TLDR Lubo et al 2023: PCA for Seismic Attribute Selection and SOMs in the Presence of Gas Hydrates
Автор: AASPI
Загружено: 2024-11-16
Просмотров: 155
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In this video, we explore the integration of Principal Component Analysis (PCA) and Self-Organizing Maps (SOM) for mapping gas hydrates and bottom-simulating reflectors (BSRs) in seismic data. Using seismic attributes such as coherence, total energy, and GLCM entropy, we demonstrate how PCA optimizes attribute selection for SOM-based seismic facies classification. By testing different training data configurations, we identify the most effective combination of attributes for robust BSR detection, particularly in challenging settings like the Blake Ridge offshore South Carolina. This approach offers insights into how machine learning can improve the identification of gas hydrates, overcoming common challenges like reflector confusion with surrounding geology.
paper link: https://www.sciencedirect.com/science...
#GasHydrates #SeismicFacies #MachineLearning #SelfOrganizingMaps #PrincipalComponentAnalysis #SeismicAttributes #GasHydrateStabilityZone #BottomSimulatingReflector #SeismicInterpretation #HydrocarbonExploration #GeophysicalAnalysis #PCA #SeismicData
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