ADVANCED APPLIED GEOLOGY
Year:2018 | Volume:8 | Issue:3 (29)|Papers Count:8

1-Introduction For the first time in the modern history of seismology, Richter, (1935) invented a local magnitude scale (ML) for earthquakes for region of California. This scale is appropriate for estimation of magnitude of a wide range of earthquakes. Few investigations were performed about ML scale for earthquakes in Iran in different areas and using different databases. Shoja-Taheri, et al. (2007), Askari et al. (2009) and Nemati et al. (2014) are the examples from ML estimation in Iran. Shoja-Taheri, et al. (2007) used the strong motion data of NE Iran, Askari et al. (2009) used amplitude of short period seismograms in north of Iran and Nemati et al. (2014) used amplitude of waveforms of a local network data in eastern Alborz to calculate a local scale for magnitude of earthquakes in Iran. 2-Methodology ML of the earthquakes is calculated using averaging between the maximum amplitude of shear waves in the horizontal components of the waveforms of the earthquakes. Equation of ML is a logarithmic and parametric relationship. ML equation parameters should be determined for each seismic area. Estimation of a local magnitude scale is necessary for precisely estimation of magnitude of earthquakes in every seismic area. For calculation of ML for the earthquake of a specific area, it is better that the parameters should be estimated using the earthquake data of the same region. In this research the ML equation is determined for entire Iran using Iranian earthquakes, for the first time. Some researchers divide Iran into distinct seismotectonic regions, in which they calculate ML for each area, separately. In this paper ML is calibrated for entire Iran, because ray path of an earthquake in a specific region may cross the neighboring provinces. The chosen earthquakes for ML determination should be the most precisely located earthquakes, because the epicentral distance is so important in the processing. For this purpose, 1409 synthetic Wood-Anderson amplitude of 229 earthquakes occurred in 24 – 42 ° N and 43 – 65 ° E with magnitude of 3. 5-5. 4, azimuthal gap less than 180° and RMS less than 0. 5 were used. These earthquakes were recorded by seismological network of International Institute of Earthquake Engineering and Seismology of Iran between 2004 and 2016. The processing was done using Matlab software and arranging a big matrix composed of the epicentral distances, amplitudes and their functions. The amplitudes have been read using Seisan software on the horizontal components by automatic picking. In this stage, the amplitudes (velocity) are changed to the Wood-Anderson torsion seismograph scale (displacement). After reading, huge number of numbers (amplitudes, distances, earthquake coordinates, … ) form the mentioned matrix equation. 3-Results and discussion After the processing and inverting thematrix equation, using a parametric equation, in which geometrical spreading and inelastic attenuation were supposed, the attenuation equation for local magnitude in Iran were estimated: Inelastic attenuation is related to microscopic incompletions in mineralogical structure of the minerals, existence of water or the other fluids in porosities of the rocks, discontinuities in earthcrust, friction and transforming the wave momentum energy into temperature in wave path. In this relation, 100 km for epicentral distance of the earthquakes and the constant number (3. 0) for magnitude of earthquakes were put, because of Richter primary conditions for calibration of the ML equation in 1935. The station corrections (Sj) were obtained for all of the stations representing overestimation and underestimation of ML for correction the output magnitudes. The abovementioned numbers were put, because it is supposed that an earthquake with the magnitude of 3. 0 produces 1 mm amplitude in a seismograph, which is installed in a distance of 100 km. This equation suggests more attenuation for wave amplitude for distances more than 150 km in comparison to the previously estimation of Hutton and Boore, (1987) equation. 4-Conclusion 1) It could be concluded from this paper that wave amplitude attenuation decreases with distance in order of 1/r1. 0928 and geological site effects in Iran on magnitude estimation is in a broad range of 0. 7. 2) One of the preferences of this work (determination of ML for the entire Iran) in comparison to the other works (determination of ML for the seismotectonic provinces of Iran, individually) is that the ray path of a regional earthquake always cut more than two seismotectonic provinces. 3) This relation is useful for the governmental institutions in Iran that calculates the ML, like International Institute of Earthquake Engineering and Seismology of Iran. 4) Obtained station corrections were estimated between-0. 198-0. 44 magnitude unit.

