Impact of Reservoir Permeability,Permeability Anisotropy and Designed Injection Rate on CO2 Gas Behavior in the Shallow Saline Aquifer at the CaMI Field Research Station,Brooks, Alberta

Natural Resources Research - Carbon capture and storage is part of Canada’s climate change action plan to reduce greenhouse gas emissions. The Containment and Monitoring Institute Field...     

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Evaluation of Air Pollution Trend Using GIS and RS Applications in South West of Iran

In this paper, remote sensing and GIS have been used to assess the status of NO₂ at the south west of Iran. 221 data about concentration of NO₂ was extracted from Ozone Monitoring Instrument. Ordinary kriging and inverse distance weighting interpolation methods was used to interpolate data. Results showed that ordinary kriging method using cross-validation have had less error. North east of the study area has the highest concentration of NO₂ (329 molecule/cm²) and the concentration of NO₂ decreases from north east to South west of the study area. On the other hand, data trend results showed that the data seems to exhibit a fairly strong trend in the east west direction and a weaker one in the north–south direction.     

Tensionless–frictionless interaction of flexible annular foundation with a transversely isotropic multi‐layered half‐space

A transversely isotropic multi‐layered half‐space, with axis of material symmetry perpendicular to the free surface, supports a flexible either annular or solid circle foundation. The contact area of the foundation and the half‐space is considered to be both frictionless and tensionless. The foundation is assumed to be affected by a vertical static axisymmetric load. Detailed analysis of the interaction of these two systems with different thickness of layers is the target of this paper. With the use of ring load Green's functions for both the foundation and the continuum half‐space, an integral equation accompanied with some inequalities is introduced to model the complex BVP. With the incorporation of ring‐shape FEM, we are capable of capturing both regular and singular solution smoothly. The validity of the combination of the analytical and numerical method is proved with comparing the results of this paper with a number of benchmark cases of both linear and nonlinear interaction of circular and annular foundation with half‐space. Some new illustrations are presented to portray the aspect of the anisotropy and layering of the half‐space. Copyright © 2014 John Wiley & Sons, Ltd.     

Formulation of the Caspian Sea’s legal regime using a joint zone approach

The legal regime of the Caspian Sea has been determined under Treaties 1921 and 1940 by and between Iran and former Soviet Union. In fact, we are neither to explain it nor to determine. As understood from the provisions therein, the exploitation and use of the Caspian Sea by both countries are based on the Condominium. However, there is a 10-nautical mile strip as exclusive fishing areas which have been allocated to the states under the 1940 treaty. It means that Soviet Union and Iran may benefit the marine livings resources out of that strip on an equal manner. This right also includes the seabed and underlying bed. So, the legal regime of this sea may be based on a 10-nautical mile exclusive region for all five states and the remaining part as common and joint area under an agreement until an explaining and supervising organization is established to exploit and supervise over living and non-livings resources.     

Effects of Slaking fluid pH on the Index Properties of Carbonate Rocks

Geotechnical and Geological Engineering - The physical and chemical characteristics of water critically influence the mechanical and chemical breakdown of rocks. The physical properties of rocks,...     

The origin of mafic rocks in the Naqadeh intrusive complex, Sanandaj-Sirjan Zone, NW Iran

The Naqadeh mafic plutonic rocks are located on a plutonic assemblage and include different granitoid rocks related to ～40 Ma. U-Pb SHRIMP data shows different ages of 96?±?2.3 Ma for mafic rocks. Naqadeh mafic plutonic rocks consist of diorite to diorite-gabbros with relatively high contents of incompatible elements, low Na2O, and $ {\hbox{Mg\# }} = \left[ {{\hbox{molar}}\;{100} \times {\hbox{MgO/}}\left( {{\hbox{MgO}} + {\hbox{FeO}}} \right)} \right] > 44.0 $ . These features suggest that the Naqadeh mafic rocks originate from enriched lithospheric mantle above subducted slab during Neotethys subduction under Iranian plate.     

Artificial Neural Network as a Tool for Backbreak Prediction

Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R² = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively.     

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.