Pore pressure measurements on IODP Exp308 sites

Overpressures measured with pore pressure penetrometers during Integrated Ocean Drilling Program (IODP) Expedition 308 reach 70% and 60% of the hydrostatic effective stress (View the MathML source) in the first 200 meters below sea floor (mbsf) at Sites U1322 and U1324, respectively, in the deepwater Gulf of Mexico, offshore Louisiana. High overpressures are present within low permeability mudstones where there have been multiple, very large, submarine landslides during the Pleistocene. Beneath 200 mbsf at Site U1324, pore pressures drop significantly: there are no submarine landslides in this mixture of mudstone, siltstone, and sandstone. The penetrometer measurements did not reach the in situ pressure at the end of the deployment. We used a soil model to determine that an extrapolation approach based on the inverse of square route of time (View the MathML source) requires much less decay time to achieve a desirable accuracy than an inverse time (1/t) extrapolation. Expedition 308 examined how rapid and asymmetric sedimentation above a permeable aquifer drives lateral fluid flow, extreme pore pressures, and submarine landslides. We interpret that the high overpressures observed are driven by rapid sedimentation of low permeability material from the ancestral Mississippi River. Reduced overpressure at depth at Site U1324 suggests lateral flow (drainage) whereas high overpressure at Site U1322 requires inflow from below: lateral flow in the underlying permeable aquifer provides one mechanism for these observations. High overpressure near the seafloor reduces slope stability and provides a mechanism for the large submarine landslides and low regional gradient (2°) offshore from the Mississippi delta.

30 years of synoptic observations from Neumayer Station with links to datasets

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.