1-Introduction The Zagros fold and thrust belt with NW– SE trend is located in the Alpine– Himalayan orogeny, resulting from the Late Cretaceous to Cenozoic convergence between the Arabian and Eurasian plates (Berberian and King, 1981; Alavi, 1994). The northeastern boundary of this belt is the structural style, and sedimentary history divides the Main Zagros Reverse Fault into several structural zones. The Zagros fold and thrust belt can also be divided, along strike from east to west, into the Fars, Izeh, Dezful embayment and Lurestan zone. The Lurestan zone has a long history of hydrocarbon exploration, and production, and only a few wells have been successful. Seismic and well data in the area indicate that folding style has the primary role in the exploration and production of the hydrocarbon. Also, in this belt, thrust faults and detachment horizons have the main character in geometry and kinematics of the folded structures. The Sargelan anticline is one of the main folded structures in the Lurestan zone. So, in this research, deformational pattern and structural geometry of the Sargelan anticline have been studied using the interpretation of five 2D seismic profiles, drawing of structural cross-sections, drilling well data in the adjacent anticline and detailed analysis. 2-Methodology The Zagros fold and thrust belt with NW– SE trend is located in the Alpine– Himalayan orogeny, resulting from the Late Cretaceous to Cenozoic convergence between the Arabian and Eurasian plates (Berberian and King, 1981; Alavi, 1994). The northeastern boundary of this belt is the anticlines in the Lurestan zone host the hydrocarbon reserves in the west of Iran, hence are well-acknowledged regarding the style of folding and their response to appropriate detachment levels. This research indicates the results of a detailed study of the Sargelan anticline, based on field surveys, seismic profiles interpretation, well data, and drawing four structural cross-sections. We will successively focus on the upper (Amiran Formation) and middle (Garu Formation) detachment levels and deep-rooted thrust faults, causing the disharmonic folding beneath and above the upper detachment level restoration of each structural cross-section. At last, restoration of each structural cross-section obtained by a shortening percentage. 3-Results and discussion Recent studies indicate that mechanical stratigraphy has the main role in folding style in the Lurestan zone (Casciello et al., 2009; Farzipur-Saein et al., 2009). In the study area, full upper detachment level causes a reduction in wavelength of overlying anticlines and formation of disharmonic folding. So, in this research four structural cross-sections (from SE to NW: AA´ , BB´ , CC´ , DD´ ) across the Sargelan anticline has been drawn transecting the anticline from the NE to SW to illustrate the vertical variations in folding style. Interpretation of seismic profile indicates that along the AA´ section deep thrust fault has not formed in the southwest limb. Also, regarding the geometrical parameters and the drawing the folding stereograph, the axial trend of the Sargelan anticline is 115/0, and its axial plane is 030/86 degrees. The BB´ structural cross-section indicates that in the southern part of the deep Sargelan anticline, another anticline is formed, which is introduced as the South Sargelan anticline. Along the South Sargelan anticline, a deep thrust fault rooted within the Garu Formation (middle detachment level) and cuts up-sections of the southern limb and flattens in the Amiran Formation (upper detachment level). In addition to the deep thrust fault, a low angle shallow thrust rooted within the upper detachment level (Amiran Formation) caused displacements and deformations in the post-Amiran formations. Along the CC´ cross-section, disharmonic folding caused the formation of the deep-seated Sargelan anticline beneath the surface syncline. The structure suggests that the deep-seated Sargelan anticline is formed along the s cross-section and continues to the north. The DD´ structural cross-section drew along the Sargelan, South Sargelan, Darreh-Baneh, and Chahar-Qaleh anticlines. Along with this structural cross-section, the Sargelan and South Sargelan anticlines have formed between the upper and middle detachment levels. Therefore, the study of these four structural cross-sections indicates that the deep-seated Sargelan anticline continues more than about 10 km just beneath the surface syncline. The reason for the formation of disharmonic folding is the high thickness of the upper detachment level (Amiran Formation) in the study area. 4-Conclusions The interpretation of structural profiles indicates two upper (Amiran Formation) and middle (Garu Formation) detachment level. Detachment levels and thrust faults have a significant influence on the geometry and kinematics of the structures, as the southwest limb thrust has formed and begin to ramp. Displacement of this thrust transported the anticline upward, and so the Sargelan anticline is a transported detachment folding. The high thickness of the upper detachment level cased formation of disharmonic folding that the deep Sargelan anticline continues to the northwest more than about 10 km and beneath the surface syncline. Measurement of the geometrical parameters indicates that the Sargelan anticline is an asymmetric, cylindrical fold, and concerning the aspect ratio and bluntness is fold-wide and semi-circle respectively. Interpretation of seismic profiles indicates that a deeper anticline is formed parallel to the Sargelan anticline, under the upper detachment level and it is introduced as the South Sargelan anticline in this study.

