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Goldschmidt2018 gsa2017 2018 Ocean Sciences Meeting (OSM) egu2017

Just Accepted Manuscripts

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  • Amira Ayed-Khaled, Taher Zouaghi, Mohamed Ghanmi. 2017. Structural dynamics in northern Atlas of Tunisian, Jendouba area: insights from geology and gravity data. Journal of Tethys, Vol. 5, No. 2, 103-114.

Abstract: This paper presents a new interpretation of the geometry of Triassic alignment of J. Sidi Mahdi –J. Zitoun in Medjerda Valley Plain (Northern Tunisia) based on detailed analysis of gravity and seismic reflection data. The main results of gravity analysis do not show a distinguish gravity anomaly over Triassic evaporites bodies. The positive gravity anomaly seems to be related to the entire structure containing Triassic evaporites and Cretaceous carbonates and marl strata. Horizontal gravity map highlights NE-SW and NW-SE two prominent directions that surround Triassic outcrops and their bordering strata. Specifically, the seismic sections show a structural evolution and halokinesis in our study area. Analysis of seismic lines associated to interval iso-velocity sections highlight Triassic rising structures related to Mesozoic and Cenozoic tectonic control. The resulted structures are interpreted as original Mesozoic diapirs followed by a lateral outpouring above the late Cretaceous to Eocene series.

Keywords: Northern Tunisia; Triassic; Rising; Seismic Interval Velocity; Salt Outpouring.

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  • Feridon Ghadimi. 2017. Machine Learning Algorithm for Prediction of Heavy Metal Contamination in the Groundwater in the Arak Urban Area. Journal of Tethys, Vol. 5, No. 2, 115-127.

This paper attempts to predict heavy metals (Pb, Zn and Cu) in the groundwater from Arak city, using support vector regression model(SVR) by taking major elements (HCO3, SO4) in the groundwater from Arak city. 150 data samples and several models were trained and tested using collected data to determine the optimum model in which each model involved two inputs and three outputs. This SVR model fit captures the prime idea of statistical learning theory in order to obtain a good forecasting of the dependence among the major elements in the city of Arak. Finally, on the basis of these numerical calculations using SVR model, from the experimental data, conclusions of this study are exposed. By comparison between the predicted and the measured data it indicates that SVR model has strong potential to estimation of the heavy metals in the groundwater with high degree of accuracy.

Keywords: Groundwater; Support vector regression; Heavy metals; Arak.

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  • Kamran Mostafaei, Hamidreza Ramazi. 2017. Correlation between IP and Rs and grade data in modeling and evaluation of a copper deposit, case study: the Sarbisheh copper deposit, Iran. Journal of Tethys, Vol. 5, No. 2, 128-137.

This paper addresses the application of integrated chargeability and resistivity method and grade data in modeling and evaluation of copper deposits. We argue that the relationship between IP, Rs and grade data may be used for modeling and reserve estimation and tested this argument for Sarbisheh copper deposit that is located in eastern Iran. Geology and mineralization situation of Sarbisheh deposit was reviewed. Then geophysical survey design was carried out based on the borehole exploration data and other parameters such as geological and topographical factors. Five profiles were designed and surveyed using dipole-dipole array. The obtained data was processed and 2D sections of IP and Rs were prepared for each profile by inverting the data using the Res2dinv software. Based on the geostatistical methods, a 3D block model for IP and Rs data was constructed using Datamine Studio software and this model was evaluated by some exploratory boreholes in the study area. The relationship between IP and Rs and copper grade has been calculated based on statistical and neural network methods. In the cases that borehole data was unavailable, Cu grade was estimated using regression and multivariate regression analysis. Moreover, Cu grade was predicted by neural network at unrecognized points. Then Cu grade was calculated for each block identified by IP 3D model. Finally, a 3D block model of this copper deposit was constructed. According to the drilling tests, there is a good correlation between 3D block model and real Cu grade modeling.

Keywords: IP and Rs; Grade Estimation; 3D Modelling; Deposit Evaluation; Statistical Methods; Artificial Neural Network.

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  • Mahnaz Parvaneh Nejad Shirazi. 2017. Upper Cretaceous planktic foraminiferal from the Zagros basin (north-northwest Shiraz), Iran. Journal of Tethys, Vol. 5, No. 2, 138-153.

The litho-and biostratigraphy of Upper Cretaceous (Gurpi Formation) has been investigated within a well-exposed section at the northern limb of Pey zard anticline, Abnow area, Southern Iran. The studied section consists mainly of grey marl, calcareous pyritic shale, and argillaceous limestones. The Formation unconformably overlies Sarvak Formation and the Tarbur Formation overlies it unconformably. The samples of the section under investigation yielded rich and various planktic foraminiferal taxa, where forty planktic species belonging to eighteen genera have been recognized, the detailed foraminiferal investigation permits the recognition of the most standard biozones defined in Mediterranean regions, especially Tethyan domain. Depending on the stratigraphic distribution and relative abundance of planktic foraminiferal faunas, the studied section is subdivided into eleven biozones, which confirm a Campanian- Maastrichtian age of the Gurpi Formation. Biozones 11 (Globotruncanita elevata zone), 10 (Globotruncana ventricosa zone) and 9 (Globotruncanita calcarata zone), 8 (Globotruncanella havanensis zone), 7 (Globotruncana aegyptiaca zone) represent the Early, Middle and Late Campanian, respectively. Biozone 6 (Gansserina gansseri zone) represent Late Campanian- Early Maastrichtian, Biozones 5 (Contusotruncana contusa/Racemiguembelina fructicosa zone), 4 (Abathomphalus mayaroensis zone), 3 (Pseudoguemblina hariaensis zone), 2 (Pseudoguemblina palpebra zone), 1 (Plummerita hantkeninoides zone) suggest the Early- Late Maastrichtian, respectively.

Keywords: Campanian- Maastrichtian; Foraminifera; Shiraz; Zagros; Iran.

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