Analysis of karst spring recession curves,west of Iran

The recession hydrographs of karst springs provide important information about aquifer characteristics such as storage properties and drainage potential, karstification degree, and other hydrological features. The Faresban, Famaasb, Gonbad-e-Kabood, and Gian springs are the important karst springs which drain main karst aquifers in west of Iran. In this study, we evaluate hydrological characteristics of these four karst springs using different analytic equations. Almost, all the recession curves were well fitted by a function that consists of three main exponential terms. The initial portion of the recession curve represents the fast drainage of large fractures and conduits, ending with the slowly decreasing curve, where the drainage of rock matrix and small fissures or baseflow is dominant. The Gian spring has the largest storage volume but the lowest drained water (7%). Using Mangin’s equation, it is apparent that the baseflow supplies over 85% of the total drained water to the Gian spring, while a small quantity of total outflow is made up of quickflow periods. Of the water available to the Famaasb, Faresban, and Gonbad-e-Kabood springs, 85, 80, and 70% are drained during period of baseflow, respectively, whereas the quickflow periods have minor importance in some springs. Based on the Coutagne’s equation results, it can be concluded that the catchment of the Famaasb and Gonbad-e-Kabood springs have a karstified saturation zone with drainage channels that provide a rapid discharge with little storage capacity. At Gian catchment area, the initial discharge is not as rapid as karstification but is fairly homogeneous throughout the aquifer.     

Origin of brine in the Kangan gasfield: isotopic and hydrogeochemical approaches

The Kangan Permo-Triassic brine aquifer and the overlying gas reservoir in the southern Iran are located in Kangan and Dalan Formations, consisting dominantly of limestone, dolomite, and to a lesser extent, shale and anhydrite. The gasfield, 2,900 m in depth and is exploited by 36 wells, some of which produce high salinity water. The produced water gradually changed from fresh to saline, causing severe corrosion in the pipelines and well head facilities. The present research aims to identify the origin of this saline water (brine), as a vital step to manage saline water issues. The major and minor ions, as well as δ²H, δ¹⁸O and δ³⁷Cl isotopes were measured in the Kangan aquifer water and/or the saline produced waters. The potential processes causing salinity can be halite dissolution, membrane filtration, and evaporation of water. The potential sources of water may be meteoric, present or paleo-seawater. The Na/Cl and I/Cl ratios versus Cl^? concentration preclude halite dissolution. Concentrations of Cl, Na, and total dissolved solid were compared with Br concentration, indicating that the evaporated ancient seawater trapped in the structure is the cause of salinization. δ¹⁸O isotope enrichment in the Kangan aquifer water is due to both seawater evaporation and interaction with carbonate rocks. The δ³⁷Cl isotope content also supports the idea of evaporated ancient seawater as the origin of salinity. Membrane filtration is rejected as a possible source of salinity based on the hydrochemistry data, the δ¹⁸O value, and incapability of this process to dramatically enhance salinity up to the observed value of 330,000 mg/L. The overlaying impermeable formations, high pressure in the gas reservoir, and the presence of a cap rock above the Kangan gasfield, all prevent the downward flow of meteoric and Persian Gulf waters into the Kangan aquifer. The evaporated ancient seawater is autochthonous, because the Kangan brine aquifer was formed by entrapment of brine seawater during the deposition of carbonates, gypsum, and minor clastic rocks in a lagoon and sabkha environment. The reliability of determining the source of salinity in a deep complicated inaccessible high-pressure aquifer can be improved by combining various methods of hydrochemistry, isotope, hydrodynamics, hydrogeology and geological settings.     

Geochemistry of subsurface Late Quaternary ironstones in Rajshahi and Bogra Districts,Bangladesh: implications for genetic and depositional conditions

The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with(fragmentary) ironstones. The ironstones contain major oxides, Fe_2 O_3*(*total Fe)(avg. 66.6 wt%), SiO_2(avg. 15.3 wt%), Al_2 O_3(avg. 4.0 wt%), MnO(avg. 7.7 wt%), and CaO(avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe_2 O_3* along with Al_2 O_3 and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO_2 indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO(avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud.Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.     

Etiology of Salinity and Water Origin,the Main Dilemma of Badab Sourt,a Unique Travertine Spring

Badab Sourt travertine‐depositing springs in the north of Iran, naturally create a unique surreal landscape containing a range of stepped travertine terraces, similarly found only in a few other places on earth. This site comprises of three travertine saline springs with different values of salinity and discharge (SP₁, SP₂, and SP₃) and one non‐travertine fresh karstic spring (SP₄) within a distance of about 300 m. The etiology behind this salinity and the water origin are the main research's dilemma that were investigated using geological, hydrochemical, and stable isotopic techniques. Based on the topography and isotopic results, the carbonate formations in northern (Khoshyeilagh and Mobarak) and southern (Cretaceous limestone) parts of the springs potentially provide the initial hydraulic gradient for deep circulation of the water and CO₂. However, geological studies indicate that the hydraulic connectivity of the Cretaceous formation to the travertine springs is interrupted by impermeable geological formations. Based on the proposed conceptual hydrogeological model and mass balance calculations, the SP₄ spring is locally recharged from the nearby karstic area of Khoshyeilagh formation through shallow, short and steep groundwater flow circulation that is completely different from the travertine springs. The travertine spring (SP₁) is recharged from more distant areas having higher altitudes on Mobarak and Khoshyeilagh limestone and circulate more deeply before emerging on the surface. The SP₂ and SP₃ springs can derive from the mixing of the saline water (SP₁) and fresh water (SP₄). The dissolution of interlayers of halite in Shemshak formation is concluded as the main source of salinity. This is the first research article in detail to survey hydrogeology of the travertine springs in Iran.     

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.     

马来半岛彭亨河和吉兰丹河沉积物稀土元素特征及其物源示踪

通过对马来半岛东部彭亨河28个站位和吉兰丹河22个站位表层沉积物进行稀土元素（REE）测试,对比分析了稀土元素的组成特征和分布规律,探讨了稀土元素组成的控制因素和物源示踪意义。结果表明,彭亨河沉积物稀土元素含量介于24.88～304.29 μg/g之间,平均含量为165.22 μg/g,吉兰丹河沉积物中稀土元素含量介于126.02～281.40 μg/g之间,平均值为181.15 μg/g。彭亨河大部分沉积物上陆壳(UCC)标准化模式为重稀土相对轻稀土富集,吉兰丹河沉积物轻重稀土无明显分异。沉积物源岩和矿物组成对两条河流的REE组成起到了重要的控制作用,化学风化对彭亨河REE组成的影响大于吉兰丹河,而彭亨河沉积物粒度组成显著差异也导致了其REE含量变化范围更大。δEu_UCC-(Gd/Yb)_UCC关系图中彭亨河和吉兰丹河沉积物分区明显,表明其可作为定性判别两条河流来源的有效指标,并可用于海区沉积物来源的示踪和定量识别。     

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