Earthquake relocation in the Central Alborz region of Iran using a non-linear probabilistic method

In this study, we calculate accurate absolute locations for nearly 3,000 shallow earthquakes (≤20 km depth) that occurred from 1996 to 2010 in the Central Alborz region of northern Iran using a non-linear probabilistic relocation algorithm on a local scale. We aim to produce a consistent dataset with a realistic assessment of location errors using probabilistic hypocenter probability density functions. Our results indicate significant improvement in hypocenter locations and far less scattering than in the routine earthquake catalog. According to our results, 816 earthquakes have horizontal uncertainties in the 0.5–3.0 km range, and 981 earthquakes are relocated with focal-depth errors less than 3.0 km, even with a suboptimal network geometry. Earthquake relocated are tightly clustered in the eastern Tehran region and are mainly associated with active faults in the study area (the Mosha and Garmsar faults). Strong historical earthquakes have occurred along the Mosha and Garmsar faults, and the relocated earthquakes along these faults show clear north-dipping structures and align along east–west lineations, consistent with the predominant trend of faults within the study region. After event relocation, all seismicity lies in the upper 20 km of the crust, and no deep seismicity (>20 km depth) has been observed. In many circumstances, the seismicity at depth does not correlate with surface faulting, suggesting that the faulting at depth does not directly offset overlying sediments.     

Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model

The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.     

A novel continuous magnetic nano-Fe<Subscript>3</Subscript>O<Subscript>4</Subscript>/perlite fixed bed reactor for catalytic wet peroxide oxidation of dyes: reactor structure

In the present work, a continuous catalytic wet peroxide oxidation fixed bed reactor was employed to treat a simulated wastewater sample with malachite green dye, as a contaminant. Natural perlite particle-supported nano-Fe₃O₄ catalyst was used as a fixed bed inside a reactor, and it was immobilized by a persistent magnetic field. The range of (perlite) particle sizes was from 100 to 1000 nm. The effects of various operating parameters, including temperature of the reactor, pH, initial hydrogen peroxide concentration and initial dye concentration, were investigated on the percentage removal of malachite green dye. Load of catalyst of 2 g and volumetric flow rate of 1 L/h were selected for all the tests. Maximum malachite green degradation was 99.5 ± 0.3%. This removal percentage was attained at temperature of 80 °C, pH = 6, initial dye concentration of 6 mg/L and initial hydrogen peroxide concentration of 100 mg/L. The process was isotherm, and the catalyst showed high catalytic activity in the steady-state condition. The loss of catalyst was less than 0.3%.     

Sediment deposition from flow at low gradients into a buffer strip—a critical test of re-entrainment theory

The spatial and size distribution of sediment deposited from short periods of overland flow due to the effect of a simulated grass buffer strip was measured for low slopes of 1.6, 3.4 and 5.1%. These data were analysed so as to critically evaluate two alternative models of the process of re-entrainment of recently deposited sediment. A model of re-entrainment, previously thought to be appropriate only for a steady-state or equilibrium situation, was found to give better agreement with experiments than did a model previously used in the literature on this subject.     

Investigation of Failed Slopes Based on a New Method of Back Analysis in Rock Masses: A Case Study

Geotechnical and Geological Engineering - Small variation in shear strength parameters results in remarkable changes in the safety factor (SF) of a rock slope. In this regard, rock mass strength of...     

Comparison between Shannon and Tsallis entropies for prediction of shear stress distribution in open channels

The concept of Tsallis entropy was applied to model the probability distribution functions for the shear stress magnitudes in circular channels (with filling ratios of 0.506, 0.666, 0.826), circular with flat bed (filling ratios of 0.333, 0.666), rectangular channel (1.34, 2, 3.94, 7.37 aspect ratios) and compound channel (with relative depths of 0.324, 0.46). The equation for the shear stress distribution was obtained according to the entropy maximization principle, and is able to estimate the shear stress distribution as much on the walls as the channel bed. The approach is also compared with the predictions obtained based on the Shannon entropy concept. By comparing the two prediction models, this study highlights the application of Tsallis entropy to estimate the shear stress distribution of open channels. Although the results of the two models are similar in the circular cross-section, the differences between them are more significant in circular with flat bed and rectangular channels. For a wide range of filling ratio values, experimental data are used to illustrate the accuracy and reliability of the proposed model.     

Experimental Evaluation of A New Developed Algorithm for An Autonomous Surface Vehicle and Comparison with Simulink Results

China Ocean Engineering - Performing repeatable duties automatically was the dreams of human being for centuries. Although full autonomy has long been dreamed of by visionaries, many researches...     

Investigation of rangeland indicator species using parametric and non-parametric methods in hilly landscapes of central Iran

This study aimed to identify indicator species and explore the most important environmental and management variables contributing to vegetation distribution in a hilly upper dam landscape in Zagros Mountain chain, Iran. A stratified random sampling method was used to collect topographic, edaphic, management and vegetation data. The density and cover percentage of perennial species were measured quantitatively. Indicator species were identified using the two-way indicator species analysis. Besides calculating physiognomic factors in sample sites, 24 soil samples were collected from 0 to 30 cm of soil depth and analyzed in terms of gravel percentage, texture, saturation moisture, organic matter, pH and electrical conductivity in saturation extract, lime percentage, soluble calcium and magnesium, available phosphorus, Cation Exchange Capacity(CEC) and soluble sodium and potassium. Multivariate techniques including Canonical Correspondence Analysis and Multi-Dimensional Scaling were used to explore the relationships of species with environmental and management variables. Seven plants were identified as indicator species due to being significantly correlated with management(grazing or non-grazing) and edaphic variables such as CEC, soil texture, pH, CaCO_3 percentage and physiographic variable including slope, elevation, and convex and concave formations(p 0.05). Overall, overgrazing and its subsequent effects on soil characteristics, loss of vegetation cover and trampling were found as the major causes of deterioration. Sustainable and integrated management practices such as the implementation of appropriate grazing systems were suggested to enhance soil quality and reduce the accelerated erosion in upper dam zones.     

Evidence for the occurrence of hydrogeochemical processes in the groundwater of Khoy plain,northwestern Iran,using ionic ratios and geochemical modeling

Rapid population growth, industrialization, and agricultural expansion in the Khoy area (northwestern Iran) have led to its dependence on groundwater and degradation of groundwater quality. This study attempts to decipher the major processes and factors that degrade the groundwater quality of the Khoy plain. For this purpose, 54 groundwater samples from unconfined and confined aquifers of the plain were collected in July 2017 and analyzed for major cations and anions (Na, K, Ca, Mg, HCO₃, SO_4, and Cl), minor ions (NO₃ and F), and Al. Magnesium and bicarbonate were identified as the dominant cation and anion, respectively. Several ionic ratios and geochemical modeling using PHREEQC indicated that the most important hydrogeochemical processes to affect groundwater quality in the plain were weathering and dissolution of evaporitic and silicate minerals, mixing, and ion exchange. There were smaller effects from evaporation and anthropogenic factors (e.g., industries). Results showed that the high salinity of the groundwater in the northeast area of the plain was due to the high solubility of the evaporitic minerals, e.g., halite and gypsum. Reverse ion exchange and the contribution of mineral dissolution were more significant than ion exchange in the northeastern part of the plain. Elevated salinity of the groundwater in the southeast was attributed mostly to reverse ion exchange and somewhat to evaporation.