Chemical composition of igneous rocks and origin of the sill and pillow-basalt complex at DSDP Site 61-462

The sill and pillow complex cored on Deep Sea Drilling Project Leg 61 (Site 462) is divided into two groups, A and B types, on the basis of chemical composition and volcanostratigraphy. The A-type basalt is characterized by a higher FeO*/MgO ratio and abundant TiO2, whereas the B-type basalt is characterized by a lower FeO*/MgO ratio and scarcity of TiO2. The A type is composed of sills interbedded with hyaloclastic sediments, and the B type consists of basalt sills and pillow basalt with minor amounts of sediment. However, the structure of pillow basalts in the B type is atypical; they might be eruptive. From paleontological study of the interbedded sediments and radiometric age determination of the basalt, the volcanic event of A type is assumed to be Cenomanian to Aptian, and that of B type somewhat older. The oceanic crust in the Nauru Basin was assumed to be Oxfordian, based on the Mesozoic magnetic anomaly. Consequently, two events of intraplate volcanism are recognized. It is thus assumed that the sill-pillow complex did not come from a normal oceanic ridge, and that normal oceanic basement could therefore underlie the complex. The Site 462 basalts are quartz-normative, and strongly hypersthene-normative, and have a higher FeO*/MgO ratio and lower TiO2 content. Olivine from the Nauru Basin basalts has a lower Mg/(Mg + Fe**2+) ratio (0.83-0.84) and coexists with spinel of lower Mg/(Mg + Fe**2+) ratio when compared to olivine-spinel pairs from mid-ocean ridge (MAR) basalt. The glass of spinel-bearing basalts has a higher FeO*/(FeO* + MgO) ratio (0.58-0.60) than that of MAR (<0.575). Therefore, the Nauru Basin basalts are chemically and mineralogically distinct from ocean-ridge tholeiite. That the Nauru Basin basalts are quartz-normative and strongly hypersthene-normative and have a lower TiO2 content suggests that the basaltic liquids of Site 462 were generated at shallower depths (<5 kbar) than ocean-ridge tholeiite: Site 462 basalts are similar to basalts from the Manihiki Plateau and the Ontong-Java Plateau, but different from Hawaiian tholeiite of hot-spot type, with lower K2O and TiO2 content. We propose a new type of basalt, ocean-plateau tholeiite, a product of intraplate volcanism.

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.