ORIGINAL_ARTICLE
Geochemistry of metapelitic rocks from the Garmichay Area, East Azerbaijan, NW Iran; protolith nature and whole rock control on metamorphic mineral assemblages
The protoliths of metamorphosed argillaceous rocks from the Garmichay area in the East Azerbaijan province of NW Iran were clay-rich sediments of reworked nature, originating most likely from an andesite to andesite-basalt source and deposited in an active continental margin tectonic setting. The protoliths of the Garmichay metapelites experienced low to moderate chemical weathering. Andalusite, cordierite, biotite, and muscovite are produced due to metamorphism under low-pressure and medium-temperature conditions. Cordierite throughout the whole assemblages is altered to the pseudomorphs pinite. Some rock samples contain both andalusite and altered cordierite as porphyroblasts, whereas others contain only andalusite or altered cordierite. Pressure and temperature estimates indicate that pressure for metamorphism was in the range 1-2.5 kbar with temperature between 500-600°C. Major oxides abundance show similar values in all analyzed samples, while concentration of some minor elements, especially zinc, show meaningful differences in rocks with different metamorphic mineral assemblages. The variations in the abundance of these minor elements may have played important role in the control of the mineral assemblages present.
https://geopersia.ut.ac.ir/article_57817_7771313106c6477277ade4d0408a7344.pdf
2016-03-01
1
18
10.22059/jgeope.2016.57817
Chemical Weathering
Metamorphism
Metapelite
Mineral Assemblage
NW Iran
Protolith
Mohssen
moazzen
moazzen@tabrizu.ac.ir
1
Department of Earth Sciences, University of Tabriz, 51664, Tabriz
LEAD_AUTHOR
Mahdi
Ghaderi
mghaderi@tabrizu.ac.ir
2
Department of Earth Sciences, University of Tabriz
AUTHOR
William
Downey
billdowney49@yahoo.com
3
Geoscience Department, University Brunei Darussalam, Tunkgu Link, Gadong BE 1410, Brunei Darussalam
AUTHOR
Hadi
Omrani
omrani.hadi@yahoo.com
4
Department of Geology, University of Gorgan
AUTHOR
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ORIGINAL_ARTICLE
Rock Brittleness Prediction Using Geomechanical Properties of Hamekasi Limestone: Regression and Artificial Neural Networks Analysis
The cold climate is a favorable parameter for the development of tension cracks and decrease of rock brittleness. Therefore, this paper attempts to investigate the Hamekasi porous limestone in order to predict the brittleness indices during freeze-thaw cycles. The freeze–thaw test was executed for one cycle including 16 h of freezing, and 8 h of thawing. The geo mechanical properties and brittleness indices (B1, B2, B3) of limestones were measured across freeze-thaw cycles from cycle 0 (fresh rock) to cycle 40. Statistical analyses, including simple and multiple regressions, were applied to identify those geomechanical parameters that are most influenced by the progression of freeze-thaw cycles and more appropriate for the brittleness prediction. Based on simple regression, all geomechanical properties including tensile strength (), uniaxial compressive strength (), P-wave velocity (Vp), porosity (n), and quick absorption index (QAI) (except dry density ()) demonstrated good correlations with brittleness index (B3). The integrated prediction of brittleness is put forward to develop some models by multiple regression (MR) and artificial neural network (ANN) with some statistic parameters (R, RMSE, VAF and ME), based on all geomechanical properties examined in this research. It is concluded that models based on n, Vp and exhibited high performance according to the obtained statistic parameters. In spite of the fact that Vp has good correlation coefficient (R) with freeze-thaw cycles, and B3 (R2= 0.74, and 0.55, respectively) in simple regression, it does not have a prominent effect on B3 in MR models. Also, parameters with low correlation coefficient in simple regression (=0.15) cannot improve the model performance in ANNmethods
https://geopersia.ut.ac.ir/article_57819_f0662191e5ee648bda73fe5969f982be.pdf
2016-03-01
19
33
10.22059/jgeope.2016.57819
ANN Models
Brittleness Indices
Freeze–Thaw Cycles
Multiple Regression Models
Porous Limestone
M.H.
