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.     

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.     

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.     

Variability of Airborne Dust Fallout and Associated Trace Metals around Industrial Sites in Esfahan,Iran

The seasonal and spatial variability of airborne dust deposits and associated trace metals such as Pb, Co, Ni, Cd, Mn in 15 sites surrounding a heavily industrialized region in south Esfahan (central Iran) are investigated. Total deposit rates (TDR) of dusts and trace metals are analyzed, contamination factor (CF) and pollution load index (PLI) are also calculated. Furthermore, correlation and cluster analysis are performed to identify the source of the pollution. The highest dust-TDR (15.97 g m⁻² per season), the highest concentration of trace metals, CF and PLI are recorded in summer because of the lack of precipitation, high temperature, and drought conditions in this season. Pb and Cd show the highest CF values. In the towns near the two major steel mills in the region (i.e., Esfahan Steel Company and Mobarekeh Steel Complex), the CF values for Pb and Cd are about 13 and 12, respectively (i.e., 13 and 12 times higher than the pre-industrial values, respectively). The spatial distribution maps of the dust deposit rate, dust-borne trace metals, and the obtained PLI of the trace metals in the study area reveal that the two major industries in the region are the main sources of dust and trace metal distribution.     

Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer,Iran)

Doklady Earth Sciences - Land subsidence, as a dangerous environmental issue, causes serious damages to farms and urban infrastructure. In this regards, this research was conducted with aimed to...     

Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model

The use of electrical conductivity (EC) as a water quality indicator is useful for estimating the mineralization and salinity of water. The objectives of this study were to explore, for the first time, extreme learning machine (ELM) and wavelet-extreme learning machine hybrid (WA-ELM) models to forecast multi-step-ahead EC and to employ an integrated method to combine the advantages of WA-ELM models, which utilized the boosting ensemble method. For comparative purposes, an adaptive neuro-fuzzy inference system (ANFIS) model, and a WA-ANFIS model, were also developed. The study area was the Aji-Chay River at the Akhula hydrometric station in Northwestern Iran. A total of 315 monthly EC (µS/cm) datasets (1984–2011) were used, in which the first 284 datasets (90% of total datasets) were considered for training and the remaining 31 (10% of total datasets) were used for model testing. Autocorrelation function (ACF) and partial autocorrelation function (PACF) demonstrated that the 6-month lags were potential input time lags. The results illustrated that the single ELM and ANFIS models were unable to forecast the multi-step-ahead EC in terms of root mean square error (RMSE), coefficient of determination (R²) and Nash–Sutcliffe model efficiency coefficient (NSC). To develop the hybrid WA-ELM and WA-ANFIS models, the original time series of lags as inputs, and time series of 1, 2 and 3 month-step-ahead EC values as outputs, were decomposed into several sub-time series using different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Coiflet of different orders at level three. These sub-time series were then used in the ELM and ANFIS models as an input dataset to forecast the multi-step-ahead EC. The results indicated that single WA-ELM and WA-ANFIS models performed better than any ELM and ANFIS models. Also, WA-ELM models outperformed WA-ANFIS models. To develop the boosting multi-WA-ELM and multi-WA-ANFIS ensemble models, a least squares boosting (LSBoost) algorithm was used. The results showed that boosting multi-WA-ELM and multi-WA-ANFIS ensemble models outperformed the individual WA-ELM and WA-ANFIS models.     

A GIS-based time-dependent seismic source modeling of Northern Iran

The first step in any seismic hazard study is the definition of seismogenic sources and the estimation of magnitude-frequency relationships for each source. There is as yet no standard methodology for source modeling and many researchers have worked on this topic. This study is an effort to define linear and area seismic sources for Northern Iran. The linear or fault sources are developed based on tectonic features and characteristic earthquakes while the area sources are developed based on spatial distribution of small to moderate earthquakes. Time-dependent recurrence relationships are developed for fault sources using renewal approach while time-independent frequency-magnitude relationships are proposed for area sources based on Poisson process. GIS functionalities are used in this study to introduce and incorporate spatial-temporal and geostatistical indices in delineating area seismic sources. The proposed methodology is used to model seismic sources for an area of about 500 by 400 square kilometers around Tehran. Previous researches and reports are studied to compile an earthquake/fault catalog that is as complete as possible. All events are transformed to uniform magnitude scale; duplicate events and dependent shocks are removed. Completeness and time distribution of the compiled catalog is taken into account. The proposed area and linear seismic sources in conjunction with defined recurrence relationships can be used to develop time-dependent probabilistic seismic hazard analysis of Northern Iran.