Geochemical constraints on the provenance of Oligocene–Miocene siliciclastic deposits (Zivah Formation) of NW Iran: implications for the tectonic evolution of the Caucasus

In this study, the whole-rock geochemistry of 35 Oligocene–Miocene sandstone and shale samples from the Zivah Formation, Moghan area (NW Iran) were collected and analyzed for evaluation of their provenance, tectonic setting and the intensity of paleo-weathering. Low to moderate values of the chemical index of alteration (mean CIA?=?53/68 for sandstones/shales) and relatively high values of index of compositional variability (mean ICV?=?1.23/1.08 for sandstones/shales) suggest weak chemical weathering and an immature source. These results support for the semi-arid and semi-humid paleoclimate conditions in the source area. The geochemistry results reveal that the sediments were deposited in a basin related to the island arc and active continental margin tectonic settings, probably indicating the time of initial collision between Arabia and Eurasia. The enrichment of Cr, Ni and V in the sandstone and shales are consistent with mafic input from the source area. However, La/Th vs. Hf and La/Sc vs. Co/Th plots reveal mixed source of felsic and intermediate volcanic rocks. The data indicate that the sediments most likely originated from a mixture of mafic, intermediate and felsic igneous source areas, possibly as the erosional products of localized topography of the Talysh and the Lesser Caucasus mountains (south to southwest), created by compression in the Moghan region during the syn-collisional development of the Caucasus.     

Rayleigh–Taylor instability in an ionized medium

We study the linear theory of the magnetized Rayleigh–Taylor instability in a system consisting of ions and neutrals. Both components are affected by a uniform vertical gravitational field. We consider ions and neutrals as two separate fluid systems that can exchange momentum through collisions. However, ions have a direct interaction with the magnetic field lines but neutrals are not affected by the field directly. The equations of our two-fluid model are linearized and by applying a set of proper boundary conditions, a general dispersion relation is derived for our two superposed fluids separated by a horizontal boundary. We found two unstable modes for a range of wavenumbers. It seems that one of the unstable modes corresponds to the ions and the other one is for the neutrals. Both modes are reduced with increasing particle collision rate and ionization fraction. We show that if the two-fluid nature is considered, the RT instability would not be suppressed and we also show that the growth time of the perturbations increases. As an example, we apply our analysis to the Local Clouds which seem to have arisen because of the RT instability. Assuming that the clouds are partially ionized, we find that the growth rate of these clouds increases in comparison to the fully ionized case.     

Sea level in the Mediterranean Sea: seasonal adjustment and trend extraction within the framework of SSA

The sea level change is a crucial indicator of our climate. The spatial sampling offered by satellite altimetry and its continuity during the past years are the major assets to provide an improved vision of the Mediterranean sea level changes. In this paper, an automatic signal extraction approach, based on Singular Spectrum Analysis (SSA), is utilized for analysis and seasonal adjustment of the Mediterranean Sea level series. This automatic approach enables us to overcome the difficulties of visual identification of trend constituents that sometimes we encounter when using the conventional SSA method. The results indicate that the Mediterranean mean sea level is dominated by several harmonic components. The annual signal is particularly strong and almost covers 73.62 % of the original sea level series variation whiles its amplitude is about 15 cm. The extracted trend also indicates that the Mediterranean main sea level has significantly been raised during the period 1993–2012 by 2.44?±?0.4 mm yr^?1. As an important consequence, considering the current situation, if this trend continues, the Mediterranean Sea level will be raised about 22 cm by the end of this century, which makes a dramatic effect on several issues such as land, flora, fauna, and people activities established along the Mediterranean coastlines.     

Study of pore pressure variation during liquefaction using two constitutive models for sand

Numerical analyses of liquefiable sand are presented in this paper. Liquefaction phenomenon is an undrained response of saturated sandy soils when they are subjected to static or dynamic loads. A fully coupled dynamic computer code is developed to predict the liquefaction potential of a saturated sandy layer. Coupled dynamic field equations of extended Biot's theory with u–P formulation are used to determine the responses of pore fluid and soil skeleton. Generalized Newmark method is employed for integration in time. The soil behavior is modelled by two constitutive models; a critical state two-surface plasticity model, and a densification model. A class ‘B’ analysis of a centrifuge experiment is performed to simulate the dynamic response of level ground sites. The results of the numerical analyses demonstrate the capability of the critical sate two-surface plasticity model in producing pore pressures that are consistent with observations of the behavior of liquefiable sand in the centrifuge test.     

