نشریه زمین شناسی کاربردی پیشرفته
سال نهم شماره 30 (زمستان 1397)

Irankuh Pb-Zn mining district is located Malayer-Isfahan Metallogenic Belt and it is MVT type. Mineralization is hosted by carbonate rock (mostly coarse-grained dolostone) and minor shale-siltstone as epigenetic. Mineral assembelages are sphalerite, galena, pyrite, and minor chalcopyrite associated with dolomite, ankerite, quartz, organic matter, calcite, and barite. Dolomitization is the most important alteration, which is occurred as karst and dissolution cavities filling, fossil replacement, veinlet, and fault berrecia cement. Silicification is mostly occurred at clastic host rock as vein-veinlet and open space fillinig. Geochemically, ore-bearing fluid is Fe- and Mn-rich, which is formed Fe-rich dolomite and ankerite at carbonate host rock. In clastic host rock, pyrite is mainly occurred due to reaction of iron of fluid and sulfur. In addition, As, Sb, Cd, and Cu contents of both carbonate and clastic host rocks are high that these elements can be exploratory tracker. This study in Irankuh mining district can be pattern of exploration for hidden sources in this area and similar deposits in Iran and the world.

 Considering the importance of rivers as part of freshwater resources and their role in meeting the needs of agriculture, industry, urban populations, etc., monitoring and predicting the quality of these water resources is essential. These water sources are affected by numerous factors due to their different geological and environmental conditions and their qualitative status also undergoes dramatic changes. However, the quality monitoring of these abundant water resources on the planet's surface is not feasible and requires the use of advanced and powerful tools (Bagherian Marzouni et al., 2014). Due to its capabilities, satellite remote sensing can be used as one of these tools in monitoring water quality and will accurately detect the spatial and temporal changes of these water sources (Bonansea et al., 2015). So far, in many studies, the capabilities of remote sensing satellites to estimate surface water quality parameters has been evaluated, and in most of them, acceptable results have been obtained indicating the ability of this technology in the issue as mentioned above. Among these studies, we can mention laili et al. (2015), in which in a small section of Indonesian waters, have figured out a new regression algorithm between Landsat 8 and groundwater quality parameters. Toming et al. (2016) in a study using satellite images of Sentinel-2 on the water quality of the lakes in Estonia, could find a good correlation between the satellite band proportions and ground. The purpose of this research is to establish a relation between satellite images of Sentinel-2 A and two quality water parameters with a suitable model along the Karun and Dez River. For this purpose, firstly suitable spectral indices were extracted from them by applying the necessary processing on satellite images. In the next step, optimal relationships between extracted indices and water quality parameters are established using different models. Finally, using models with higher accuracy in terms of modeling, the dispersion map of each parameter in the length of the Karun River is provided.The purpose of this research is to establish a relation between satellite images of Sentinel-2 A and 2 quality water parameters with a suitable model along the Karun and Dez River. For this purpose, firstly suitable spectral indices were extracted from them by applying the necessary processing on satellite images. In the next step, optimal relationships between extracted indices and water quality parameters are established using different models. Finally, using models with higher accuracy in terms of modeling, the dispersion map of each parameter in the length of the Karun River is provided. 

This study presented in eight steps as below: Step 1: Preparation of ground data and satellite imagery:The ground data used in this study is the measured data at the water quality sampling stations. The data included information on these quality parameters that were used from 2015 to early 2017 in ten stations. Step 2: Recording the value of the reflection bands at the ground measurement stations:In order to implement this research, satellite images of sentinel-2 and groundwater quality parameters were collected and measured at the same time from the study area. In this step, the values of measured water quality parameters were also sorted by date and sampling stations were prepared in separate files. Step 3: Analyze the initial sensitivity and determine the bands that have a stronger connection with each water quality parameter  

