Tectonic significance of Triassic mafic rocks in the June Complex,Sanandaj–Sirjan zone,Iran

This study concentrates on the petrological and geochemical investigation of mafic rocks embedded within the voluminous Triassic June Complex of the central Sanandaj–Sirjan zone (Iran), which are crucial to reconstruct the geodynamics of the Neotethyan passive margin. The Triassic mafic rocks are alkaline to sub-alkaline basalts, containing 43.36–49.09 wt% SiO₂, 5.19–20.61 wt% MgO and 0.66–4.59 wt% total alkalis. Based on MgO concentrations, the mafic rocks fall into two groups: cumulates (Mg# = 51.61–58.94) and isotropic basaltic liquids (Mg# = 24.54–42.66). In all samples, the chondrite-normalized REE patterns show enrichment of light REEs with variable (La/Yb)_N ratios ranging from 2.48 to 9.00, which confirm their amalgamated OIB-like and E-MORB-like signatures. Enrichment in large-ion lithophile elements and depletion in high field strength elements (HFSE) relative to the primitive mantle further support this interpretation. No samples point to crustal contamination, all having undergone fractionation of olivine + clinopyroxene + plagioclase. Nevertheless, elemental data suggest that the substantial variations in (La/Sm)_PM and Zr/Nb ratios can be explained by variable degrees of partial melting rather than fractional crystallization from a common parental magma. The high (Nb/Yb)_PM ratio in the alkaline mafic rocks points to the mixing of magmas from enriched and depleted mantle sources. Abundant OIB alkaline basalts and rare E-MORB appear to be linked to the drifting stage on the northern passive margin of the Neotethys Ocean.     

Maturity modeling and burial history reconstruction for Garau and Sargelu formations in Lurestan basin,south Iran

The Upper Jurassic Sargelu and the Cretaceous Garau formations are important source rocks in the Lurestan basin, southwest Iran. In this study, the maturity evolution and burial history of these source rocks are investigated using 1D modeling in 15 wells within the Lurestan basin. These models are calibrated using temperature and vitrinite reflectance readings at well locations. Results indicate that thermal maturity of studied source rocks increases towards the northwest of the Lurestan basin and is the lowest over the Anaran High. The present-day level of maturity suggests that these formations are suitable candidates for shale gas resources in the Lurestan basin.     

Land assessment for flood spreading site selection using geospatial information system

Irregular patterns of precipitations from temporal as well as spatial perspectives not only cause destructions but also waste surface water resources. Hence, controlling surface water and leading the flood to underground stores improve the efficiency of water usage. Selecting appropriate sites for optimal use of water floods is one of the most important factors in recharging underground water tables in dry lands where the agricultural and rangelands are vulnerable. Traditional methods of site selections are, however, time consuming and error prone. This paper attempts to; analyze existing schemes of site selection; introduces an appropriate method of locating flood-spreading sites using Geospatial Information System; implements the strategy in a case study; and scientifically assesses its results. The study area of this research is Samal sub-basin covering 31571.7 ha of Ahrom basin in Boushehr province. In the present research, factors pertain to earth sciences (quaternary units, slope and landuse) and hydrology (runoff infiltration rate and aquifers’ depth) are considered. Information layers are weighted, classified and integrated through several models such as boolean logics, index overlay and fuzzy logics. The results are then checked against the existing sites to estimate their accuracy. The results of this research demonstrated that fuzzy logic operators including gamma=0.1, gamma=0.2 and products of fuzzy logics yield the best when compared to control fields and therefore, the models are introduced as the most suitable site selection strategies for flood spreading.     

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg?=?89.9%, veg–water 1?=?88.8%, veg–water 2?=?87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.     

Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the reduction in estimated noise levels for those groups with the fewer number of noisy data points.     

Sea‐level rise impact on fresh groundwater lenses in two‐layer small islands

The fresh groundwater lenses (FGLs) of small islands can be highly vulnerable to climate change impacts, including sea‐level rise (SLR). Many real cases of atoll or sandy islands involve two‐layer hydrogeological conceptualizations. In this paper, the influential factors that affect FGLs in two‐layer small islands subject to SLR are investigated. An analytical solution describing FGLs in circular islands, composed of two geological layers, is developed for the simplified case of steady‐state and sharp‐interface conditions. An application of the developed model is demonstrated to estimate the FGL thickness of some real‐world islands by comparison with existing FGL thickness data. Furthermore, numerical modelling is applied to extend the analysis to consider dispersion effects and to confirm comparable results for both cases. Sensitivity analyses are used to assess the importance of land‐surface inundation caused by SLR, relative to other parameters (i.e. thickness of aquifer layers, hydraulic conductivity, recharge rate and land‐surface slope) that influence the FGL. Dimensionless parameters are used to generalize the findings. The results demonstrate that land‐surface inundation has a considerable impact on a FGL influenced by SLR, as expected, although the FGL volume is more sensitive to recharge, aquifer thickness and hydraulic conductivity than SLR impacts, considering typical parameter ranges. The methodology presented in this study provides water resource managers with a rapid‐assessment tool for evaluating the likely impacts of SLR and accompanying LSI on FGLs.     

Experimental Investigation of Wettability Alteration from Water-wet to Oil-wet During Oil Migration

Natural Resources Research - Most oil reservoirs are first saturated with water and become oil-wet gradually due to oil migration and accumulation. Previous studies reported that the adsorption of...