenuation Attributes with Applications on Conventional and Unconventional Reservoirs.Fangyu Li, Sumit Verma, Huailai Zhou, Tao Zhao & Kurt J. Marfurt - 2016 - Interpretation: SEG 4 (1):SB63-SB77.
    Seismic attenuation, generally related to the presence of hydrocarbon accumulation, fluid-saturated fractures, and rugosity, is extremely useful for reservoir characterization. The classic constant attenuation estimation model, focusing on intrinsic attenuation, detects the seismic energy loss because of the presence of hydrocarbons, but it works poorly when spectral anomalies exist, due to rugosity, fractures, thin layers, and so on. Instead of trying to adjust the constant attenuation model to such phenomena, we have evaluated a suite of seismic spectral attenuation attributes to (...)
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  3.  24
    The Thickness Imaging of Channels Using Multiple-Frequency Components Analysis.Yan Ye, Bo Zhang, Niu Cong, Jie Qi & Huailai Zhou - 2019 - Interpretation 7 (1):B1-B8.
    Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components with a red color, medium-frequency components with a green color, and high-frequency components with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same (...)
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  4.  21
    The Thickness Imaging of Channels Using Multiple-Frequency Components Analysis.Yan Ye, Bo Zhang, Cong Niu, Jie Qi & Huailai Zhou - 2019 - Interpretation: SEG 7 (1):B1-B8.
    Blending of different frequency components of seismic traces is a common way to estimate the relative time thickness of the formation. Red, blue, and green color blending is one of the most popular blending models in analyzing multiple seismic attributes. Geologists and geophysicist interpreters typically associate low-frequency components with a red color, medium-frequency components with a green color, and high-frequency components with a blue color for the thickness estimation of thin beds using frequency components. However, we found that the same (...)
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  5.  9
    Value of Nonstationary Wavelet Spectral Balancing in Mapping a Faulted Fluvial System, Bohai Gulf, China.Huailai Zhou, Yuanjun Wang, Tengfei Lin, Fangyu Li & Kurt J. Marfurt - 2015 - Interpretation: SEG 3 (3):SS1-SS13.
    Seismic data with enhanced resolution allow interpreters to effectively delineate and interpret architectural components of stratigraphically thin geologic features. We used a recently developed time-frequency domain deconvolution method to spectrally balance nonstationary seismic data. The method was based on polynomial fitting of seismic wavelet magnitude spectra. The deconvolution increased the spectral bandwidth but did not amplify random noise. We compared our new spectral modeling algorithm with existing time-variant spectral-whitening and inverse [Formula: see text]-filtering algorithms using a 3D offshore survey acquired (...)
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  6.  18
    Depositional Sequence Characterization Based on Seismic Variational Mode Decomposition.Fangyu Li, Bo Zhang, Rui Zhai, Huailai Zhou & Kurt J. Marfurt - 2017 - Interpretation: SEG 5 (2):SE97-SE106.
    Subtle variations in otherwise similar seismic data can be highlighted in specific spectral components. Our goal is to highlight repetitive sequence boundaries to help define the depositional environment, which in turn provides an interpretation framework. Variational mode decomposition is a novel data-driven signal decomposition method that provides several useful features compared with the commonly used time-frequency analysis. Rather than using predefined spectral bands, the VMD method adaptively decomposes a signal into an ensemble of band-limited intrinsic mode functions, each with its (...)
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  7.  1
    Multichannel Synchrosqueezing Generalized S-Transform for Time-Frequency Analysis of Seismic Traces.Nanke Wu, Huailai Zhou, Yuanjun Wang, Bo Zhang, Haitao Yan & Niu Cong - 2020 - Interpretation 8 (4):T793-T801.
    The synchrosqueezing generalized S-transform is commonly used to generate an isofrequency component of a signal by squeezing the decomposed frequency components of the signal. However, for seismic signals, the single-trace process can have a lack of lateral information in the squeezed results and lead to some discontinuous geologic information that will mislead the interpreter. Thus, to improve the stability of SSGST, we have developed a multichannel seismic trace squeezing method. Multichannel SSGST considers the decomposed frequency components of neighboring traces of (...)
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  8.  5
    Introduction to Special Section: Thin Beds.Hongliu Zeng, Kurt Marfurt, Sergey Fomel, Satinder Chopra, Gregory Partyka, Bradley Wallet, Michael Smith, Marcilio Matos, Huailai Zhou, Yihua Cai & Osareni Ogiesoba - 2015 - Interpretation: SEG 3 (3):SSi-SSii.
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    Introduction to Special Section: Seismic Time-Frequency Analysis.Bo Zhang, Wenkai Lu, Xiaohong Chen, Rui Zhang, Xiaotao Wen, Huailai Zhou & Danping Cao - 2017 - Interpretation: SEG 5 (1):SCi-SCi.
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