Petrology of Cr-rich Ophiolitic Chromitites of Bulqiza, Eastern Ophiolitic Belt, Albania

<正>The ultramafic massif of Bulqiza,which belongs to the eastern ophiolitic belt of Albania,is the most important area for metallurgical chromitite ores.The massif consists of a thick(4 km)rock sequence,with a generalized profile from the bottom to the top as follows.The tectonite     

An Overview of the Albanian Ophiolite and Related Ore Minerals

<正>1 A Short Introduction on the Albanian Ophiolite Albanian ophiolite(or Mirdita ophiolite)represents a remnant of the Tethyan oceanic lithosphere in the territory of Albania,as an elongated belt from south to the north,about 250 km long and 30-50 km wide.It covers an area of about 4300 km2 or 1/7 of the territory of Albania and is     

Site response assessment of an urban extension site using microtremor measurements,Ahud Rufeidah,Abha District,Southwest Saudi Arabia

Arabian Journal of Geosciences - Microtremor horizontal-to-vertical spectral ratio (HVSR) method has been conducted at 33 sites in Ahud Rufeidah urban expansion zone in order to assess the...     

Simultaneous perturbation stochastic approximation for tidal models

The Dutch continental shelf model (DCSM) is a shallow sea model of entire continental shelf which is used operationally in the Netherlands to forecast the storm surges in the North Sea. The forecasts are necessary to support the decision of the timely closure of the moveable storm surge barriers to protect the land. In this study, an automated model calibration method, simultaneous perturbation stochastic approximation (SPSA) is implemented for tidal calibration of the DCSM. The method uses objective function evaluations to obtain the gradient approximations. The gradient approximation for the central difference method uses only two objective function evaluation independent of the number of parameters being optimized. The calibration parameter in this study is the model bathymetry. A number of calibration experiments is performed. The effectiveness of the algorithm is evaluated in terms of the accuracy of the final results as well as the computational costs required to produce these results. In doing so, comparison is made with a traditional steepest descent method and also with a newly developed proper orthogonal decomposition-based calibration method. The main findings are: (1) The SPSA method gives comparable results to steepest descent method with little computational cost. (2) The SPSA method with little computational cost can be used to estimate large number of parameters.     

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.     

An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data

This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of Selangor located in the Peninsular Malaysia. The method comprised four components including image segmentation, Taguchi optimization, attribute selection using random forest, and rule-based feature extraction. Results indicated the robustness of the proposed approach as the area under curve of forest; grassland, old oil palm, rubber, urban tree, and young oil palm were calculated as 0.90, 0.89, 0.87, 0.87, 0.80, and 0.77, respectively. In addition, results showed that SAR data is very useful for extracting rubber and young oil palm trees (given by random forest importance values). Finally, further research is suggested to improve segmentation results and extract more features from the scene.     

Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

This study assesses the changes in surface area of Manzala Lake, the largest coastal lake in Egypt, with respect to changes in land use and land cover based on a multi-temporal classification process. A regression model is provided to predict the temporal changes in the different detected classes and to assess the sustainability of the lake waterbody. Remote sensing is an effective method for detecting the impact of anthropogenic activities on the surface area of a lagoon such as Manzala Lake. The techniques used in this study include unsupervised classification, Mahalanobis distance supervised classification, minimum distance supervised classification, maximum likelihood supervised classification, and normalized difference water index. Data extracted from satellite images are used to predict the future temporal change in each class, using a statistical regression model and considering calibration, validation, and prediction phases. It was found that the maximum likelihood classification technique has the highest overall accuracy of 93.33%. This technique is selected to observe the changes in the surface area of the lake for the period from 1984 to 2015. Study results show that the waterbody surface area of the lake declined by 46% and the area of floating vegetation, islands, and land agriculture increased by 153.52, 42.86, and 42.35% respectively during the study period. Linear regression model prediction indicates that the waterbody surface area of the lake will decrease by 25.24% during the period from 2015 to 2030, which reflects the negative impact of human activities on lake sustainability represented by a severe reduction of the waterbody area.     

Biomarker analysis of the upper Jurassic Naokelekan and Barsarin formations in the Miran Well-2, Miran oil field,Kurdistan region,Iraq

The Miran oilfield is one of the new oil fields in Kurdistan region, northern Iraq, located in the Sulaimani Governorate. Twelve Cuttings samples from the Upper Jurassic Naokelekan and Barsarin formations in well Miran-2 were selected for detailed organic geochemical investigations. All the samples were subjected to bitumen extraction in order to study any biomarkers present using gas chromatography-mass spectrometry. The dominance of low-molecular-weight n-alkanes and other calculated parameters indicate a marine source for the organic matter derived from planktonic algal and bacterial precursors deposited under anoxic conditions. The isoprenoids/n-alkanes ratios indicate type II and mixed II/III kerogen for both formations. The type II/III kerogen is characteristic of transitional environment under anoxic to dysoxic conditions as also indicated by the homohopane index for studied samples. More argillaceous carbonate rocks were deposited when reducing conditions were prevalent. Medium to high gammacerane index values in the rock extracts probably indicate a stratified water column during deposition of both formations. The studied samples from both formations have entered peak oil window maturity as reflected from the biomarker ratios from both aliphatic and aromatic fractions of the extracts.