Ghobadi
ghobadio@yahoo.com
1
Bu_ Ali Sina university, Hamedan, Iran
LEAD_AUTHOR
Fateme
Naseri
naseri.f91@gmail.com
2
Bu-Ali Sina university
AUTHOR
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ORIGINAL_ARTICLE
Prediction of long-term slake durability of clay-bearing rocks
A research program was conducted on different clay-bearing rocks selected from the Ilam, Sarpol-e Zahab and Tajarak regions (Iran) to predict their slaking characteristics. The new durability apparatus (nested mesh drums) separates disintegrated particles varying from > 25.4 to < 2 mm as the drums were rotated. On the basis of the particle size distribution, the disintegration ratio (DR) was used to evaluate the rock durability. A concept was proposed to describe the rock slaking characteristics under the slake durability test cycles, by using the difference in the DR values between the adjacent (ΔDR) and backward (N*) cycles. This allows the estimation of the rock durability as it is subjected to a larger number of test cycles, and hence the prediction of the effects of weathering processes. This prediction provides an effective approach when data are limited or inaccessible. Therefore, a new classification system is introduced for rock durability assessment. The test results show that the Gurpi-limey marls (G-2, G-3) and the Pabdeh-marly limestone (P-3) are classified as moderate to high durability rocks
https://geopersia.ut.ac.ir/article_57820_9ee63eeb8ffca9b2f03c7d8c5a1de463.pdf
2016-03-01
35
43
10.22059/jgeope.2016.57820
Clay-Bearing Rocks
Disintegration Ratio
durability
Slaking
Vahid
Rastegarian
heidarim_enggeol@yahoo.com
1
Postgraduate student in Engineering geology, Bu-Ali Sina University, Geology Department, Hamadan
LEAD_AUTHOR
Mojtaba
Heidari
heidarim90@yahoo.com
2
Assisstant Prof. in Engineering geology, Bu-Ali Sina University, Geology Department, Hamadan
AUTHOR
Behrouz
Rafiei
behrouzrafiei@yahoo.com
3
Associate Prof. in Sedimentary Petrology, Bu-Ali Sina University, Geology Department, Hamadan
AUTHOR
Yazdan
Mohebi
yazdan5@yahoo.com
4
PhD student in Engineering geology, Bu-Ali Sina University, Geology Department, Hamadan
AUTHOR
ASTM C127., 1998. Standard test method for density, relative density (specific gravity), and absorption of coarse aggregate.
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16
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26
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27
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28
ORIGINAL_ARTICLE
Using stream sediment data to determine geochemical anomalies by statistical analysis and fractal modeling in Tafrash Region, Central Iran
Iranian Cenozoic magmatic belt, known as Urumieh-Dokhtar, is recognized as an important polymetallic mineralization which hosts porphyry, epithermal, and polymetallic skarn deposits. In this regard, multivariate analyses are generally used to extract significant anomalous geochemical signature of the mineral deposits. In this study, stepwise factor analysis, cluster analysis, and concentration–area fractal model have been used to delineate geochemical anomalies associated with skarn mineralization, based on Au, Cu, Pb, Zn, Ag, Mo, W, Sn, and As stream sediment data. These results indicate that the Urumieh-Dokhtar belt potentially hosts Au skarn deposits. The hybrid method combining the statistical analysis and C-A fractal model is an effective tool to identify geochemical anomalies
https://geopersia.ut.ac.ir/article_57821_7cc259c5db67234eb3b098cabbcad921.pdf
2016-03-01
45
61
10.22059/jgeope.2016.57821
Au Skarn Deposit
Concentration Area Fractal Model
Iran
Statistical Analysis
Stream Sediment
Feridon
Ghadimi
drghadimi@yahoo.com
1
Department of Mining Engineering,Arak University of Technology
LEAD_AUTHOR
Mohammad
Ghomi
mghomi@aut.ac.ir
2
PhD student Candidate, Amirkabir University of Technology, Department of Mining and Metallurgical Engineering, Tehran, Iran
AUTHOR
ٍEhsan
Malaki
ehsan_malaki@yahoo.com
3
Department of Mining Engineering, Arak University of Technology, Arak,
AUTHOR
Afzal, P., Khakzad, A., Moarefvand, P., Rashidnejad, N.O., Esfandiari, B., FadakarAlghalandis, Y., 2010. Geochemical anomaly separation by multifractal modeling inKahang (GorGor) porphyry system, Central Iran .Journal of Geochemical Exploration 104, 34–46.
1
Afzal, P., FadakarAlghalandis, Y., Khakzad, A., Moarefvand, P., RashidnejadOmran, N., 2011. Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling, Journal of Geochemical Exploration 108, 220–232.
2
Afzal, P., Harati, H., FadakarAlghalandis, Y., Yasrebi, A.B., 2013. Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran, Chemie der Erde - Geochemistry, Volume 73, Issue 4, December 2013, 533-543.
3
Afzal, P., Alhoseini, S.H., Tokhmechi, B., KavehAhangarana, D., Yasrebi, A.B., Madani, N., ,Wetherelt, A., 2014. Outlining of high quality coking coal by Concentration-Volume fractal Model and Turning Bands Simulation in East-Parvadeh Coal Deposit, Central Iran. International Journal of Coal Geology 127: 88-99.
4
Aitchison, J., 1986. The statistical analysis of compositional data. London, UK: Chapman and Hall; p. 416.
5
Alavi, M., 1994. Tectonic of Zagros orogenic belt of Iran: new data and interpretations. Tectonophysics 229, 211–238.
6
Arias, M., Gumiel, P., Marti-Izard, C., 2012. Multifractal analysis of geochemical anomalies: a tool for assessing prospectivity at the SE border of the Ossa Morena Zone, Variscan Massif (Spain). Journal of Geochemical Exploration 122, 101–112.
7
Borna, B., 2004. Exploration studies of Au in TafrashZaghar, Geological Organization of Iran.
8
Carranza E.J.M. 2009. Mapping of anomalies in continuous and discrete fields of stream sediment geochemical landscapes. Geochemistry: Exploration, Environment, Analysis. 10: 171–187.
