Hydrogeologic characteristics of the alluvial tuff aquifer of northern Sahand Mountain slopes, Tabriz, Iran

The Tabriz area is a densely populated area of northwestern Iran (more than 1.5 million in population) with a large proportion of its drinking, domestic, industrial and agricultural water supplied from groundwater resources. The average rate of drinking and industrial water use in the city of Tabriz is about 3.45 m³ s^–1. The Plio-Pleistocene unconfined alluvial tuff aquifer (about 1,275 km²), the most important aquifer in the area, has been known for many years as a reliable resource. The greatest estimated thickness of the alluvial tuff lies in the Saidabad area, with 350 m thickness. There are 994 deep and 284 shallow active pumping wells and 83 qanats operate in the alluvial tuff aquifer. The total water withdrawal from all these artificial discharge points has been measured at 72, 3.8 and 17 million m3/year, respectively. Analytical and numerical methods have been applied to the constant rate pumping test data from the Saidabad wellfield (eight pumping and three observation wells). The values of electrical conductivity in the groundwater of alluvial tuff aquifer range from 203 to 960 μS cm^–1 and bicarbonate type water dominates.     

A Preliminary Study on the Aghbolaq(Fe,Cu) Skarn Deposit,Oshnavieh, NW Iran: Constraints on Metasomatic Fluid Evolution

The Aghbolaq skarn deposit is located in the Urumieh-Golpayegan plutonic belt,NW Iran.The garnetite skarn(stage I) has been intensely cross-cut by the magnetite-garnet skarn (stage II) which were,in turn,cut and offset by the orehosting quartz veins/veinlets (stage III).The predominance of andradite (Adr_(82.5–89.1)) and its high Fe~(3+)/Al ratio (up to 1685)apparently supports the high f O_2,salinity and prevalence of magmatic/hydrothermal fluids involved,rather than meteoric waters,during the magnetite-garnet skarn formation.Two major groups of fluid inclusions,namely aqueous (LV,LVS) and aqueous–carbonic (LV_C,LL_CV_C),were recognized in garnet and quartz veins that,especially in growth zones and along intra-granular trails,better display fluid inclusion assemblages (FIAs) than those in clusters.The prograde magnetite-garnet skarn was formed by the metasomatic fluid at relatively high T_h (209–374℃),under a lithostatic pressure of～200 bars.The retrograde mineralized quartz veins were formed at temperatures ranging from 124℃to 256℃,by dilute and less saline(2.57–11.93 wt%Na Cl eq.) hydrothermal fluids under a hydrostatic pressure of～80 bars.The fluid evolution of the Aghbolaq skarn began with an earlier simple cooling of metasomatic fluid during the prograde stage,followed by the later influx of low salinity meteoric fluids during the retrograde stage.     

Modeling of the Shale Volume in the Hendijan Oil Field Using Seismic Attributes and Artificial Neural Networks

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs, necessitating that diverse kinds of information are used to infer these properties. In this study, the seismic data related to the Hendijan oil field were utilised, along with the available logs of 7 wells of this field, in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (model-based, band limited and sparse spike inversion) were applied to it. Amongst all of these techniques, the model-based method presented the better results. By using seismic attributes and artificial neural networks, the shale volume was then estimated using three types of neural networks, namely the probabilistic neural network (PNN), multi-layer feed-forward network (MLFN) and radial basic function network (RBFN).     

REE Tetrad Effect as a Powerful Indicator of Formation Conditions of Karst Bauxites: A Case Study of the Shahindezh Deposit,NW Iran

Study of the concentration of major, trace, and rare earth elements (REE) in the Shahindezh karst bauxite deposit, northwestern Iran clarifies the relationship of the tetrad effect with geochemical parameters in the bauxite ores. The existence of irregular curves in the chondrite-normalized REE patterns as well as non-CHARAC behavior of geochemically isovalent pairs (Y/Ho) are related to the tetrad effect. The meaningful positive correlation between the sizes of the calculated T3 tetrad effect and some geochemical factors such as Y/Ho, ΣREE, La/Y, (La/Yb)N, and (LREE/HREE)N as well as some major oxides-based parameters like Al2O3 + LOI/SiO2 + Fe2O3, Al2O3/Fe2O3, Al2O3 + LOI, IOL, and SiO2 + Fe2O3 indicate that the studied bauxite horizon was likely deposited by different (acidic and/or alkalic) solutions at different stages. The lower part of the studied horizon with a thickness of ~4.7 m displays alkali characteristics whereas the upper parts of the horizon with a thickness of ~5.3 m are characterized by more acidic conditions. These results are fully supported by the co-occurrence of convex-concave tetrad effect curves in the chondrite-normalized REE patterns. Therefore, the tetrad effect phenomenon used in this study has proved to be a good and reliable geochemical proxy to assess the conditions of the depositional environment in the Shahindezh bauxite ores.     