1-Introduction For a dam project in the karstic areas, the most important parameter is the hydraulic conductivity values which are necessary for determining the amounts of leakage from its reservoir and abutments However, secondary porosity and flow networks can cause heterogeneity and anisotropy in karst fields, leading to changes in hydraulic conductivity values and then significant differences in leak calculation values due to scale changes. Limestone formation in a regional scale is generally heterogeneous, and the heterogeneity of hydraulic conductivity (Karami 2002) usually specify the value of heterogeneity in karstic aquifers Kiraly (1975) investigated karstic aquifers and fractures in the Jura Mountains in Switzerland. He reported that the sub-local scale to the well-scale and the great permeabilities on the regional scale are related to karstic conduits and increase hydraulic conductivity. Rovey and Cherkauer (1995) measured the hydraulic conductivities of five hydrostratigraphic carbonate units at different scales and reported that the values of hydraulic conductivities show direct proportionality with the measurement scales. Sauter (2005) studied the various methods, sub-local to regional scales, for determining the permeability in a karstic environment. He mentioned that the various hydraulic conductivities in different scales could be due to the spatial organization and the degree of networking of the drainage system (Sauter 2005). Other hydrological and geological studies have investigated the relationship between scale with values of hydraulic conductivities in fractured rocks (Illman 2007) and sedimentary formations (Chapuis 2010, Galvã o et al. 2016). The main objective of this study is an investigation of the scale effect on the amount of water leakage from the reservoir and the dam abutments in the karstic area. We selected the Beheshtabad for the case study of this issue. 2-Methodology Beheshtabad dam is double-arch dam with a height of 180 meters and the reservoir volume of 1050Í 106 m3. The dam is situated on the Beheshtabad River with an approximately 33 m3/s flowrate. The right side of the reservoir is in contact with karstic limestone-dolomite of the Sangvil anticline named Jahrom-Asmari Formation with a thickness of about 700 meters. Determining the amount of leakage from the reservoir requires an accurate estimation of the hydraulic conductivity according to the contact scale with the karstic aquifer. The values of hydraulic conductivities have been measured in Jahrom-Asmari Formation of the right-side reservoir using various methods in three scales, sub-local, local and regional scales. We used the Slug and lugeon tests in sub-local scales and conducted simultaneous measurements of spring discharge and boreholes water levels such as pumping wells in the local scale. Also, hydraulic conductivity in the regional scale determined by Recession curve and Darcy method. Finally, the amount of leakage was calculated in different scales and compared based on the hydraulic conductivity values of different scales. 3-Findings The hydraulic conductivity has been calculated on a different scale with a related test for limestone aquifer on the reservoir’ s right-side. The values of hydraulic conductivities are not the same in different methods, and it is in the range of 2. 1×10-6 to 1. 6×10-4 m/s (Table 1). The lowest hydraulic conductivity belongs to a slug test conducted on a sub-local scale, and the highest hydraulic conductivity is for recession curve and Darcy method in regional scale. Also, the spring discharge method and the water levels of boreholes as a pumping well in the local scale estimates the values of hydraulic conductivities. The calculated hydraulic conductivities on the regional scale are about 70 times higher than those for the sub-local scale, which is related to the effect of scale in the karstic environments. According to the results, experiments with a radius of greater than 500 meters will determine the equivalent volume of hydraulic conductivity to the karstic mass region. Table 1. Calculated hydraulic conductivity value using different methods Methods KMIN (m/s) KMAX (m/s) KRE (m/s) PACKER TEST 1. 00E-05 1. 00E-07 3. 20E-06 SLUG TEST 1. 10E-06 3. 30E-06 2. 10E-06 DARCY 1. 00E-04 1. 30E-04 1. 20E-04 RORABAUGH 1. 41E-04 3. 60E-04 1. 60E-04 MILANOVIC 4. 10E-05 2. 00E-04 8. 00E-05 Seep 2D software determined the leakage value for the northern limb to the downstream and southern limb. The amount of leakage varies due to changes in values of hydraulic conductivities. The amount of leakage calculated based on the average reservoir cross-section with a limestone aquifer at sub-local, local and regional scales are 0. 3, 3. 9, and 5. 4 to 8. 1 m3/s, respectively. Such differences in the estimation of leakage are related to the scale effects on the heterogeneity of karstic areas. Hydrogeological studies indicate that the Beheshtabad reservoir is contacted regionally with the right-side limestone aquifer; therefore, the leakage amount should be modeled based on the regional hydraulic conductivity. According to the regional scale, hydraulic conductivity varies between 1. 1×10-4 to 1. 6×10-4 m/s. Therefore, the leakage amount from the dam reservoir will be between 6. 3 m3/s and 8. 1 m3/s (Fig. 1). Fig 1. Leakage amount based on regional scale hydraulic conductivity 4-Conclusion For determining the amount of dam reservoir leakage in the karstic areas, the most critical parameter is the value of hydraulic conductivity that changes with the measuring scale. When reservoir and abutment of the dam contacted with karst, some methods for determining the regional hydraulic conductivity such as dye tracing, the Darcy method, and the recession curve method are more accurate to estimate the leakage amount. If the reservoir contact is local, the pumping test is more accurate for determining the leakage value. Also, in the sub-local contact scale, lugeon, injection, and slug experiments are more accurate methods in terms of determining leakage amount. In karstic formations, the scale effect is one of the most important factors for reservoir considering in the stage of site selection of the dam. Therefore, the choice of the dam axis can be carried out in places where the reservoir and abutments of the dam are not in contact with the karst network as far as possible.