9
Carranza, E.J.M., 2010. Catchment basin modeling of stream sediment anomalies revisited: incorporation of EDA and fractal analysis. Geochemistry: Exploration, Environment, Analysis. 10: 365–381.
10
Carranza, E.J.M., 2011. Analysis and mapping of geochemical anomalies using logratiotransformed stream sediment data with censored values. Journal of Geochemical Exploration. 110: 167–185.
11
Cheng, Q., Agterberg, F.P., Ballantyne, S.B., 1994. The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration. 54: 109–130.
12
Cheng, Q., 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews. 32: 314–324.
13
Cheng, Q., Agterberg, F.P., 2009. Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Computers and Geosciences. 35: 234–244.
14
Cheng, Q., Xia, Q., Li, W., Zhang, S., Chen, Z., Zuo, R., Wang, W., 2010. Density/area power–law models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China. Biogeosciences. 7: 3019–3025.
15
Cheng, Q., Bonham-Carter, G.F., Wang, W., Zhang, S., Li, W., Xia, Q., 2011. A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Computers and Geosciences. 5: 662–669.
16
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17
Geranian, H., Mokhtari, A.R., Cohen,D.R., 2013. A comparison of fractal methods and probability plots in identifying and mapping soil metal contamination near an active mining area, Iran, Science of the Total Environment, 463-464.
18
Grunsky E.C., Drew, L.J., and Sutphin, D.M., 2009. Process recognition in multi-element soil and stream-sediment geochemical data. Applied Geochemistry 24, 1602–1616.
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Grunsky, E.C., 2010. The interpretation of geochemical survey data. Geochemistry: Exploration, Environment, Analysis. 10: 27–74.
20
Hajian, J., 1999. Geological map of Tafrash, Geological Organization of Iran.
21
Heidari, M., Ghaderi, M., Afzal, P., 2013. Delineating mineralized phases based on lithogeochemical data using multifractal model in Touzlar epithermal Au-Ag (Cu) deposit, NW Iran. Applied Geochemistry. 31: 119-132.
22
Jolliffe, T., 2002. Principal Component Analysis. Springer Verlag, New York.
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31
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34
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37
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38
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47
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48
ORIGINAL_ARTICLE
Measuring infiltration rate and hydraulic conductivity in a dry well in a thin overburden
IInfiltration rate and hydraulic conductivity are immensely important parameters for evaluating the hydrology of subsurface environments. Specifically, in disposal wells schemes and in artificial recharge plans both properties must be correctly assessed to better analyze the performance of these installations. In a new research, tanker water and rainfall runoff were injected into a 22.5 m deep well dug in a 15 m thick dry overburden and the underlying impermeable marl bedrock (7.5 m) to evaluate the feasibility of using the well to store winter runoff in the overburden for recovery in the summer. Rates of rise and fall in the hydraulic head were measured, and infiltration rate in various depths were calculated. Also, hydraulic conductivity of the overburden was calculated using particle distribution curves of the overburden samples. Infiltration rate showed close correlation with the hydraulic conductivity. Maximum infiltration rate occurs at depths of 10-11 m; depth of 10 m is the most conductive interval. New findings have come out of this experience including 1. negative correlation between maximum head generated in a specific injection event and the rate of infiltration and 2. the important role of the contact zone between bedrock and the overburden in draining the injected water
https://geopersia.ut.ac.ir/article_57822_1d7ae40c1658a686b6c17f365a9efc56.pdf
2016-03-01
63
73
10.22059/jgeope.2016.57822
Hydraulic conductivity
Infiltration rate
Recharge Well
Soil Grain Size
Water Injection
kobra
Sheikh leiveci
k.sh82@yahoo.com
1
univercity
AUTHOR
Gholam Abas
Kazemi
kazemi.gholamabas@gmail.com
2
Faculty of Earth Sciences,University of Shahrood, Shahrood, Iran
LEAD_AUTHOR
NoorAli
ِDamough
ndamough@yahoo.com
3
Khuzestan Water and Power Authority, Ahvaz, Iran
AUTHOR
Cahill, M., Derek, C., Sowles, M,. 2011. Dry wells. Oregon State University, From Oregon Sea Grant, Corvallis, 8 pp.
1
Carman, P.C., 1937. Fluid flow through granular beds. Trans. Inst. Chem. Eng. 15:150 pp.
2
Carman, P.C., 1956. Flow of gases through porous media. Butterworths scientific publications, London 6-12.
3
Carrier, W.D., 2003. Goodbye, Hazen; hello, Kozeny-Carman. ASCE Journal of Geotech Geoenviron Eng. 129 (11): 1054–1056.
4
Cheng, C., Chen, X.H., 2007. Evaluation of methods for determination of hydraulic properties in an aquifer-aquitard system hydro logically connected to river. Hydrogeol Journal 15: 669–678.
5
Connecticut Department of Environmental Protection, 2004. Connecticut Storm water Quality Manual. http://dep.state.ct.us
6
Diamond, J., Shanley, T., 1998. Infiltration rate assessment of some major soils. End of Project Report, Armis 4102, Teagasc, Dublin.