Table 1: result of sensitivity analysis for sentinel-2 bands TDS Turbidity EC pH Hco3 So4 Cl Na K Mg Ca Parameter Type Band Number 0.376 0.472 0.296 0.384 0.493 0.219 0.338 0.279 0.312 0.217 0.294 B2 0.379 0.303 0.325 0.307 0.238 0.239 0.268 0.238 0.179 0.291 0.217 B3 0.352 0.237 0.283 0.278 0.260 0.232 0.225 0.269 0.165 0.196 0.269 B4 0.346 0.332 0.274 0.428 0.315 0.214 0.256 0.294 0.256 0.275 0.313 B5 0.401 0.208 0.248 0.322 0.294 0.278 0.253 0.249 0.210 0.268 0.239 B6 0.403 0.257 0.227 0.299 0.273 0.281 0.258 0.256 0.203 0.283 0.210 B7 0.263 0.285 0.301 0.346 0.198 0.245 0.231 0.227 0.184 0.209 0.224 B8 0.422 0.306 0.316 0.309 0.241 0.275 0.251 0.244 0.195 0.299 0.212 B8a 0.249 0.205 0.267 0.325 0.238 0.273 0.233 0.287 0.205 0.209 0.158 B11 0.391 0.265 0.214 0.310 0.282 0.293 0.254 0.247 0.270 0.244 0.178 B12

  Step 4: Calculating spectral indices and selecting spectral indicators with higher correlation Step 5: Secondary Sensitivity Analysis and Selection of Spectral Indicators with Stronger Connections   In the next step, by applying the sensitivity analysis method, the relationship between each spectral indicator and water quality parameters was calculated (Table 2).

  Table 2. Result of sensitivity analysis for spectral indicator TDS Turbidity EC pH So4 Hco3 Cl Na K Mg Ca Parameter Type   Spectral Indexes 0.455 0.580 0.470 0.407 0.534 0.260 0.482 0.535 0.364 0.511 0.366 Single bans reflectance 0.465 0.659 0.563 0.516 0.599 0.501 0689 0.688 0.670 0.532 0.666 ( 14BmaxBmin)"> 0.436 0.740 0.452 0.633 0.562 0.681 0.701 0.598 0.600 0.485 0.677 ( 14BminBmax)"> 0.396 0.702 0.438 0.720 0.527 0.581 0.758 0.669 0.656 0.506 0.740 ( 14Bmax-BminBmax+Bmin)">   Step 6: Normalization of data Step 7: Modeling the relationship between satellite images and groundwater quality parameters:In order to model the relationship between satellite images and groundwater quality parameters, and based on the results of previous steps, the normalized values derived from the calculation of spectral indices were determined as inputs and water quality parameters were determined as outputs of ANN and ANFIS models. Step 8: Providing water dispersion map for water quality parameters:At this step, the modeling process was repeated with the transformation of ANN and ANFIS models until each model was accurately mapped the relationship between water quality parameters. 

Table 3 shows the evaluation result of used model in this study.   Table 3. Evaluation result of ANN and ANFIS model for water quality parameters. Hco3 So4 Cl Na K Mg Ca WQPT ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN Error Type 0.497 0.315 0.0871 0.691 0.266 0.263 0.229 0.264 0.136 0.0709 0.127 0.397 0.120 0.279 RE 0.164 0.131 0.0587 0.311 0.0959 0.0748 0.102 0.079 0.126 0.0605 0.077 0.157 0.115 0.194 RMSE   Figures 1– 4 show the concentration map of TDS and turbidity parameters studied in this research in Karun River in Dez and Karkheh dam and the Karun River from Malasani section to the Farsiat station.                                  (a)                               (b) Figure 1: Concentration map of TDS parameter in a) Karkheh and b) Dez dam.   Figure 2. Concentration map of TDS parameter Karun River.     (a)   (b) Figure 3. Concentration map of turbidity parameter in A) Karkheh and b) Dez dam.   Figure

map of turbidity parameter Karun River.   4- Conclusion In this study, two models of ANN and ANFIS computational intelligence models were used to model the relationship between satellite images of Sentinel-2 and two quality parameters of water along the Karun River. The results of this study indicate the high level of remote sensing ability to monitor water quality, similar to other studies; as this is well understood in previous researches, remote sensing technology can be widely used to monitor other surface water resources of Khuzestan province.   References Bagherian.Marzouani.M., Akhoundali.A.M., Moazed.H., Jaafarzadeh.N., Ahadian.J., Hasoonizadeh.H., 2014, Evaluation of Karun River Water Quality Scenaros Using Simulation Model Results, International Journal of Advanced Biological and Biomedical Research, Volume 2, Issue 2, 339-358. Bonansea.M., Claudia Rodriguez.M., Pinotti.L., Ferrero.S., 2015, Using multi-temporal Landsat imagery and linear Models for assessing water quality parameters in Rio Tercero reservoir (Argentina), Journal of Remote Sensing of Environment, 158, 28–41. Laili, N., Arafah, F., Jaelani, L., Subehi, L., Pamungkas, A., Koenhardono, E., & Sulisetyono, A. 2015. Development of water quality parameter retrieval algorithms for estimating total suspended solids and chlorophyll-a concentration using landsat-8 imagery at poteran island water. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(2), 55. Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, P., Nõges T., 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery, Remote Sensing journal, 8, 640.

