Susceptibility and time-dependent magnetization of basalts at DSDP Hole 76-534A

Basalts from Hole 534A are among the oldest recovered from the ocean bottom, dating from the opening of the Atlantic 155 Ma. Upon exposure to a 1-Oe field for one week, these basalts acquire a viscous remanent magnetization (VRM), which ranges from 4 to 223% of their natural remanent magnetization (NRM). A magnetic field of similar magnitude is observed in the paleomagnetic lab of the Glomar Challenger, and it is therefore doubtful if accurate measurements of magnetic moment in such rocks can be made on board unless the paleomagnetic area is magnetically shielded. No correlation is observed between the Konigsberger ratio (beta), which is usually less than 3, and the ability to acquire a VRM. The VRM shows both a log t dependence and a Richter aftereffect. Both of these, but especially the log t dependence, will cause the susceptibility measurements (made by applying a magnetic field for a very short time) to be minimum values. The susceptibility and derived Q should therefore be used cautiously for magnetic anomaly interpretation, because they can cause the importance of the induced magnetization to be underestimated.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.