7
Environmental Protection Agency, 2013. Evaluation of dry wells and cisterns for storm water control: Millburn Township, NJ. EPA 600/R–12/600.
8
Freeze, R.A., Cherry, J.A., 1979. Groundwater. Prentice Hall, Englewood Cliffs, NJ, 604 pp.
9
Hazen, A., 1892. Some physical properties of sands and gravels. Massachusetts State Board of Health, 24th annual report, Boston, pp 539–556.
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Heidari, M.M., 2011. Determination of permeability coefficient based on distribution curve. Third national cconference on irrigation and drainage network, Ahvaz, March 1-3, 2011.
11
Ishaku, J.M., Gadzama, E.W., Kaigama, U., 2011.Evaluation of empirical formulae for the determination of hydraulic conductivity based on grain-size analysis. Journal of Geology and Mining Res 3(4): 105–113.
12
Johnson, A.I., 1963. A field method for measurement of infiltration. U. S, Geological Survey Water Supply Paper 1544–F.
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Kozeny, J., 1927. Uber kapillare leitung des wassers in boden: Sitzungsber [On capillary flow of water in soil], Sitz Ber Akad Wiss Wien, Vienna, 136: 271–306.
14
Massmann, J., 2004. An approach for estimating infiltration rates for storm water infiltration dry wells. Washington State Department of Transportation Technical Monitor, 68 pp.
15
Mousavi Harami, A., 2007. Sedimentology. 8th edition, Razavi Ghods Astan, Mashhad, 474 pp. (In Persian).
16
McWhorter, D.B., Sunada, D.K., 1977. Groundwater Hydrology and Hydraulics. Water Resources Publications, Fort Collins, CO, 290 pp.
17
Odong, J., 2007. Evaluation of empirical formulae for determination of hydraulic conductivity based on grain-size analysis. Journal Am Sci. 3(3): 54–60.
18
Philips, C.E., Kitch, W.A., 2011. A review of methods for characterization of site infiltration with design recommendations. In: Proceedings of 43rd Symposium on Engineering Geology and Geotechnical Engineering, Las Vegas, Nevada.
19
Ren, Lu., Yan, Xu., 2014. The streambed sediment grain size analysis and empirical formula of vertical hydraulic conductivity of Wei River. Journal of Applied Sciences and Engineering Research. 3(2): 411–421.
20
Schwartz, F.W., Zhang, H., 2003. Fundamentals of Groundwater, John Wiley & Sons, 583 pp.
21
Steiakakis, E., Gamvroudis, C., Alevizos, G., 2012. Kozeny-Carman equation and hydraulic conductivity of compacted clayey soils, Geomaterials 2: 37–41.
22
Terzaghi, K., Peck, R.B., 1964. Soil Mechanics in Engineering Practice. Wiley, New York.
23
Todd, D.K., Mays, L.W., 2005.Groundwater Hydrology, Wiley, Hoboken, NJ, 636 pp.
24
Van Hoorn, J.W.,1979. Determining hydraulic conductivity with the inversed auger hole and infiltrometer methods. In: Wesseling, J. (ed.), Proceedings of the International Drainage Workshop, ILRI Publication 25, Wageningen, The Netherlands, ILRI, pp. 150–154.
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Vukovic, M., Soro, A., 1992. Determination of hydraulic conductivity of porous media from grain-size composition. Water Resources Publications, Littleton, Colorado, 83 pp
26
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27
ORIGINAL_ARTICLE
Groundwater potentiality through Analytic Hierarchy Process (AHP) using remote sensing and Geographic Information System (GIS)
Nowadays the use of remote sensing and Geographic Information System (GIS) is one of the most powerful cost effective tools to identify and discover the available groundwater resources. In this paper, Lithological Units, Lineaments, Slope, Topography, Drainage density, Vegetation and Isohyets lines have been achieved by stations and through remote sensing and GIS techniques. All layers of different classes were initialized through hierarchical analysis to potential areas of groundwater and after modeling in GIS Environment, Mahdishahr zone was classified according to the potential groundwater basins. The results show that in the 7 reviewed criteria by the expertise and Analytical Hierarchy Process, geological criterion and lineaments with the relative importance of 0.33 and 0.22, respectively, have greatest importance and priority for potentiality of groundwater in the region. Also in the studied area, Quaternary Alluvium consisted of old and new terrace and river sediments have the highest relative importance and desirability and terrace resources and the elevated old and low height new Foothill Alluvial fans are considered as good potential areas of groundwater. Shemshak Sandstone formations and Barut with a high density lineaments and Tizkouh formation with the thick layers of limestone and Barut are also good areas for groundwater
https://geopersia.ut.ac.ir/article_57823_c30089d1ca5d750e2faa4fb74dc5d8f3.pdf
2016-03-01
75
88
10.22059/jgeope.2016.57823
AHP
GIS
Groundwater
remote sensing
Amir
Hatefi Ardakani
hatefiati@gmail.com
1
Semnan University
LEAD_AUTHOR
Mohamad
Ekhtesasi
mr_ekhtesasi@yahoo.com
2
Yazd university
AUTHOR
Abdalla, F., 2012. Mapping of groundwater prospective zones using remote sensing and GIS techniques: A case study from the Central Eastern Desert, Egypt. Journal of African Earth Sciences. 70: 8–17.