Stochastic-fuzzy multi criteria decision making for robust water resources management

All realistic Multi Criteria Decision Making (MCDM) problems in water resources management face various kinds of uncertainty. In this study the evaluations of the alternatives with respect to the criteria will be assumed to be stochastic. Fuzzy linguistic quantifiers will be used to obtain the uncertain optimism degree of the Decision Maker (DM). A new approach for stochastic-fuzzy modeling of MCDM problems will be then introduced by merging the stochastic and fuzzy approaches into the Ordered Weighted Averaging (OWA) operator. The results of the new approach, entitled SFOWA, give the expected value and the variance of the combined goodness measure for each alternative, which are essential for robust decision making. In order to combine these two characteristics, a composite goodness measure will be defined. By using this measure the model will give more sensitive decisions to the stakeholders whose optimism degrees are different than that of the decision maker. The methodology will be illustrated by using a water resources management problem in the Central Tisza River in Hungary. Finally, SFOWA will be compared to other methods known from the literature to show its suitability for MCDM problems under uncertainty.     

Unsupported advance length in tunnels constructed using New Austrian Tunnelling Method and ground surface settlement

Tunnels constructed using New Austrian Tunnelling Method (NATM) are always based on certain round (unsupported) advance lengths, after which, the temporary lining is placed. The settlement of the ground surface resulting from such construction is of high significance in design and practice. The existing data in this respect, however, is scarce. It is the aim of this paper to propose a semi‐analytical procedure based on three‐dimensional finite element analyses to predict the maximum surface settlement of the ground in NATM tunnels under different combinations of tunnel diameter, overburden depth, round length and soil and lining properties. The comparison of the results with three case histories of real tunnels reveals reasonable accuracy of the present solution. Copyright © 2012 John Wiley & Sons, Ltd.     

Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry

A genetic algorithm (GA)-based neuro-fuzzy approach is used for identification of geochemical anomalies by implementing a Takagi, Sugeno and Kang (TSK) type fuzzy inference system in a 5-layered feed-forward adaptive artificial neural network. This paper investigates the effectiveness of GA-based neuro-fuzzy for separating zone dispersed mineralization (ZDM) from blind mineralization, and its application for identification of geochemical anomalies in the arid landscape of the Lut metallogenic province in eastern Iran. Other classification algorithms such as metallometry, zonality, criteria, and back-propagation artificial neural network classifiers are also used for comparison. The genetic operators are carefully designed to optimize the artificial neural network, avoiding premature convergence and permutation problems. The results show that the GA-based hybrid neuro-fuzzy model can provide accurate results in comparison with those results obtained by other techniques. Neuro-fuzzy and GA-based neuro-fuzzy techniques appear to be well-suited for routine exploration geochemistry applications. In conjunction with statistics and conventional mathematical methods, hybrid approaches can be developed and may prove a step forward in the practice of applied geochemistry.     

Geochemical and mineralogical pattern recognition and modeling with a Bayesian approach to hydrothermal gold deposits

The Bayesian approach is an effective method of identifying the probability of mineralogical and geochemical type (MGT) mineralization of trace elements in galena, pyrite and other distributions in ore mineralization. Monomineralic samples have been identified using a computer-based Bayesian method and exploration geochemical techniques of Au deposits for MGT. In order to employ the method, a data bank was used consisting of the results of analysis of more than 12,000 monomineralic samples collected from the main hydrothermal Au deposits in Tajikistan (a territory of CIS). The Bayesian approach applied to geochemical data, such as posterior probabilities and discriminant analysis, provide numerical and graphical means through which the relationships between the trace elements and samples can be studied. The method used here, along with GIS, to find MGT can be used as geochemical indicators of regions with Au mineralization. The results of analyzing 100 monomineralic samples of pyrite from the Au–Ag Shkolnoe deposit (Tajikistan) show a multi-MGT anomaly superposition which is a combination of three MGT: (1) Au–Ag type (85% and more), (2) Au–sulfide-polymetallic type (46%), and (3) Au–sulfide type (40%). Mineralogical and geochemical maps (MGM) can be drawn based on results of MGT anomalies in a GIS environment. These maps can replace traditional metallogenic maps. The advantage of MGM substitutions is that a qualitative tool is replaced by a quantitative one. This helps one to make optimal managerial and more economical decisions.     

Automated classification of sloan digital sky survey (SDSS) stellar spectra using artificial neural networks

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.