The studied area is located in the westernmost part of Mazandaran province around Ramsar city and is part of the southern coast of the Caspian Sea. Elika, Nesen, Ruteh, Mobarak, Dorood, and Javaherdeh formation are exposed (Baharfiroozi et al., 2000). The general strike of geological structures of the area is northwest-southeast and dominated by abundant faults (Alavi, 1996). Studied springs of this area are including Kachanak, Katalom, Rishboraz Darreh, Namak Darreh, and Giash. Kachanak and Katalom springs are located in the plain areas, and Rishboraz Darreh, Namak Darreh, and Giash are in highlands of Ramsar city.
Hydrogeologically, the aquifer of the study area is unconfined, and its hydraulic gradient is toward the sea. In this research, the quality of water of springs has been considered because it represents not only the quality of underground water but also the people of the area drinks it, and it can affect the native’s health. So, the aim of this research is the specification of the hydrogeochemical properties and define the water quality of 5 selected springs around the Ramsar city for drinking.

The springs were sampled according to the required standards using polyethylene bottles in the last days of summer. Dual-purpose conductor and acidity measured electrical conductivity, and total dissolved solids were measured by pH meter. BOD was estimated in five days using incubator (equipped with oxygen meter sensor) in 200C temperature. The alkalinity and chloride contents were measured through titration. Alkalinity was obtained by addition of Phenolphthalein and Methyl Orange to the samples and performing titration until reaching the final point (orange-pink color) and presented in milligram per liter of calcium carbonate. Bicarbonate also was measured by using calcium carbonate contents and applying related coefficients. Potassium chromate was used as a marker for measuring centration of chloride contents in the titration process. Nitrate and sulfate were evaluated using ultraviolet (UV) and spectrophotometry methods, respectively. Major and trace elements concentration obtained through inductively coupled plasma mass spectrometry (ICP-MS) in the laboratory of Act Labs Company in Canada. Hydrogeochemical processing of the samples was done by AqQa software.

Data processing results indicated that except Kachanak spring (which is of Si-HCO3 type), all the springs are of Si-Cl water type (table 1). The plot of hydrogeochemical data of the studied springs on the Piper diagrams suggested that alkaline earth metals (Ca+, Mg2+) are more than alkaline elements (K+, Na+) and anions of strong acids (SO2-4) are more than weak acids ones (HCO-3), and noncarbonate hardness is over 50%. Water quality also considered through comparing of physicochemical parameters of the springs with different standards (Table 1).
Table 1- Correlation the physiochemical parameters of the springs with different standard
---------------

Measured parameters indicated that according to the standards of World Health Organization (WHO, 2011) all the springs (except Katalom which is somewhat acidic) are in permissive and desirable limit in respect of total dissolved solids (TDS), electric conductivity and acidity (pH). BOD values also showed that due to wastewater pollution there are many aerobic microorganisms and organic materials in the water of Giash spring while in the other springs, this parameter is zero and so there is no microorganism. Electric conductivity rates in all springs are in permissive range, but in Kachanak spring exceeds it. Comparing the anions contents with that provided by WHO, indicated that anions also are in the permissive ranges. Bicarbonate content in Kachanak spring is higher than other springs and nitrate in the Giash spring is the highest. Comparing the major cations in 5 studied springs showed that the lowest sodium and potassium contents are in the Giash and the greatest in Katalom spring. The highest contents of both calcium and magnesium were in Kachanak but the lowest ones in Rishboraz Darreh and Giash respectively. Silicon amount in Katalom was the greatest and the lowest in Giash. It seems that the unusually high amount of silicon is due to mixing of magmatic hot water with groundwater. According to WHO all the cations are in permissive range. Metal index (MI) (Tamasi et al., 2004) and Heavy Metal Pollution Index (HPI) (Mohan et al., 1996) are indicators to determine the pollution extent in the water resources in respect of heavy metals. MI is used to evaluate the potability, and HPI is used to examine the effects of the heavy metal on human health. To determine these indices, 13 elements data including Ba, As, Cd, Cr, Pb, Ni, Mo, Zn, Se, Mn, Sb, V, Cu were used. In all the springs, calculated MI and HPI were in the permissive range, which suggests a lack of severe pollution in terms of heavy metals. Katalom and Kachanak springs have the highest, and Namak Darreh has the lowest indices values (table 2). Geothermal activities in the vicinity of Katalom, Sadat Shahr and Ramsar, presence of thermal springs (and mixing of their water with mentioned springs), old mining activities in Katalom and agriculture activities in the area are among the reasons for these high indices’ values in Katalom and Kachanak springs.