An efficient approach for historical storage and retrieval of segmented road data in Geographic Information System for Transportation

One of the most powerful functions of Geographic Information System for Transportation(GIS-T)is Dynamic Segmentation(DS),which is used to increase the efficiency and precision of road management by generating segments based on attributes.The road segments describing transportation data are both spatially and temporally referenced.For a variety of transportation applications,historical road segments must be preserved.This study presents an appropriate approach to preserve and retrieve the historical road segments efficiently.In the proposed method,only the portions of segments of a time stamp that have been changed into new segments rather than storing the entire segments for every old time stamp are recorded.The storage of these portions is based on the type of changes.A recursive algorithm is developed to retrieve all segments for every old time stamp.Experimental results using real data of Tehran City,Iran justify the strength of the proposed approach in many aspects.An important achievement of the results is that database volume for 2006,2007 and 2008 within the Historical Line Event Table(HLET)is reduced by 70%,80%and 78%,respectively.The proposed method has the potential to prevent from vast data redundancy and the unnecessary storage of entire segments for each time stamp.Since the present technique is performed on ordinary plain tables that are readable by all GIS software,special software platforms to manage the storage and retrieval of historical segments are not needed.In addition,this method simplifies spatio-temporal queries.     

Modification of the DRASTIC Framework for Mapping Groundwater Vulnerability Zones

The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO₃) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin-Shahr plain, Iran. Twenty-two groundwater samples were collected from wells in the study area, and their respective NO₃ concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO₃ concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R² of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R² value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R² value to the highest degree.     

Seismic modelling for reservoir studies: a comparison between convolutional and full-waveform methods for a deep-water turbidite sandstone reservoir

Two seismic modelling approaches, that is, two-dimensional pre-stack elastic finite-difference and one-dimensional convolution methods, are compared in a modelling exercise over the fluid-flow simulation model of a producing deep-water turbidite sandstone reservoir in the West of Shetland Basin. If the appropriate parameterization for one-dimensional convolution is used, the differences in three-dimensional and four-dimensional seismic responses from the two methods are negligible. The key parameters to ensure an accurate seismic response are a representative wavelet, the distribution of common-depth points and their associated angles of incidence. Conventional seismic images generated by the one-dimensional convolutional model suffer from lack of continuity because it only accounts for vertical resolution. After application of a lateral resolution function, the convolutional and finite-difference seismic images are very similar. Although transmission effects, internal multiples and P-to-S conversions are not included in our convolutional modelling, the subtle differences between images from the two methods indicates that such effects are of secondary nature in our study. A quantitative comparison of the (normalized root-mean-square) amplitude attributes and waveform kinematics indicates that the finite-difference approach does not offer any tangible benefit in our target-oriented seismic modelling case study, and the potential errors from one-dimensional convolution modelling are comparatively much smaller than the production-induced time-lapse changes.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Investigating the effect of complexity on groundwater flow modeling uncertainty

Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with Model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity.     

Comparison of machine learning models for predicting fluoride contamination in groundwater

Groundwater is an especially important freshwater source for water supplies in the Maku area of northwest Iran. The groundwater of the area contains high concentrations of fluoride and is, therefore, important in predicting the fluoride contamination of the groundwater for the purpose of planning and management. The present study aims to evaluate the ability of the extreme learning machine (ELM) model to predict the level of fluoride contamination in the groundwater in comparison to multilayer perceptron (MLP) and support vector machine (SVM) models. For this purpose, 143 water samples were collected in a five-year period, 2004–2008. The samples were measured and analyzed for electrical conductivity, pH, major chemical ions and fluoride. To develop the models, the data set—including Na⁺, K⁺, Ca²⁺ and HCO₃ ^? concentrations as the inputs and fluoride concentration as the output—was divided into two subsets; training/validation (80% of data) and testing (20% of data), based on a cross-validation technique. The radial basis-based ELM model resulted in an R ² of 0.921, an NSC of 0.9071, an RMSE of 0.5638 (mg/L) and an MABE of 0.4635 (mg/L) for the testing data. The results showed that the ELM models performed better than MLP and SVM models for prediction of fluoride contamination. It was observed that ELM models learned faster than the other models during model development trials and the SVM models had the highest computation time.