The Pairwise Similarity Partitioning Algorithm: A Method for Unsupervised Partitioning of Geoscientific and Other Datasets Using Arbitrary Similarity Metrics
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Abstract not provided.
Abstract not provided
Presentation given at the 98th AMS Annual meeting on January 8, 2018. Information presented includes the motivation, model configuration, observation info, statistics, summary and conclusion.
Principal component analysis of spectral decomposition results combined with amplitude and frequency seismic attributes derived from 3D seismic data are used for the identification and characterization of gas hydrate deposits in the deepwater eastern Gulf of Mexico. In the central deepwater Gulf of Mexico (GoM), logging while drilling LWD data provided insight to the amplitude response of gas hydrate saturation in sands, which could be used to characterize complex gas hydrate deposits in other sandy deposits. In this study, a large 3D seismic data set from equivalent and distal Plio Pleistocene sandy channel deposits in the deepwater eastern Gulf of Mexico is screened for direct hydrocarbon indicators for gas hydrate saturated sands.
The presence of a gas hydrate petroleum system and seismic attributes derived from 3D seismic data are used for the identification and characterization of gas hydrate deposits in the deepwater eastern Gulf of Mexico. In the central deepwater Gulf of Mexico (GoM), logging while drilling (LWD) data provided insight to the amplitude response of gas hydrate saturation in sands, which could be used to characterize complex gas hydrate deposits in other sandy deposits. In this study, a large 3D seismic data set from equivalent and distal Plio-Pleistocene sandy channel deposits in the deepwater eastern Gulf of Mexico is screened for direct hydrocarbon indicators for gas hydrate saturated sands.
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Abstract not provided.
Abstract not provided.
Abstract not provided.
Abstract—Virtual Reality is a budding new medium of interaction, but ways in which to use it for data analytics are not immediately obvious. Through our research, we investigated new and useful ways to view multidimensional data in a virtual environment. In this paper, we present a virtual laboratory with collaborative visualizations in different spaces. We focused on geographic view in the center of the room by utilizing spatial freedom in Virtual Reality, which informed both the geographic visualization and time series data. Through this laboratory and the collaborative visualizations housed within, we believe we’ve found a novel way to interpret data in Virtual Reality.
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