1
Ataee, M., 2010. Multi-criteria decision, Shahrood University Press, 333 p..
2
Bagyaraj, M., Ramkumar, T., Venkatramanan, S., Gurugnanam, B., 2013. Application of remote sensing and GIS analysis for identifying groundwater potential zone in parts of Kodaikanal Taluk, South India. Frontiers of Earth Science. 7 (1): 65-75.
3
Bouaziz, M., Leidig, M., Gloaguen, R., 2011. Optimal parameter selection for qualitative regional erosion risk monitoring: A remote sensing study of SE Ethiopia, GEOSCIENCE FRONTIERS 2(2): 237-245.
4
Chaabouni, R., Bouaziz, S., Peresson, H., Wolfgang, J., 2012. Lineament analysis of South Jenein Area (Southern Tunisia) using remote sensing data and geographic information system. The Egyptian Journal of Remote Sensing and Space Sciences. 15: 197–206.
5
Chenini, I., Mammou, A.B., 2010. Groundwater recharge study in arid region: An approach using GIS techniques and numerical modeling, Computers & Geosciences 36: 801–817.
6
Dar, I A., Sankar, K., Dar, M A., 2010. Remote sensing technology and geographic information system modeling: An integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin, Journal of Hydrology. 394 : 285–295.
7
Dar, I A., Sankar, K., Dar, M A., 2011. Deciphering groundwater potential zones in hard rock terrain using geospatial technology. Environ Monit Assess. 173: 597–610.
8
Ganapuram, S., Kumar, G., Krishna, I., Kahya, E., Demirel, M., 2008.Mapping of groundwater potential zones in the Musi basin usingremote sensing and GIS. Advances in Engineering Software. 40: 506-518.
9
Ganapuram, S., Vijaya Kumar, G.T., Murali Krishna, I.V., Kahya, E., 2009. Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS, Advances in Engineering Software. 40: 506–518.
10
Ghodsipoor, H., 2009. AHP, Amir Kabir University Press, 236 p.
11
Ishizaka, A and Labib, A., 2009. Analytic Hierarchy Process and Expert Choice: Benefits and Limitations, ORInsight, 22(4): 201–220.
12
Kheirkhah Zarkesh, M., 2005. DSS for floodwater site selection in Iran, PhD Thesis, Wageningen University. 273 pp.
13
Khodaei, K., Nassery, H, R., 2013. Groundwater exploration using remote sensing and geographic information systems in a semi-arid area (Southwest of Urmieh, Northwest of Iran)., Arab J Geosci. 6: 1229–1240
14
Krishnamurthy, J., Mani, A., Jayaraman, V., Manive, M., 2000. Groundwater resources development in hard rock terrain an approach using remote sensing and GIS techniques, International Journal of Applied Earth Observation and Geoinformation. 2 ( 3/4): 204–215.
15
Kumar, U., Kumar, B., Mallick, N., 2013. Groundwater Prospects Zonation Based on RS and GIS Using Fuzzy Algebra in Khoh River Watershed, Pauri-Garhwal District, Uttarakhand, India. Global Perspectives on Geography (GPG) .1 (3): 37-45.
16
Madi, K., Zhao, B., 2013. Neotectonic belts, remote sensing and groundwater potentials in the Eastern Cape Province, South Africa. International Journal of Water Resources and Environmental Engineering. 5(6): 332-350.
17
Magesh, N.S., Chandrasekar, N., Soundranayagam, J.P., 2012. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. GEOSCIENCE FRONTIERS 3(2) (2012) 189e196.
18
Malczewski, J., 2006. GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science. 20(7): 703–726.
19
Oh, H J., Kim, U S., Choi, J K., Park, E., Lee, S., 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea., Journal of Hydrology. 399 : 158–172.
20
Oswald, M., 2004. Implementation of the analytical hierarchy process with VBA in ArcGIS. Computers and Geosciences. 30: 637–646.
21
Rai, B., Tiwari, A., Dubey, V.S., 2005. Identification of groundwater prospective zones by using remote sensing and geoelectrical methods in Jharia and Raniganj coalfields, Dhanbad district, Jharkhand state. Journal of Earth System Science. 114 ( 5): 515–522.
22
Roy, I. G., 2014., Multiscale analysis of high resolution aeromagnetic data for groundwater resource exploration in an arid region of South Australia., Journal of Applied Geophysics..105: 159–168.
23
Saaty, T. L., 2000. Fundamentals of Decision Making and Priority Theory. 2nd ed. Pittsburgh, PA: RWS Publications, p.11.
24
Saaty, T. L., 2002. Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research. 145: 85-91.
25
Saha, D., Dhar, Y R, Vittal, S S., 2010. Delineation of groundwater development potential zones in parts of marginal Ganga alluvial plain in south Bihar, eastern India. Environ Monitor, 165 (1–4): 179–191.
26
Sener, E., Davraz, A., Ozcelik, M., 2005. An integration of GIS and remote sensing in groundwater investigations: a case study in Burdur, Turkey. Hydrogeology Journal. 13: 826- 834.
