Diagenetic model of carbonate rocks of Guri Member of Mishan Formation (Lower to Middle Miocene) SE Zagros basin,Iran

In order to understand the post-depositional history of carbonate rocks of Guri Member (Lower to Middle Miocene), three stratigraphic sections were selected in north Bandar-Abbas in southeast of Iran. Sampling was carried out, analyzed for selective parameters such as oxygen and carbon isotopic compositions (δ¹⁸O and δ¹³C) and interpreted in the present study. We recognized several diagenetic processes including micritization, cementation, neomorphism, compaction, dissolution, silicification, dolomitization, fracturing and vein filling. Some of the diagenetic processes occurred at different conditions, so in order to achieve precise interpretation, samples from different carbonate components such as, micrite, fracture cement, solution pore cement, intergranular cement, and some biotic allochems were analyzed. In this study micrite samples were subdivided into two groups including micro-spary and micrite. They were recognized under Cathodoluminescence microscope. In addition, micrite samples were classified into five groups based on their depositional environments: supratidal, lagoon, coral bar, open sea, and open basin. There were minor changes in stable isotope ratios based on the sedimentary environments, stratigraphy successions, and micro-spary or micrite properties. In this study, similar calcite cements in petrography studies were differentiated by stable isotope data. Those calcite cements have formed in different diagenetic environments such as meteoric and burial cements. Paragenetic sequence of carbonate rocks were interpreted by integration of petrographic and isotopic studies. We have reconstructed diagenetic models of Guri Member into four stages including marine, meteoric, burial, and uplifting.     

Brittleness Effect on Rock Fatigue Damage Evolution

The damage evolution mechanism of rocks is one of the most important aspects in studying of rock fatigue behavior. Fatigue damage evolution of three rock types (onyx marble, sandstone and soft limestone) with different brittleness were considered in the present study. Intensive experimental tests were conducted on the chosen rock samples and acoustic emission (AE) sensors were used in some of them to monitor the fracturing process. Experimental tests indicated that brittleness strongly influences damage evolution of rocks in the course of static and dynamic loading. AE monitoring revealed that micro-crack density induced by the applied loads during different stages of the failure processes increases as rock brittleness increases. Also, results of fatigue tests on the three rock types indicated that the rock with the most induced micro-cracks during loading cycles has the least fatigue life. Furthermore, the condition of failure surfaces of the studied rocks samples, subjected to dynamic and static loading, were evaluated and it was concluded that the roughness of failure surfaces is influenced by loading types and rock brittleness. Dynamic failure surfaces were rougher than static ones and low brittle rock demonstrate a smoother failure surface compared to high brittle rock.     

Impact of climate change and urban development on extreme flows in the Grote Nete watershed,Belgium

This study investigates how extreme flows in the Grote Nete watershed located in the Flanders region of Belgium will respond to climate change and urban growth using the hydrological model WetSpa. Three climate change scenarios (low, mean and high), three urban development scenarios (low, medium and high) and the nine combined climate–urban change scenarios are considered. The results indicate that extreme low flows would decrease noticeably by climate change, while they would be less sensitive to urban development. On the other hand, extreme peak flows are predicted to increase considerably due to both climate change and urban growth. It is concluded that coupling the effects of land use change with climate change may lead to severe increase in the frequency river floods in winter as well as the frequency of extreme river low flows in summer.     

An alternative method for non-negative estimation of variance components

A typical problem of estimation principles of variance and covariance components is that they do not produce positive variances in general. This caveat is due, in particular, to a variety of reasons: (1) a badly chosen set of initial variance components, namely initial value problem (IVP), (2) low redundancy in functional model, (3) an improper stochastic model, and (4) data’s possibility of containing outliers. Accordingly, a lot of effort has been made in order to design non-negative estimates of variance components. However, the desires on non-negative and unbiased estimation can seldom be met simultaneously. Likewise, in order to search for a practical non-negative estimator, one has to give up the condition on unbiasedness, which implies that the estimator will be biased. On the other hand, unlike the variance components, the covariance components can be negative, so the methods for obtaining non-negative estimates of variance components are not applicable. This study presents an alternative method to non-negative estimation of variance components such that non-negativity of the variance components is automatically supported. The idea is based upon the use of the functions whose range is the set of all positive real numbers, namely positive-valued functions (PVFs), for unknown variance components in stochastic model instead of using variance components themselves. Using the PVF could eliminate the effect of IVP on the estimation process. This concept is reparameterized on the restricted maximum likelihood with no effect on the unbiasedness of the scheme. The numerical results show the successful estimation of non-negativity estimation of variance components (as positive values) as well as covariance components (as negative or positive values).     

Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images

Currently, methods of extracting spatial information from satellite images are mainly based on visual interpretations and drawing the consequences by human factor, which is both costly and time consuming. A large volume of data collected by satellite sensors, and significant improvement in spatial and spectral resolution of these images require the development of new methods for optimal use of these data in order to produce rapid economic and updating road maps. In this study, a new automatic method is proposed for road extraction by integrating the SVM and Level Set methods. The estimated probability of classification by SVM is used as input in Level Set Method. The average of completeness, correctness, and quality was 84.19, 88.69 and 76.06% respectively indicate high performance of proposed method for road extraction from Google Earth images.     

Generating non-stationary random fields of auto-correlated,normally distributed CPT profile by matrix decomposition method

This paper emphasises the true realisation of Cone Penetration Test (CPT) profiles considering non-stationary nature of the data. Formulation of stationary random field theory has been modified and adapted to non-stationary state in order to take into account the mean and variance variability for soil properties. Multi-variance correlation matrix along with the Cholesky decomposition technique was employed to produce realisations of non-homogenous and non-stationary random fields of CPT profiles. A piecewise and segmental data realisation according to the lithology and site class specifications acquired directly from CPT data is adopted in this study so as to render an accurate data simulation. For validation of proposed method 8 CPT test profiles collected from Urmia Lake site have been introduced and simulated by the stationary and non-stationary algorithms. The mean correlation coefficient between the actual CPT data profiles and related realisations along with some other important statistical parameters and their coefficients of variation strongly demonstrate that non-stationary random field generation technique gives quite better accuracy, by comparison to the conventional stationary random field generation scheme.