Geopersia
Volume:3 Issue: 1, Winter-Spring 2013

Surface oil seeps from Alasht, Lapur and Jenesem share many compositional affinities including distributions and concentrations of normal alkanes, terpanes and steranes. These oils also show many similarities with that of Jurassic sediments (Shemshak Group, Klariz Formation) systematically sampled from the Galandrud Coal Mine. Recent geochemical studies in this area indicated that the Shemshak Group is rich in organic matter and has potential to generate oil and chiefly gas. The Kalariz Formation here is composed of350 m of mudstones, siltstones and fine-grained sandstones with over 30 coal seams. This formation is thought to have sourced Alasht and Lapur oil seeps. GC and GC-MS chromatograms of Jenesem oil are unique and show no any similarities with that of above mentioned oils or the Shemshak Formation. This indicates that the Jenesem oil has been generated from a different source rock but its origin is still unknown.

Statistical studies on Palynology contents of Late Cretaceous to Paleocene age Gurpi Formation in a surface section in Zagros Basin, SW Iran indicate changes in abundance, species diversity, ratio of Spiniferites to Cyclonephelium (S/C), palynological marine index (PMI) values and organic facies. These palynological variations clearly reflect fluctuations in relative sea-level and depositional environment and can be used for recognition and differentiation of systems tracts and key horizons such as flooding surfaces and sequence boundaries. Dramatic increase in PMI, dinoflagellate species diversity and S/C ratio associated with sedimentary facies parameters indicate a marine transgressive systems tract while, a reduced species diversity, lower S/C ratio and PMI value along with increase in abundance of phytoclasts and degraded land-plant materials are characteristics of marine regression. The variation trends of the palynological parameters when consecutive indicate a complete cycle of relative sea-level change. Abundance of Spiniferites, Achomosphaera, Areoligera, and Cleistosphaeridium species and presence of peridiniacean dinoflagellate cysts show a warm Tethyan, upper bathyal to middle shelf environment of deposition for the Gurpi Formation. The nine depositional sequences identified here can be correlated to the global third-order sea-level cycles. The main regional sequence surfaces like 180 (maximum flooding surface) in the Late Cretaceous sequences (68Ma) in northeast of Arabian Plate are recognizable here.

Well exposed Famennian, mainly carbonate rocks of the Bahram Formation in the vicinity of Bagher Abad village, Central Iran, yielded chondrichthyan teeth some sarcopterygii and actinopterygii teeth and fragments. Three different genera were recognized among the chondrichthyan teeth represented by: Phoebodus turnerae, Ph. spp., Deihim mansureae (M2, M3), Stethacanthus sp..Acanthodian and sarcopterygians teeth and fragments include: Dipnoi indet., Onychodonthid tooth?, Osteichthyes bone and Paleonisciformes indet. This assemblage recovered from a shallow shelf environment on the northern margin of Gondwana during Late Devonian time.

In this paper, the Artificial Neural Network (ANN) approach is applied for forecasting groundwater level fluctuation in Aghili plain, southwest Iran. An optimal design is completed for the two hidden layers with four different algorithms: gradient descent with momentum (GDM), levenberg marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG). Rain, evaporation, relative humidity, temperature (maximum and minimum), discharge of irrigation canal, and groundwater recharge from the plain boundary were used in input layer while future groundwater level was used as output layer. Before training, the available data were divided into three groups, according to hydrogeological characteristics of different parts of the plain surrounding, each piezometer. Therefore, FFN-LM algorithm has shown best result in the present study for all three hydrogeological groups. At last, to evaluate applied division, a unit network with all data and using LM algorithm was trained. Validation of the network shows that dividing the piezometers into different groups of data and designing distinct networks gives more focus on simulating groundwater level in the plain. The degree of accuracy of the ANN model in prediction is acceptable. Thus, it can be determined that ANN provides a feasible method in predicting groundwater level in Aghili plain.

