Anelastic strain recovery and elastic properties of ODP Leg 123 oceanic basalt samples

A knowledge of rock stress is fundamental for improving our understanding of oceanic crustal mechanisms and lithospheric dynamic processes. However, direct measurements of stress in the deep oceans, and in particular stress magnitudes, have proved to be technically difficult. Anelastic strain recovery measurements were conducted on 15 basalt core samples from Sites 765 and 766 during Leg 123. Three sets of experiments were performed: anelastic strain recovery monitoring, dynamic elastic property measurements, and thermal azimuthal anisotropy observations. In addition, a range of other tests and observations were recorded to characterize each of the samples. One common feature of the experimental results and observations is that apparently no consistent orientation trend exists, either between the different measurements on each core sample or between the same sets of measurements on the various core samples. However, some evidence of correspondence between velocity anisotropy and anelastic strain recovery exists, but this is not consistent for all the core samples investigated. Thermal azimuthal anisotropy observations, although showing no conclusive correlations with the other results, were of significant interest in that they clearly exhibited anisotropic behavior. The apparent reproducibility of this behavior may point toward the possibility of rocks that retain a "memory" of their stress history, which could be exploited to derive stress orientations from archived core. Anelastic strain recovery is a relatively new technique. Because use of the method has extended to a wider range of rock types, the literature has begun to include examples of rocks that contracted with time. Strong circumstantial evidence exists to suggest that core-sample contractions result from the slow diffusion of pore fluids from a preexisting microcrack structure that permits the rock to deflate at a greater rate than the expansion caused by anelastic strain recovery. Both expansions and contractions of the Leg 123 cores were observed. The basalt cores have clearly been intersected by an abundance of preexisting fractures, some of which pass right through the samples, but many are intercepted or terminate within the rock matrix. Thus, the behavior of the core samples will be influenced not only by the properties of the rock matrix between the fractures, but also by how these macro- and micro-scale fractures mutually interact. The strain-recovery curves recorded during Leg 123 for each of the 15 basalt core samples may reflect the result of two competing time dependent processes: anelastic strain recovery and pore pressure recovery. Were these the only two processes to influence the gauge responses, then one might expect that given the additional information required, established theoretical models might be used to determine consistent stress orientations and reliable stress magnitudes. However, superimposed upon these competing processes is their respective interaction with the preexisting fractures that intersect each core. Evidence from our experiments and observations suggests that these fractures have a dominating influence on the characteristics of the recovery curves and that their effects are complex.

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.