Kinematics of the eastern Caucasus near Baku, Azerbaijan

The potential for large, shallow earthquakes and their associated seismic hazard in the eastern Caucasus, an area of dense population and sensitive industrial infrastructure, remains speculative based on historical precedent and current geologic and seismologic observations. Here we present updated and expanded results from a GPS network between the northern edge of the Lesser Caucasus and Greater Caucasus, providing geodetic constraints to the problem. A significant strain rate is observed in a profile over a distance of about 150?km across the Kura Basin. We attribute this to inter-seismic strain accumulation on buried fault structures and present simple elastic dislocation models for their plausible geometry and slip rate based on the known geology, seismicity and the GPS velocities. Due to the close proximity of the strain anomaly to Baku, further observations are needed to determine whether observed contraction is due to inter-seismically locked faults and, if so, implications for the seismic hazard in the region.     

Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.     

The negative effect of environmental geological conditions of some geo-archaeological sites of North Coast and Alexandria

Three geo-archaeological sites at the North Coast and Alexandria, namely, the Alexandria wall (El Shalalat Park site), Abu Soir temple, and Marina excavations, were investigated to determine the negative impact of a salty environmental condition. The monuments suffer from rock decay of different rates. The geo-archaeological sites were built mainly from oolitic limestone blocks (i.e., the Alexandria wall at the El Shalalat Park site and Abu Soir temple) or excavated on them (i.e., Marina excavations). Field inspection and a lab analysis were carried out to understand the weathering mechanism. Salt weathering criteria such as disintegration, pitting, scaling, exfoliation, and honeycomb are observed on the Alexandria wall and upper parts of the Abu Soir temple, while dangerous cracks are detected on the Marina excavation tombs. The petrographic study of the oolitic limestone samples shows that they consist mainly of oolities and drusy sparite as a cement (oolitic grainstone). Some oolities have quartz grains as nuclei. Hydrochemical analysis shows that the total dissolved salts of extracted solutions of the North Coast quarry samples range from 539 to 686 ppm and dramatically increase (i.e., ten times) for extracted solutions from monument samples, ranging from 5395 to 6880 ppm. The dominant cation is sodium while the dominant anion is chlorine. Acid insoluble residue analysis shows that the carbonate content ranges from 89.2% to 96.4% for fresh samples from quarries and from 9.2% to 94.8% for weathered monument samples. The weight loss of the quarry oolitic limestone samples range from 30.7% to 32.7% and its physical and mechanical properties become worse after being subjected to 15 cycles of a durability simulation soundness test (using a sodium sulphate solution). Our main recommendations are to use suitable grouting for binding the cracks, high durability reconstruction rocks, and suitable cleaning methods to remove salts from the monuments.     

Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes

Increasing demand on infrastructures increases attention to shallow soft ground tunneling methods in urbanized areas. Especially in metro tunnel excavations, due to their large diameters, it is important to control the surface settlements observed before and after excavation, which may cause damage to surface structures. In order to solve this problem, earth pressure balance machines (EPBM) and slurry balance machines have been widely used throughout the world. There are numerous empirical, analytical, and numerical analysis methods that can be used to predict surface settlements. But substantially fewer approaches have been developed for artificial neural network-based prediction methods especially in EPBM tunneling. In this study, 18 different parameters have been collected by municipal authorities from field studies pertaining to EPBM operation factors, tunnel geometric properties, and ground properties. The data source has a preprocess phase for the selection of the most effective parameters for surface settlement prediction. This paper focuses on surface settlement prediction using three different methods: artificial neural network (ANN), support vector machines (SVM), and Gaussian processes (GP). The success of the study has decreased the error rate to 13, 12.8, and 9, respectively, which is relatively better than contemporary research.     

The Zaghawa and the Midob of North Darfur – A comparison of migration behaviour

Two ethnic groups of North Darfur, the Zaghawa and the Midob, are being compared as regards their migration behaviour and their identification in their destination areas. Both groups have severely suffered as a result of the drought phase of the last three decades, during which more than half of the Zaghawa and Midob have migrated southwards and to the towns. Despite the similarity of the two ethnic groups, being non-Arab agropastoralists who are socially and politically marginalised both within Darfur and within the Sudan, they show marked differences in their migration behaviour and ethnic solidarity. The Zaghawa migrants are considerably more economically successful than the Midob migrants. The main reasons are: – The Zaghawa started their outmigration earlier than the Midob. Thus, they could establish ethnically-based networks in Libya and the Gulf countries, to which Zaghawa migrants can resort to make a good start in their destination areas. By the time the Midob started their migration in the 1980s, the conditions of employment in the Arab oil countries had worsened. – The Zaghawa show a strong clan solidarity. They readily lend money to young members of their clan to cover the high costs of travelling to the Arab oil countries for work, where many of them have already established themselves. – The Midob, both in the Sudan and abroad, have much less resources available. The only country open for them outside the Sudan is Libya. Lacking capital, the Midob go there on camels, mostly illegally. Having no connections in Libya, except those to other poor Midob there, they usually try to work in herding sheep and goats, which is not a lucrative profession. This revised version was published online in July 2006 with corrections to the Cover Date.     

Discovery and Significance of Diamonds and Moissanites in Chromitite within the Skenderbeu Massif of the Mirdita Zone Ophiolite, West Albania

In recent years diamonds and other unusual minerals(carbides,nitrides,metal alloys and native elements) have been recovered from mantle peridotites and chromitites(both high-Cr chromitites and high-Al chromitites) from a number of ophiolites of different ages and tectonic settings.Here we report a similar assemblage of minerals from the Skenderbeu massif of the Mirdita zone ophiolite,west Albania.So far,more than 20 grains of microdiamonds and 30 grains of moissanites(SiC) have been separated from the podiform chromitite.The diamonds are mostly light yellow,transparent,euhedral crystals,200~300 μm across,with a range of morphologies;some are octahedral and cuboctahedron and others are elongate and irregular.Secondary electron images show that some grains have well-developed striatums.All the diamond grains have been analyzed and yielded typical Raman spectra with a shift at ~1325 cm~(-1).The moissanite grains recovered from the Skenderbeu chromitites are mainly light blue to dark blue,but some are yellow to light yeUow.All the analyzed grains have typical Raman spectra with shifts at 766 cm~(-1),787 cm~(-1),and 967 cm~(-1).The energy spectrums of the moissanites confirm that the grains are composed entirely of silicon and carbon.This investigation expands the occurrence of diamonds and moissanites to Mesozoic ophiolites in the Neo-Tethys.Our new findings suggest that diamonds and moissanites are present,and probably ubiquitous in the oceanic mantle and can provide new perspectives and avenues for research on the origin of ophiolites and podiform chromitites.     

Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study

A rainfall‐runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall‐runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km²) in the Nile basin is selected here as a case study. The catchment is classified into seven subcatchments, and the mean areal precipitation over those subcatchments is computed as a main input to the ANN model. The available daily data (1992–99) are divided into two sets for model calibration (1992–96) and for validation (1997–99). The results of the ANN model are compared with one of physical distributed rainfall‐runoff models that apply hydraulic and hydrologic fundamental equations in a grid base. The results over the case study area and the comparative analysis with the physically based distributed model show that the ANN technique has great potential in simulating the rainfall‐runoff process adequately. Because the available record used in the calibration of the ANN model is too short, the ANN model is biased compared with the distributed model, especially for high flows. Copyright © 2005 John Wiley & Sons, Ltd.