Based on the interpretation and processing of the information obtained from chemical analysis and evaluation of physical parameters, results on the studied springs are as follow:Kachanak spring is of Si-HCO3 type, and the others are of Si-Cl type. Silicon contents are higher than other elements. Alkaline earth metals (Ca2+, Mg2+) are more than alkaline elements (Na+, K+) and anions of strong acids (SO2-4) are more than weak acids (HCO3-). Noncarbonated hardness exceeds 50%.
According to Schoeller standard, Kachanak spring is in non-drinkable and bad classes concerning calcium and magnesium contents, respectively. In Katalom spring, pH value is out of the limit of Schoeller standard and calcium, and magnesium contents were bad and moderate respectively. In Rishboraz Darreh calcium and magnesium, parameters are in acceptable for emergency conditions and moderate classes, respectively. In Namak Darreh spring, calcium is in unsuitable class, and magnesium is in moderate one. Moreover, pH parameter is also lower than the defined limit in this classification. In Giash spring, only in respect of calcium parameters is unsuitable class. All the springs of the studied area are in a good or acceptable group concerning other parameters. According to the Iranian standard (1053), in Kachanak spring, total hardness (TH) is unsuitable in undesirable range but is permissive, in Rishboraz Darreh, Namak Darreh, and Giash springs, parameters are desirable. According to WHO (2011) and U.S Environmental Protection Agency (U.S.EPA), pH parameter of Katalom is out of permissive limit, but TDS and total alkalinity in all springs are in the permissive range. Also, MI and concentration of heavy metals such as nickel, arsenic, lead, chromium in Katalom spring are most significant among other springs.

In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. True measurement of this parameter, carried out by laboratory measurements, is very expensive. Therefore, many researchers have attempted to find rapid and accurate alternative ways to predict this parameter (Bhatt and Helle 2002, Rezaee, Jafari et al. 2006, Hamada and Elshafei 2009, Al-Anazi and Gates 2010, Bjørlykke and Jahren 2012, Wang, Wang et al. 2013, Zerrouki, Aifa et al. 2014). Intelligent methods such as artificial neural networks (ANN) and swarm intelligence (SI) are robust tools for estimation of this parameter. Review of the literature shows that many intelligent methods for prediction of porosity have been suggested by the past researchers. In the research documented here, ANN optimized by simulated annealing algorithm (SAA), is investigated for its capability to predict porosity from log data. 

Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Porosity is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, the application of soft computing methods for data analysis called ANN optimized by SAA to estimate porosity is demonstrated. The simulated annealing algorithm was used for initial weighting of the parameters in the artificial neural network. The developed methodology was examined using real field data (Marun reservoir, Iran). 

In this paper, hybrid ANN was SAA utilized to build a prediction model for the estimation of the porosity from available data, using MATLAB environment. A dataset that includes 1356 data points was employed in current study, while 1085 data points (80%) were utilized for constructing the model and the remainder data points were utilized for assessment of degree of accuracy and robustness. The training and testing procedures of ANN-SAA model were conducted from scratch for the mentioned five datasets. The obtained mean squared error (MSE), root mean squared error (RMSE) and correlation coefficient (R) values for training datasets indicate the capability of learning the structure of data samples, whereas the results of testing dataset reveal the generalization potential and the robustness of the system modeling method. The correlations between measured and predicted values of porosity for training and testing phases are shown in Figs. 1 and 2. Also, a comparison between predicted values of porosity and measured values for data sets at training and testing phases is shown in Figs. 3 and 4.   Figure 1. Correlation between measured and predicted values of porosity for training data   Figure 2. Correlation between measured and predicted values of porosity for testing data   Figure 3. Comparison between measured and predicted values of porosity for training data   Figure 4. Comparison between measured and predicted values of porosity for testing data   As shown in Figs. 3 and 4, the results of the ANN optimized by SAA model in comparison with actual data show a good precision of the ANN optimized by SAA model.

