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.     

Hydrogeochemical assessment of groundwater in Kashmir Valley,India

Groundwater samples (n = 163) were collected across Kashmir Valley in 2010 to assess the hydrogeochemistry of the groundwater in shallow and deep aquifers and its suitability for domestic, agriculture, horticulture, and livestock purposes. The groundwater is generally alkaline in nature. The electrical conductivity (EC) which is an index to represent the total concentration of soluble salts in water was used to measure the salinity hazard to crops as it reflects the TDS in groundwater ranging from 97 to 1385 μS/cm, except one well in Sopore. The average concentration of major ions was higher in shallow aquifers than in deeper aquifers. In general, Ca²⁺ is the dominant cation and HCO \(_{3}^{-}\) the dominant anion. Ca–HCO₃, Mg–HCO₃, Ca–Mg–HCO₃, Na–HCO₃ were the dominant hydrogeochemical facies. High concentration of HCO₃ and pH less than 8.8 clearly indicated that intense chemical weathering processes have taken place in the study area. The groundwater flow pattern in the area follows the local surface topography which not only modifies the hydrogeochemical facies but also controls their distribution. The groundwater in valley flows into four directions, i.e., SW–NE, NE–W, SE-NW and SE–NE directions. The results suggest that carbonate dissolution is the dominant source of major ions followed by silicate weathering and ion-exchange processes. The concentrations of all the major ions determined in the present study are within the permissible limits of WHO and BIS standards. The results of Total Hardness, SAR, Na%, Kelly Index, USDA classification, Magnesium absorption ratio, residual sodium carbonate, and PI suggested that groundwater is good for drinking, livestock, and irrigation purposes.     

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.     

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).     

Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.     

Markov CA,Multi Regression,and Multiple Decision Making for Modeling Historical Changes in Kirkuk City,Iraq

The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R²) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency     

Gravitational clustering of galaxies in an expanding universe

We inquire the phenomena of clustering of galaxies in an expanding universe from a theoretical point of view on the basis of thermodynamics and correlation functions. The partial differential equation is developed both for the point mass and extended mass structures of a two-point correlation function by using thermodynamic equations in combination with the equation of state taking gravitational interaction between particles into consideration. The unique solution physically satisfies a set of boundary conditions for correlated systems and provides a new insight into the gravitational clustering problem.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

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.