Distribution of clay minerals in surface sediments of the Aegean Sea: a compilation

Extensive published data sets and some new data on the clay mineral composition of surface sediments in the Aegean Sea, northeastern Mediterranean Sea, have been compiled in order to map the distribution patterns of clay mineral assemblages and to decipher source areas and transport paths. We distinguished six provinces, a Northwest Aegean Province, a Marmara-Dardanelles Province, a West Turkey Province, a Southeast Aegean Province, a Kithira Province and a Central Aegean Province. The clay mineral assemblages in the coastal and shelf areas carry the signature of the riverine sediment discharge from southeast Europe and Turkey, respectively. The southern Aegean Sea is probably influenced by the River Nile discharge and transport of clay minerals by surface currents. The clay minerals in the central Aegean Sea form a mixed assemblage comprising components of the other assemblages. A dispersion and dilution of clay minerals by surface currents is obvious.     

Earthquake early warning for transport lines

This paper analyzes the potential of earthquake early-warning systems for transport lines. The interdisciplinary work focuses on rapidly producing an alert map during an ongoing earthquake as well as providing a damage map immediately after the strong-motion phase that visualizes potential damages to the railway infrastructure. In order to meet these application requirements, a service-oriented architecture based on geospatial standards is specified. This ensures the portability of the system architecture to different geographic regions as well as a potential transfer to other natural disasters and infrastructure systems. The first part of the paper describes the standard-based services of the system architecture together with design principles that are useful for the realization of early-warning systems. In the second part of the paper, an online demonstrator for the exemplary test area in the federal state of Baden-Württemberg, Germany, is presented. The system architecture of the demonstrator includes an earthquake early-warning methodology based on artificial neural networks and an infrastructure-specific damage assessment. The third part of the paper analyzes the potential of implementing low-cost sensors in the track, which would provide a dense network directly at the railway infrastructure.