A quantitative formulation between conventional well logs (available in all wells) and porosity eliminates the aforementioned problems and makes it possible to perform geophysical and geomechanical studies. Due to significance of calling for porosity knowledge, several researchers attempted to determine porosity through empirical correlations and/or traditional intelligent systems. Nonetheless, the quest for highest precision possible demands looking for high accuracy methods. In this study, hybrid ANN with SAA was employed in order to respond this demand. ANN-SAA model was used to formulate conventional well log data. The results indicated ANN optimized by SAA performed acceptably and it was capable of mining hidden knowledge about porosity from conventional well logs.

In this paper fundamental goal is discussed about tectonic activities and survey role of different factors of forming and deformations to understand better about origin of morphotectonic of land forms occurring to reveal geomorphological patterns in the Bakharden-Quchan zone in the context of Arabia-Eurasia collision (Berberian, 1976; Lybris and Manby, 1999; Shabanian, 2009).  

This paper uses of satellite images observations from Landsat 7, topographic data (SRTM), GIS and GPS data, geology and topographic maps, field observations of the morphotectonic landforms to clarify the active tectonics in the Bakharden-Quchan zone.  

In this zone there is an array active right lateral-strike slip fault that they obliquely have cut the range and produced offsets of several Kms in the geological structures. These faults have identifiable ends, where they turn into thrust and link to blind faults mechanism changing of faults to revers have caused shortening by thrusting in their ends bending. Through convergence of Arabia-Eurasia plates have put constantly under neotectonic activities this zone since last phase of Alpine orogeny. Morphotectonic features have revealed by uplifts, deformations, ruptures and incision of late Quaternary traces, displacement of channel this sections, shear and displacement of Quaternary alluvial fan deposits. There faults have rotated anti clockwise around their vertical axes to cause several Kms of NS shortening. They also require of several Kms along strike extension that is taken by the westward component of motion between south Caspian Sea basin, Shahrood Fault system and both Eurasia and central Iran (Hollingsworth, 2006: Bretis and Conrady, 2012). 

The most important results of this paper is the identification of an array of active right lateral-strike slip faults which are almost certainly responsible for major destructive earthquakes in both historical and modern. All of these faults have ended where they turn into thrusts and link to blind faults. Mechanism changing of these faults to reverse of caused to increase stress, shortening by thrusting in their ends bending and their neotectonic activities create different morphotectonic features in Bakharden-Quchan zone particulary along the Qhuchan and Baghan-Garmab faults that there are enough morphotectonic evidences (Tchalenko, 1975; Masson et al., 2007; Shabanian, 2009).

Pb mineralization in Horeh area is located in 25 km northeast of Shahrekord, the middle part of Sanandaj-Sirjan zone in the mineralization belt of Malayer-Isfahan, in the geology map of 1/100000 of Chadgan (Ghasemi et al., 2005). There are nine mineralization belts, and 120 index mineralization of Pb-Zn has been identified based on paragenesis of mineralogy, time and type of mineralization in the Sanandaj-Sirjan zone. The Malayer-Isfahan mineralization belt is in the middle part of the Sanandaj-Sirjan zone, which formed in Mesozoic in carbonate sequences along with deep faults (Shahabpour, 1385). Often, this type mineralization is similar to Mississippi Valley-type (MVT) Pb-Zn deposits that many of these deposits have been created simultaneously with orogeny so that topographic slope is an essential factor in the ore fluids displacement (Leach et al., 2005, 2003, 2001; Appold and Gruven, 1999). The lithostratigraphy units in Horeh area include dolomite and limestone Permian, conglomerate, sandstone and shale Jurassic, limestone cretaceous, low grade metamorphic and young alluvium. The primary trend of the structure of the Hore Pb mineralization is NW-SE as same as the trend of the Sanandaj-Sirjan zone and the Zagros fault. This paper aims to identify the geological, geochemical and petrogenesis of the Pb mineralization on the base of the mineralogy, geochemistry, geophysics and fluid inclusion data. 

