Physical properties, grain size distribution and clay mineralogy of ODP Leg 117 sites

The fabric of sediments recovered at sites drilled on the Indus Fan, Owen Ridge, and Oman margin during Ocean Drilling Program Leg 117 was examined by scanning electron microscopy to document changes that accompany sediment burial. Two sediment types were studied: (1) biogenic sediments consisting of a variety of marly nannofossil and nannofossil oozes and chalks and (2) terrigenous sediments consisting of fine-grained turbidites deposited in association with the Indus Fan. Biogenic sediments were examined with samples from the seafloor to depths of 306 m below seafloor (mbsf) on the Owen Ridge (Site 722) and 368 mbsf on the Oman margin (Sites 723 and 728). Over these depth ranges the biogenic sediments are characterized by a random arrangement of microfossils and display little chemical diagenetic alteration. The microfossils are dispersed within a fine-grained matrix that is predominantly microcrystalline carbonate particles on the Owen Ridge and clay and organic matter on the Oman margin. Sediments with abundant siliceous microfossils display distinct, open fabrics with high porosity. Porosity reduction resulting from gravitational compaction appears to be the primary process affecting fabric change in the biogenic sediment sections. Fabric of illite-rich clayey silts and silty claystones from the Indus Fan (Site 720) and Owen Ridge (Sites 722 and 731) was examined for a composite section extending from 45 to 985 mbsf. In this section fabric of the fine-grained turbidites changes from one with small flocculated clay domains, random particle arrangement, and high porosity to a fabric with larger domains, strong preferred particle orientation roughly parallel to bedding, and lower porosity. These changes are accomplished by a growth in domain size, primarily through increasing face-to-face contacts, and by particle reorientation which is characterized by a sharp increase in alignment with bedding between 200 and 400 mbsf. Despite extensive particle reorientation, flocculated clay fabric persists in the deepest samples examined, particularly adjacent to silt grains, and the sediments lack fissility. Fabric changes over the 45-985 mbsf interval occur in response to gravitational compaction. Porosity reduction and development of preferred particle orientation in the Indus Fan and Owen Ridge sections occur at greater depths than outlined in previous fabric models for terrigenous sediments as a consequence of a greater abundance of silt and a greater abundance of illite and chlorite clays.

Results of analysis and model simulation of the bed profiles

Bedforms such as dunes and ripples are ubiquitous in rivers and coastal seas, and commonly described as triangular shapes from which height and length are calculated to estimate hydrodynamic and sediment dynamic parameters. Natural bedforms, however, present a far more complicated morphology; the difference between natural bedform shape and the often assumed triangular shape is usually neglected, and how this may affect the flow is unknown. This study investigates the shapes of natural bedforms and how they influence flow and shear stress, based on four datasets extracted from earlier studies on two rivers (the Rio Paraná in Argentina, and the Lower Rhine in The Netherlands). The most commonly occurring morphological elements are a sinusoidal stoss side made of one segment and a lee side made of two segments, a gently sloping upper lee side and a relatively steep (6 to 21°) slip face. A non-hydrostatic numerical model, set up using Delft3D, served to simulate the flow over fixed bedforms with various morphologies derived from the identified morphological elements. Both shear stress and turbulence increase with increasing slip face angle and are only marginally affected by the dimensions and positions of the upper and lower lee side. The average slip face angle determined from the bed profiles is 14°, over which there is no permanent flow separation. Shear stress and turbulence above natural bedforms are higher than above a flat bed but much lower than over the often assumed 30° lee side angle.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.