Hydrothermal plumes are ongoing venting of hot solutions, on a time scale of months to years, relating to volcanic activities on the seafloor. Recent developments in acoustical observational techniques have produced images to support the scientific investigation of such plumes. However, understanding the complex behavior of plumes in a long-time series poses a challenge to the existing analysis approaches. The motivation of this work is to use visualization techniques to facilitate the visual exploration and analysis of plumes and to help domain scientists compare the actual behavior of plumes predicted by tidal interaction models and buoyant plume models incorporating forced entrainment effects. Methods of geovisualization are combined with time-varying feature-based techniques to create visualizations of plumes which are applied to an acoustic imaging dataset collected using the Cabled Observatory Vent Imaging Sonar in the Northeast Pacific. The results give new insights to the data and confirm the hypothesis of plumes.
Few tools for career exploration utilize visualizations despite their potential to help students understand the intangible relationships between jobs and majors. Our application-driven design combines the intuitiveness of node-link diagrams and the scalability of aggregation-based techniques to combine an overview of a job database with the option for individualized exploration.
Career Choice , Computer Graphics , Employment , Students , Vocational Guidance/methods , Adult , Female , Humans , Male , Universities , Young Adult
The relative importance of suspended particles and turbulence as backscattering mechanisms within a hydrothermal plume located on the Endeavour Segment of the Juan de Fuca Ridge is determined by comparing acoustic backscatter measured by the Cabled Observatory Vent Imaging Sonar (COVIS) with model calculations based on in situ samples of particles suspended within the plume. Analysis of plume samples yields estimates of the mass concentration and size distribution of particles, which are used to quantify their contribution to acoustic backscatter. The result shows negligible effects of plume particles on acoustic backscatter within the initial 10-m rise of the plume. This suggests turbulence-induced temperature fluctuations are the dominant backscattering mechanism within lower levels of the plume. Furthermore, inversion of the observed acoustic backscatter for the standard deviation of temperature within the plume yields a reasonable match with the in situ temperature measurements made by a conductivity-temperature-depth instrument. This finding shows that turbulence-induced temperature fluctuations are the dominant backscattering mechanism and demonstrates the potential of using acoustic backscatter as a remote-sensing tool to measure the temperature variability within a hydrothermal plume.
For large-scale simulations, the data sets are so massive that it is sometimes not feasible to view the data with basic visualization methods, let alone explore all time steps in detail. Automated tools are necessary for knowledge discovery, i.e., to help sift through the data and isolate specific time steps that can then be further explored. Scientists study patterns and interactions and want to know when and where interesting things happen. Activity detection, the detection of specific interactions of objects which span a limited duration of time, has been an active research area in the computer vision community. In this paper, we introduce activity detection to scientific simulations and show how it can be utilized in scientific visualization. We show how activity detection allows a scientist to model an activity and can then validate their hypothesis on the underlying processes. Three case studies are presented.
