Detection of chromite bearing mineralized zones in Abdasht ophiolite complex using ASTER and ETM+ remote sensing data

Podiform chromite ore deposits in ultramafic parts of ophiolite rock complexes can be detected using remote sensing data. This study focuses on the discrimination of chromite bearing mineralized zones using Landsat TM and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data in Abdasht ophiolite complex, south of Iran. Several image processing methods, including Log residual, Decorellation Stretch, Band ratio and Mixture-Tuned Matched-Filtering (MTMF) have been evaluated for lithological mapping using Landsat ETM and ASTER data. The outcome showed that TIR band ratios of ASTER can discriminate quartzite, carbonate and mafic–ultramafic rocks in the ophiolite complex. Log residual, Decorollation Stretch and MTMF methods were more capable than previous published ASTER methods specifically for lithological mapping at a regional scale. New geological map of Abdasht region was produced based on the interpretation of ASTER image processing results and field verification. Consequently, the proposed methods demonstrated the ability of ASTER and Landsat ETM data to provide information for detecting chromite host rock (serpentinized dunites) that is valuable for chromite prospecting in study area. Additionally, the techniques used in this study are subtle for exploration geologist and mine engineering to identify high-potential chromite-bearing zones in the ophiolite complex, minimizing costly and time-consuming field works.     

Random vibration of nonlinear structures with stiffness and strength deterioration by modified tail equivalent linearization method

In this research the tail equivalent linearization method(TELM) has been extended to study structures with degrading materials. The responses of such structures to excitations are non-stationary, even if the excitations are stationary. Non-stationary behavior of the system cannot be considered by conventional TELM. Applying the conventional TELM, the only distinction in the design point excitation for two stationary excitations with different durations is in the addition of a zero value part at the beginning of the design point of the longer excitation. This means that the failure probability is the same for the non-stationary systems under excitations with different durations. Therefore, this solution cannot be correct. In this study, in using TELM for systems with degrading materials, hysteretic energy is replaced by average hysteretic energy, calculated by averaging the obtained hysteretic energy of the structure subjected to a few random sample load realization. In this way, the degradation parameters under design point coincide with those under sample load realizations. Since the average of the hysteretic energy is converges very fast, the modified TELM only requires about tens to hundreds solutions of the response in addition to the ordinary calculations of conventional TELM.     

Nitrate pollution of groundwater in Toyserkan,western Iran

A total of 95 groundwater samples were collected from Toyserkan, western Iran to assess the chemical composition and nitrate (NO₃ ⁻) status of groundwater. The most prevalent water type is Ca–HCO₃ followed by water types Ca–Mg–HCO₃. In comparison with the World Health Organization (WHO) drinking water guideline of 50 mg l⁻¹ for NO₃ ⁻, a total of nine wells (9.5%) showed higher concentrations. In 36% of samples (34) NO₃ ⁻ concentration was low (<20 mg l⁻¹), and in 53.7% of samples (51), in the range of 20–50 mg l⁻¹. The samples were classified into four groups based on NO₃ ⁻ and chloride (Cl⁻) concentrations. Of the samples, 40% were classified as group 4 and were relatively high in Cl⁻ and NO₃ ⁻ (Cl⁻ > 47 mg l⁻¹, NO₃ ⁻ > 27 mg l⁻¹). The high correlation between NO₃ ⁻ and Cl⁻ (r = 0.86, p < 0.01) is consistent with a manure source, resulting from the practice of adding salt to animal feed. Pollution of groundwaters appeared to be affected by the application of inorganic fertilizer at greater than agronomic rates, Cl-salt inputs, and irrigation practice.     

Metals removal during estuarine mixing of Arvand River water with the Persian Gulf water

In the present study, the removal of dissolved and colloidal Cd, Co, Cu, Ni and Zn in Arvand River water during estuarine mixing with the Persian Gulf water is investigated. The flocculation process was investigated for a series of mixtures with salinities ranging from 0.48 to 30.3^. The flocculation rates were indicative of the non-conservative behavior of studied metals during estuarine mixing. Rapid flocculation in the low salinity regimes was observed. The order of the final flocculation rate of metals in the river water was as follows: Co (91.2%)> Cd (86.9%)> Zn (83%)> Cu (75.2%)> Ni (74.3%). Salinity, pH, EC and dissolved oxygen do not govern the flocculation of metals during estuarine mixing. The results of the present investigation show that estuarine processes can be considered as an effective mechanism in self purification of colloidal metals that are anthropogenically introduced into the fresh water ecosystem.     

Prediction of water inflow to mechanized tunnels during tunnel-boring-machine advance using numerical simulation

An accurate estimate of the groundwater inflow to a tunnel is one of the most challenging but essential tasks in tunnel design and construction. Most of the numerical or analytical solutions that have been developed ignore tunnel seepage conditions, material properties and hydraulic-head changes along the tunnel route during the excavation process, leading to inaccurate prediction of inflow rates. A method is introduced that uses MODFLOW code of GMS software to predict inflow rate as the tunnel boring machine (TBM) gradually advances. In this method, the tunnel boundary condition is conceptualized and defined using Drain package, which is simulated by dividing the drilling process into a series of successive intervals based on the tunnel excavation rates. In addition, the drain elevations are specified as the respective tunnel elevations, and the conductance parameters are assigned to intervals, depending on the TBM type and the tunnel seepage condition. The Qomroud water conveyance tunnel, located in Lorestan province of Iran, is 36 km in length. Since the Qomroud tunnel involved groundwater inrush during excavating, it is considered as a good case study to evaluate the presented method. The groundwater inflow to this tunnel during the TBM advance is simulated using the proposed method and the predicted rates are compared with observed rates. The results show that the presented method can satisfactorily predict the inflow rates as the TBM advances.     

Comparison of two intelligent models to estimate the instantaneous global solar radiation in semi-arid climate conditions: Application in Iran

Solar radiation incident on the earth’s surface is a fundamental input for many aspects of climatology, hydrology, biology, and architecture. In addition, it is an important parameter in solar energy applications. Due to the high cost of the measuring instruments of solar radiation, many researchers have suggested different empirical methods to estimate this essential parameter. In this study, with the help of fuzzy systems and neural networks, two models have been designed to estimate the instantaneous global solar radiation in Rafsanjan city which has a typical climatic conditions of semi-arid region of middle eastern countries. In fuzzy and neural network model, the inputs are the number of the given day in the year, time, ambient temperature and cloudiness, The comparison between the results of the models and the measurements, shows that the estimated global radiation is similar to the measurement; for fuzzy model, statistical indicators RMSE, MBE and t-test are 103.4367 \((\hbox {w/m}^{2})\), 4.1169 \((\hbox {w/m}^{2})\) and 9.1318, respectively and for ANN, they are 85.46 \((\hbox {w/m}^{2})\), 3.08 \((\hbox {w/m}^{2})\) and 5.41, respectively. As the results indicate, both models are able to estimate the amount of radiation well, while the neural network has a higher accuracy. The output of the modes for six other cities of Iran, with similar climate conditions, also proves the ability of the proposed models.