27
Sharma, M. P., Kujur, A., 2012. Application of Remote Sensing and GIS for groundwater recharge zone in and around Gola Block, Ramgargh district, Jharkhand, India. International Journal of Scientific and Research Publications. 2( 2): 1-6.
28
Singh, A. K., Prakash., S. R., 2003. An integrated approach of remote sensing, geophysics and GIS to evaluation of groundwater potentiality of Ojhala sub watershed, Mirzapur district, UP, India. Map India conference.
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Srinivasa Rao, Y.& Jugran, K. D., 2003. Delineation of groundwater potential zones and zones of groundwater quality suitable for domestic purposes using remote sensingand GIS. Hydrogeology Science Journal. 48 (5): 821–833.
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Teeuw, R. 1995. Groundwater exploration using remote sensing and a low-cost geographic information system. Hydrogeology Journal. 3: 21-30.
31
Vasanthavigar, M., Srinivasamoorthy, K., Vijayaragavan, K., Gopinath, S., Sarma, S., 2011. Groundwater potential zoning in Thirumani- muttar sub-basin Tamilnadu, India—a GIS and remote sensing approach, geo-spatial. Inf Sci. 14 (1): 17–26.
32
ORIGINAL_ARTICLE
Evaluating hydrogeochemistry and turbidity problem of a carbonate aquifer, Shiraz, Iran
Important karstic aquifers exist in west and southwest of Iran. Mansour-Abad Karstic region is located in Shiraz, southwest of Iran. It supplies the drinking water for the whole area by 4 pumping water wells, some of which have water turbidity problem. The present research aims to assess the hydrogeochemistry and turbidity problem in the karstic water production wells. The EC varies between 703 (in well No.1) and 1096 µmohs/cm (in well No.4). All water wells have similar ion concentration trend, indicating the same origin. The dissolution of gypsum during dedolomitization process induces the transformation of dolomite to calcite in the study area, especially in well No.4. The concentrations of most trace elements in the study area are lower than the standard value. Bacteriological water parameters are outside the accepted limits recommended by WHO for drinking water. Wells No. 1, 2 and 3 have turbidity values greater than limited values for drinking water which is about 5 TU; therefore, only well No.4 is used as drinking water resource in the study area. Most probably, interference of clay mineral layers with groundwater flow is one of the main causes of turbidity in some wells.
https://geopersia.ut.ac.ir/article_57824_c9937ec2a85631f9b833a33dd07f09c7.pdf
2016-03-01
89
103
10.22059/jgeope.2016.57824
drinking water
hydrochemistry
Karst aquifer
microorganism
Turbidity
Gholam Hossein
Karami
r.bagheri@shahroodut.ac.ir
1
Faculty of Earth Sciences, Shahrood University of Technology, Shahrood
AUTHOR
Rahim
Bagheri
rahim.bagheri86@gmail.com
2
Faculty of Earth Sciences, University of Shahrood, Shahrood, Iran
LEAD_AUTHOR
Farzaneh
Gharehzaeh
r.bagheri121@gmail.com
3
Faculty of Earth Sciences, Shahrood University of Technology
AUTHOR
Alavi, M., 2004. Regional stratigraphy of the Zagros fold-thrust belt of Iran and its pro-foreland evolution. Am. J. Sci. 304: 1–20.
1
Auckenthaler, A., Huggenberger, P., 2003. Pathogene Mikroorganismen im Grund- und Trinkwasser. Transport Nachweismethoden Wassermanagement (Pathogenic microorganisms in groundwater and drinking water. Transport, analytical methods and water management), 196.
2
Bagheri, R., Nadri, A., Raeisi, E., Eggenkamp, H.G.M., Kazemi, G.A., Montaseri, A., 2014. Hydrochemical and isotopic (δ18O, δ2H, 87Sr/86Sr, δ37Cl and δ81Br) evidence for the origin of saline formation water in a gas reservoir, Chemical Geology. 384: 62–75.
3
Bagheri, R., Nadri, A., Raeisi, E., Kazemi, G.A., Eggenkamp, H.G.M., Montaseri, A., 2013. Origin of brine in the Kangan gasfield: isotopic and hydrogeochemical approaches. Environ Earth Sci. DOI 10.1007/s12665-013-3022-7.
4
Bagheri, R., Nadri, A., Raeisi, E., Shariati, A., Mirbagheri, M., Bahadori, F., 2013. Chemical evolution of a gas-capped deep aquifer, southwest of Iran. Environ Earth Sci. doi:10.1007/s12665-013- 2705-4.
5
Bordenave, M.L., 2008. The origin of Permo-Triassic gas accumulations in the Iranian Zahros fold belt and contiguous offshore areas: a review of the Paleozoic petroleum system. J Petrol Geol. 31: 3–42.
6
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7
Chadha, D.K., 1999. A proposed new diagram for geochemical classification of natural water and interpretation of chemical data, Hydrogeology Journal. 7: 431–439.
8
Drew, D.H.H., 1999. Karst Hydrogeology and Human Activities impact. Consequences and Implications International Contributions to hydrogeology, 20.
