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  1.  9
    Pitfalls and Implementation of Data Conditioning, Attribute Analysis, and Self-Organizing Maps to 2D Data: Application to the Exmouth Plateau, North Carnarvon Basin, Australia.Thang N. Ha, Kurt J. Marfurt, Bradley C. Wallet & Bryce Hutchinson - 2019 - Interpretation 7 (3):SG23-SG42.
    Recent developments in attribute analysis and machine learning have significantly enhanced interpretation workflows of 3D seismic surveys. Nevertheless, even in 2018, many sedimentary basins are only covered by grids of 2D seismic lines. These 2D surveys are suitable for regional feature mapping and often identify targets in areas not covered by 3D surveys. With continuing pressure to cut costs in the hydrocarbon industry, it is crucial to extract as much information as possible from these 2D surveys. Unfortunately, much if not (...)
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  2.  6
    Unsupervised Seismic Facies Using Gaussian Mixture Models.Bradley C. Wallet & Robert Hardisty - 2019 - Interpretation 7 (3):SE93-SE111.
    As the use of seismic attributes becomes more widespread, multivariate seismic analysis has become more commonplace for seismic facies analysis. Unsupervised machine-learning techniques provide methods of automatically finding patterns in data with minimal user interaction. When using unsupervised machine-learning techniques, such as [Formula: see text]-means or Kohonen self-organizing maps, the number of clusters can often be ambiguously defined and there is no measure of how confident the algorithm is in the classification of data vectors. The model-based probabilistic formulation of Gaussian (...)
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  3. Attribute Expression of Channel Forms in a Hybrid Carbonate Turbidite Formation.Bradley C. Wallet - 2016 - Interpretation: SEG 4 (2):SE75-SE86.
    Much of the world’s conventional oil and gas production comes from turbidite systems. Interpreting them in three dimensions using commercially available software generally requires seismic attributes. Hybrid carbonate turbidite systems are an interesting phenomenon that is not fully understood. I have examined the attribute expression of channel forms in a hybrid carbonate turbidite system from off the coast of Western Australia. I have determined several characteristic responses to attributes that improve the ability to identify and delineate the channel forms. Finally, (...)
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  4.  5
    Using the Image Grand Tour to Visualize Fluvial Deltaic Architectural Elements in South Texas, USA.Bradley C. Wallet - 2013 - Interpretation: SEG 1 (1):SA117-SA129.
    Spectral decomposition can produce dozens of attributes for a single data set, far exceeding the ability for direct visualization. Some solutions have been proposed. The state-of-the-art approach is via the use of principal component analysis. However, this approach has significant inherent weaknesses, such as a lack of inclusion of spatial information and a tendency to inflate noise. Previous work has shown the ability of the image grand tour to construct lower-dimensional views of spectral information resulting in multiple images showing distinct (...)
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  5.  1
    An in-Depth Analysis of Logarithmic Data Transformation and Per-Class Normalization in Machine Learning: Application to Unsupervised Classification of a Turbidite System in the Canterbury Basin, New Zealand, and Supervised Classification of Salt in the Eugene Island Minibasin, Gulf of Mexico.Thang N. Ha, David Lubo-Robles, Kurt J. Marfurt & Bradley C. Wallet - 2021 - Interpretation 9 (3):T685-T710.
    In a machine-learning workflow, data normalization is a crucial step that compensates for the large variation in data ranges and averages associated with different types of input measured with different units. However, most machine-learning implementations do not provide data normalization beyond the z-score algorithm, which subtracts the mean from the distribution and then scales the result by dividing by the standard deviation. Although the z-score converts data with Gaussian behavior to have the same shape and size, many of our seismic (...)
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  6. Fluid Discrimination Using Detrended Seismic Impedance.Xavier E. Refunjol, Bradley C. Wallet & John P. Castagna - 2022 - Interpretation 10 (1):SA15-SA24.
    Compaction effects can obscure the impedance separation between hydrocarbon-bearing and fully brine-saturated sandstones. We have improved their discrimination by removing depth-related trends from inverted seismic impedance. Although the ratio of compressional- to shear-wave velocity versus seismic compressional-wave impedance crossplots shows differences among pay, brine sand, and shale trends, using absolute inverted impedances only imperfectly distinguishes hydrocarbon sands from brine sands due to outliers. In a given locality, statistical comparison of well log and seismic-derived impedances enables us to obtain a shale (...)
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  7.  3
    A Deep-Learning Method for Latent Space Analysis of Multiple Seismic Attributes.Bradley C. Wallet & Thang N. Ha - 2021 - Interpretation 9 (3):T945-T954.
    Seismic attributes are a well-established method for highlighting subtle features in seismic data to improve interpretability and suitability for quantitative analysis. Seismic attributes are an enabling technology in such areas as thin-bed analysis, geobody extraction, and seismic geomorphology. Seismic attributes are mathematical functions of the data that are designed to exploit geologic and/or geophysical principles to provide meaningful information about underlying processes. Seismic attributes often suffer from an “abundance of riches” because the high dimensionality of seismic attributes may cause great (...)
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  8.  2
    Integrating Phase Into the Visualization of Spectral Decomposition Attributes.Bradley C. Wallet & Oswaldo Davogustto - 2015 - Interpretation: SEG 3 (3):SS73-SS86.
    Much of the world’s conventional oil and gas production comes from fluvial-deltaic reservoirs. The ability to accurately interpret the architectural elements comprising these systems greatly reduces the risk in exploration and development in these environments. We have evaluated methods for using spectral decomposition attributes to improve the visualization in fluvial-deltaic environments using data from the Middle Pennsylvanian age Red Fork Formation of Oklahoma. We determined how spectral phase and magnitude attributes can be effectively combined using an hue-saturation-value color map to (...)
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