There are taken 35 samples and the number of 10thin section and 5 polished sections were prepared and studied in order to petrography and mineralogy. Major oxides (XRF method) elements were analyzed for 5 samples. 3 samples (calcite) were selected for Fluid inclusion study by linkam THMS-600 in Isfahan University. Data Geophysics was taken by  IPRSw-888 set and was measured Rs, Ip, Sp. 

Horeh Pb Mineralization occurred as lens and veins with a thickness of several centimeters to several meters in sedimentary rocks, with slopes and stretches of NW-SE and angle of 45°. This deposit is sulfide-type consists of galena, pyrite, and chalcopyrite as the primary ore and malachite, calcite and iron oxides as gangue. There are observed galen as fine-coarse grain, euhedral to xenomorph with triangular cleavage cavities, pyrite, and chalcopyrite as finely-coarse-grain, calcite as open space filling and comb texture, and veins in other rocks. Malachite often is formed by oxidation of the pyrite and chalcopyrite. Also, there are goethite, hematite, magnetite, illite, dolomite, and quartz. The mineralogical paragenesis sequence in Horeh area is two stages: the initial phase of the reduction that caused to deposit the sulfide minerals such as galena, and the second phase of the oxidation, which led to the formation of oxides and hydroxides minerals by initial carbonate and silicate minerals. Based on geochemical data, SiO2 =38.31% indicates to low maturity of sedimentary rocks compared to the upper crust (Taylor and McLennan, 1985; SiO2 = 64.8%). The high mean value of CaO = 25.22% (upper crustal crust = 4.19%) indicates to high amounts of carbonate cement, which cause to decrease of the relative amounts of SiO2 and Al2O3 in the samples. Al2O3 amounts are due to the clay and mica and Al-rich mineralogy, especially illite (Elsass et al., 1997). Fluid inclusion data of mineral calcite indicate to the two-phase of the fluid include (L + V) with irregular shapes in the size of 4 to 10 μm, and 136.6 ° C average homogeneity, -14.5° to -20° ice melting and 20.15% average salinity ( weight equivalent to NaCl)( Bodnar,1993). The result of fluid inclusion indicates to the basinal brines that is similar to the Pb deposits of the Mississippi Valley type. Geophysical investigations identified 4 Pb anomalies in the region, which begins at depths of 10 m and extends along NS and steep slope toward the west to the depth of 50 m by measure chargeability (PI), electrical resistivity (RS) and metal coefficient map (FM). 

The Pb mineralization in the Horeh area is as Galena with chalcopyrite and pyrite. Based on field study and petrography data, Galena is the main mineral and carbonate, and silicate minerals are gangue. Pb Mineralization has occurred as the replacement, bedding and tangential in the Jurassic formations by basin brine fluid.  The combinations of field study and mineralogy, geochemical and geophysical data indicated to the similarity Pb deposit of the Horeh to the Mississippi type that was formed during the two-stage reduction and oxidation. Geophysical data were indicated to the, 4 Pb anomalies from 10 m the topography level with 50m thick with the steep slope to the west.

Due to improvements in remote sensing techniques and available analyzes it is now possible to prepare maps of lithology and alteration of an area. In this study, Aster image as well as several image classification (Maximum likelihood(MLC), Spectral Angle Mapper(SAM) and Spectral Information Divergence(SID)) were used to provide lithology maps and spectrum of minerals has been applied for the enhancing of alterations. To evaluate the accuracy of the prepared geologic maps were used. The classification results showed that the MLC method has the highest accuracy and the classified image using this method is acceptable. Also, spectrum of minerals which obtained by FieldSpec3 Analytical Spectral Device (ASD) were utilized to prepare the alteration map using Mixture Tuned Matched Filtering (MTMF) method. The presence of sericite and chlorite minerals were confirmed by examination of thin sections. The obtained lithological and alteration maps represent that the phyllic zone associated with granite and granodiorite rocks, while argillic and propylitic zones are mostly accompanied with andesitic rocks of study area.