9
Dupont, M.F.B., 2007. Using turbidity dynamics and geochemical variability as a tool for understanding the behavior and vulnerability of a karst aquifer. Hydrogeology Journal. 689 –704.
10
Falcon, N.L., 1967. Southern Iran, Zagros Mountains. In: Spencer AM (ed) Mesozoic-Cenozoic orogenic belts. Geological Society London. 4: 199- 211 (Spec Publ)
11
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13
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Knauth, L.P., 1988. Origin and mixing history of brines, of Palo Duro Basin, Texas, USA. Appl Geochem. 3: 455–474
16
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17
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19
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28
WHO. 1983. guidlines to drinking water quality world helth organisation. geneva.
29
ORIGINAL_ARTICLE
The effect of estimation methods on fractal modeling for anomalies’ detection in the Irankuh area, Central Iran
This study aims to recognize effect of Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) estimation methods for separation of geochemical anomalies based on soil samples using Concentration-Area (C-A) fractal model in Irankuh area, central Iran. Variograms and anisotropic ellipsoid were generated for the Pb and Zn distribution. Thresholds values from the C-A log-log plots based on the estimation methods revealed the presence of various geochemical anomalies within estimation variances which were compared in both methods. The comparison among the estimation variances for different geochemical anomalies based on the C-A fractal model indicated that the estimation variance is less in the OK method especially for extremely and highly Zn-Pb anomalies. The estimated variances for different Zn anomalies via OK and C-A fractal method are lower than fractal modeling obtained by IDW estimation. However, extremely and highly Pb anomalies due to OK method have estimation variances lower than IDW method. Based on the results, main Zn and Pb anomalies are situated in the NW part of the area.
https://geopersia.ut.ac.ir/article_57825_1df95282b8f1705e47f059849d8617d0.pdf
2016-03-01
105
116
10.22059/jgeope.2016.57825
Concentration Area (C-A) Fractal Model
Estimation variance
Inverse Distance Weighted (Idw)
Irankouh
Ordinary Kriging (OK)
Peyman
Afzal
peymanafzal@yahoo.com
1
بخش مهندسی معدن دانشگاه آزاد واحد تهران جنوب
LEAD_AUTHOR
Mehdi
Rezaie
me.re2004@yahoo.com
2
Department of Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Afzal, P., Fadakar Alghalandis, Y., Khakzad, A., Moarefvand, P., Rashidnejad Omran, N., 2011. Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J Geochem Explor. 108: 220–232.
1
Afzal, P., FadakarAlghalandis, Y., Moarefvand, P., RashidnejadOmran, N., AsadiHaroni, H., 2012. Application of power-spectrum–volume fractal method for detecting hypogene,supergene enrichment, leached and barren zones in Kahang Cu porphyry deposit, Central Iran.J Geochem Explor. 112: 131-138.
2
Afzal, P., Khakzad, A., Moarefvand, P., Rashidnejad Omran, N., Esfandiari, B., Fadakar Alghalandis, Y., 2010. Geochemical anomaly separation by multifractal modeling in Kahang (GorGor) porphyry system, Central Iran. J Geochem Explor . 104: 34–46.
3
Afzal, P., Harati, H., Fadakar Alghalandis, Y., Yasrebi, A.B., 2013. Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran. Chemie der Erde. 73: 533– 543.
4
Agterberg, F.P., 1995.Multifractal modeling of the sizes and grades of giant and supergiant deposits. Int Geol Rev. 37: 1–8.
5
Aramesh Asl, R., Afzal, P., Adib, A., Yasrebi, A.B., 2015. Application of multifractal modelling for the identification of alteration zones and major faults based on ETM+ multispectral data. Arab J Geosci. 8: 2997–3006.
6
Bayraktar, H., Turalioglu, F.S., 2005. Kriging-based approaches for locating a sampling site-in the assessment of air quality. SERRA. 19: 301-305.
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8
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9
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51
ORIGINAL_ARTICLE
Investigating the paleoecological characteristics of Abtalkh Formation at Bahadorkhan Section (Central Kopet-Dagh) based on planktonic and benthic foraminifera
Study of a late Late Santonian to Late Campanian hemipelagic succession from Abtalkh Formation at the Bahadorkhan section (Central Kopet-Dagh) enabled us to verify paleoecology changes based on planktonic and benthic foraminifera assemblage. Bahadorkhan section is consisted of calcareous shale, lime marl, marl, and a few dispersed chalky limestone beds. Upper and lower boundaries of Abtalkh Formation are conformable with Abderaz and Neyzar formations. Since foraminifera are proper tools for paleoecological investigations, we used them in three methods to analyze the changes of paleobathymetry: 1) Van der Zwaan’s equations of determining depth and method (the ratio of planktonic foraminifera to benthic), 2) Leckie's morphotype model, and 3) investigating the changes of benthic foraminifera and the ratio of agglutinated benthic foraminifera to the utilized calcareous benthic foraminifera. Trox model (the ratio of epifaunal to infaunal (Ep/In)) was also used with the goal of identifying oxygen level and nutrients. According to the statistical counting, four stages of changes in depth and environmental conditions in this section with age range of late Late Santonian- Late Campanian were observed. According to the analysis, in the Early Campanian, P/B ratio and infaunal benthic are high which indicates the high level of water and eutrophic environment. In such an environment, nutrient and oxygen levels are respectively high and low. Then, at end of the Early Campanian and the beginning of Middle Campanian, water level decreases, and the environment moves toward oligotrophic conditions. During Middle Campanian and the early Late Campanian, water level increases again. Finally, during Late Campanian and along with the change of lithology to Neyzar Formation sandstones, we will witness the decrease of P/B ratio, water level drop, oxygen level increase, and nutrient decrease.