Fuzzy Water Quality Index (FWQI) was applied in order to assess the degree of drinking water resources in Yazd province, Iran. This study has also offered the creation of a new fuzzy water quality index (FWQI) to evaluate this tool’s applicability. 12 chemical parameters including toxic and non-toxic heavy metals measured in 71 groundwater samples collected from drinking water resources in rural areas were used. In FWQI, input data are categorized into three linguistic terms (“Desirable” or “Low”, “Acceptable” or “Medium” and “Not-acceptable” or “High”) based on water quality standards for drinking water, Whereas the output data are categorized into five classes (“Poor”, “Fair”, “Medium”, “Good” and “Excellent”) based on water quality index (WQI). The results show that 8 groundwater samples were classified in the “Excellent” class with a certainty level of 5.33-76.67%, 41 samples in the “Good” group with a certainty level of 8.5-96.5%, 8 samples were in the “Medium” category with a certainty level of 14-93.5%, 1 sample in the “Fair” level with a certainty level of 36.5%, and 13 samples were classified in the “Poor” class with a certainty level of 54.8-81.5% for potable purposes. The proposed Index can be a useful tool to be used in decision making and environmental.

In this study multivariate statistical analysis are used to characterize relationships between hydrochemical properties of thermal and non-thermal springs. Four factors for thermal waters and two for non-thermal springs were extracted based on factor analysis. In thermal springs, the first factor showed high loading on Ca, Mg, Na and K and this factor was interpreted as leaching of cations in the rocks by meteoric thermal water. The second factor showed high loading on SO4 and it was assumed to be extracted from gypsum dissolution. The third factor showed loading on HCO3 and it was interpreted to be caused by dissolution of limestone. The forth factor showed high loading on SiO2 and it was supposed to be result of alteration of silicate minerals. The δ18O-enrichment with respect to the meteoric water line (MWL), confirms that thermal waters have been diluted by shallow waters with meteoric origin. Comparison of the chemistry of thermal and non-thermal springs and other evidences are indicative of an immature hydrothermal water system in Mahalat. Quartz and Chalcedony geothermometery show the range of 96oC to 131 oC temperature for the Mahalat reservoir.

Biotite samples from different units of Boroujerd Granitoid Complex (BGC) of the Sanandaj-Sirjan Zone, western Iran, have been analyzed by electron microprobe for major elements. Biotite analyses from three units of quartzdiorite, granodiorite and monzogranite of BGC have their own distinct non-overlapping compositional fields in the annite – siderophyllite – phlogopite – eastonite quadrilateral (ASPE), reflecting their host rock compositions. Biotite from each rock unit has an increasing trend of Al contents at almost fixed Fe/(Fe+Mg) values. In quartzdiorite it shows an approximately constant range of Fe (Fe+Mg) with a low to moderate Al content from 2.5 to 3 atoms per formula unit (apfu). Biotite from granodiorite exhibits a fairly wide range of Al values reaching up to 3.32 apfu, at Fe/(Fe+Mg) from 0.6 to 0.7, whereas biotite from monzogranite have a relatively narrow range of Fe/(Fe+Mg) and total Al values of limited range of 3.1 to 3.3 apfu. Biotite compositions from these two latter units considered to be derived entirely from crustal material, characterized by a remarkable increase in total Al at relatively high Fe contents. Biotite samples of quartzdiorites define a distinct and non-overlapping trend from those of granidiorites and monzogranites and hence interpreted to be derived from a parental magma with different composition. Calculation of log(XMg/XFe) ranges from -0.09 to -0.0 and most of samples from quartzdiorite fall within weakly and moderately contaminated I-type field of log(XF/XOH) versus lo (XMg/XFe) diagram, whereas the other two units, containing biotites with log(XMg/ XFe)< -0.21, classified as strongly contaminated reduced I-type. Oxygen fugacity (log ƒO2) of -15.4 to -17.5 bars and ƒH2O of 200 to 560 bars were calculated for quartzdiorite. Likewise, log (ƒO2) of –17.66 bars and water fugacity (ƒH2O) of 400 and 700 bars were also calculated for granodiorite and monzogranites respectively. In the FeO*–MgO–Al2O3 biotite discrimination diagram, biotite compositions from BGC are distributed between the calc-alkaline and peraluminous fields, i.e., biotite from the qaurtzdioritic rocks fall principally in the calc-alkaline field, whereas those from the granodioritic and monzogranitic units plot almost exclusively in the peraluminous field consistent with their host rock nature