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Protista
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isotopes
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metal ores
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GeoRef Categories
Era and Period
Epoch and Age
Book Series
Date
Availability
pattern recognition
Estimation of CO 2 saturation maps from synthetic seismic data using a deep-learning method with a multi-scale approach Available to Purchase
Unsupervised classification applications in enhancing lithological mapping and geological understanding: a case study from Northern Ireland Available to Purchase
How to determine the optimal balance for geochemical pattern recognition and anomaly mapping based on compositional balance analysis Available to Purchase
Interpolation method based on pattern-feature correlation Available to Purchase
Adaptive multiple subtraction using a hybrid ℓ 2 - ℓ 1 pattern-learning approach Available to Purchase
Seismic facies analysis based on deep convolutional embedded clustering Available to Purchase
Mineral Exploration: Discovering and Defining Ore Deposits Available to Purchase
Wide-azimuth seismic and potential field data integration for salt recognition in the southern Gulf of Mexico Available to Purchase
Mo, Sn and W patterns in topsoils of the Campania Region, Italy Available to Purchase
Spatial pattern recognition of arsenic in topsoil using high-density regional data Available to Purchase
Unsupervised seismic facies analysis via deep convolutional autoencoders Available to Purchase
Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights Available to Purchase
Seismic Attributes as the Framework for Data Integration Throughout the Oilfield Life Cycle
After reading this short-course book, the seismic interpreter should be able to: use attributes to quantify geometric, dynamic, kinematic, statistical, and geomechanical properties of the 3D seismic data volume, use 3D visualization and multiattribute crossplots to interactively enhance and isolate geologic features that otherwise might be overlooked, use concepts of geomorphology, diagenesis, and tectonic deformation to integrate seismic and well-log data within an appropriate geologic framework, use classical statistics and modern machine learning to establish correlations between 3D seismic data, rock properties, and engineering data that then can be employed to predict future rates of penetration, well-completion success, and well-fluid production, and use seismic attributes computed from 3D seismic data as the framework for data integration throughout the lifespan of the oil field.