https://geopersia.ut.ac.ir/article_57826_2a9359e5d45bda5897e2f8f220ec8d69.pdf
2016-03-01
117
128
10.22059/jgeope.2016.57826
Abtalkh Formation
Kopet-Dagh basin
Paleoecology
Planktonic and Benthic Foraminifera
Atusa
Honarmand
atusa.honarmand@yahoo.com
1
ferdowsi university of Mashhad
LEAD_AUTHOR
Mohammad
Vahidinia
vahidinia@ ferdowsi.um.ac.ir
2
ferdowsi University of Mashhad
AUTHOR
Abbas
Ghaderi
aghaderi@um.ac.ir
3
ferdowsi University of Mashhad
AUTHOR
Aghanabati, A., 2004. Geology of Iran. Geological survey of Iran, 568 p.
1
Ahmadi, M., Vahidinia, M., Ashouri, A.R., 2013. Paleoecology of Abtalkh Formation based on planktonic and benthic foraminifera in Padeha section, Kopet-Dagh Basin. Sedimentary Facies. 5(2): 119-134 (in Persian).
2
Alegret, L., & Thomas, E., 2001. Upper Cretaceous and Lower Paleogene benthic foraminifera from northeastern Mexico. Micropaleontology. 47: 269-316.
3
Alegret, L, Molina, E, Thomas, E., 2003. Benthic foraminiferal turnover across the Cretaceous/Paleogene boundary at Agost (southeastern Spain): paleoenvironmental inferences. Marine Micropaleontology. 48: 251-279.
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Nyazi, M., Vahidinia, M., Ashouri, A.R., 2013. Microbiostratigraphy and Paleoecology of the Abtalkh Formation based on foraminifera at Qareh-Sou section (Southwest of Kalat-e-Naderi, NE Iran). Sedimentary Facies 6(1), 95-114 (in Persian).
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33
ORIGINAL_ARTICLE
Calcareous nannofossil assemblages of the Late Campanian- Early Maastrichtian form Gurpi Formation (Dezful embayment, SW Iran): Evidence of a climate cooling event
A succession of Late Campanian- Early Maastrichtian is analyzed from Gurpi Formation with regard to the calcareous nannofossils. Correlation Matrix was applied for the first time to the entire nannofossil assemblage to reconstruct environmental conditions. A detailed quantitative calcareous nannofossil analyses is performed on samples in order to further investigate the climate events, and interpret changes of surface water temperature. The calcareous nannofossil assemblage is divided into 3 groups as cold, cool and warm water taxa. Although cold water taxa (Ahmuellerella octoradiata and Gartnerago segmentatum) are rare (less than 1%), cool (Eiffellithus turriseiffelii, Prediscosphaera cretacea, Micula staurophora, Zeugrhabdotus spp., Arkhangelskiella cymbiformis, Biscutum constans, Tranolithus orionatus and Lucianorhabdus cayeuxii) and warm (Watznaueria barnesae, Uniplanarius trifidus, Uniplanarius sissinghii, Ceratolithoides spp. and Broinsonia spp.) water taxa are more frequent. The number of warm water taxa is higher than the number of cool water taxa and a reverse trend can be observed between them. Concerning the temperature index (TI), four important trends of climate variability from warmer to cooler phases (two cooling phases and two warming phases) have been documented at the studied interval. According to the recorded data, two pronounced cooling event are observed at Late Campanian and Early Maastrichtian, respectively.
https://geopersia.ut.ac.ir/article_57827_4f5852b2a67eab406c8628dd0e9e5587.pdf
2016-03-01
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148
10.22059/jgeope.2016.57827
Calcareous Nannofossil
Cooling Event
Gurpi Formation
Iran
Late Campanian
Early Maastrichtian
Zagros basin
Azam
Mahanipour
a_mahanipour@uk.ac.ir
1
Department of Geology, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
Amineh
Najafpour
a.najafpur@yahoo.com
2
Department of Geology, Faculty of Science, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
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ORIGINAL_ARTICLE
Appendix 1 List of fossils, described as new by Senowbari-Daryan et al. from the Permian and Mesozoic of Iran
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10.22059/jgeope.2016.57828
Permian
Triassic
Jurassic
Cretaceous
Fossil
Sponges
Gastropods
Foraminifera
Algae
Iran
Baba
Senowbari-Daryan
basendar@gzn.uni-erlangen.de
1
Geozentrum Nordbayern, Department of Palaeoenvironment, University of Erlangen-Nürnberg, Erlangen, Germany
AUTHOR