1-Introduction Corundum (Al2O3) is one of the rare metamorphic minerals that its formation requires particular chemical condition (low SiO2 and high Al2O3) in combination with high temperature (Simonet et al., 2008). Therefore, one of the most critical cases in the study of corundum-bearing rocks is the accurate determination of protolith and metamorphic conditions (Yakymchuk and Szilas, 2018). In the Broujerd area, Berthier et al. (1974) reported the corundum in migmatites for the first time. Despite the many studies in metamorphic rocks, these corundum-bearing rocks never reported in later works, until (Ghaffari, 2010) and then (Papi, 2015) again reported corundum-bearing rocks in the area and both concluded that they are a kind of migmatites, evidence of granulite facies metamorphism. Neither Berthier et al. (1974) nor Gaffari (2010) and Papi (2015) reported albitite rocks that are in direct relations with corundum-bearing rocks. Also differences between whole rock composition in migmatites and corundum-bearing rocks and its possible role in corundum formation, never discussed. 2-Materials and Methods Different samples collected for petrographic studies and finally, five samples had chosen for whole rock analysis (Table 1). Bulk rock compositions of selected samples were determined using an X-ray fluorescence spectrometer at the Zarazma Co., Tehran, Iran. Chemical compositions of minerals were obtained using a JEOL W-EPMA JXA8900-R electron microprobe in the Institute of Earth Sciences, Academia Sinica, Taiwan, at an acceleration voltage of 15 kV, a current of 15 nA, and a beam size of 2 nm. (Droop, 1987). The method used for Recalculation of Fe as Fe3+ and Fe2+. All mineral abbreviations are from (Whitney and Evans, 2010). 3-General geology and field relations Borujerd area located in Sanandaj-Sirjan Zone in the western part of Iran. Intrusive rocks, as main granitoids and rare norite, gabbro, and pegmatites, with NW – SE trend, injected along dominant schistosity in metapelites (Berthier et al., 1974), during middle Jurassic (175-170Ma; (Ahmadi-Khalaji et al., 2007; Mahmoudi et al., 2011). Contact metamorphism developed during magmatism, especially in the northern part of Broujerd batholith. Highest metamorphic grade restricted either to the northern part of granitoids or as large enclaves. Migmatites in Ab-Bakhshan area, show one of the most significant outcrops and located between granitoids and hornfelses. Based on Fig. 1, albitites dikes injected in migmatites. In general, towards albitites, migmatites first start to metasomatized and formed metasomatized-migmatites, then corundum-bearing rocks will appear either in contact or inside albitites. Migmatites are metatexite with the low-melt portion, generally with patchy structure. In petrographic studies, they include relatively uniform mineralogy: quartz, plagioclase and potassium feldspar are main minerals in the leucosomes with granoblastic texture, while biotite, cordierite, andalusite, sillimanites, spinel, and Fe-Ti oxides, are major minerals in mesosome, generally with lepidoblastic or grno-lepidoblastic texture. Their chemical composition (Table 1) with high Al2O3 and SiO2, and low alkali and calcium show that they have the pelitic origin. Metasomatized migmatites in the field are the same as migmatites, but under the microscope, andalusite and sillimanites start to replace by sericites, and biotites with chlorite. Near the corundum-bearing rocks (based on Figure 1) all andalusite and sillimanites completely replaced by sericites, all biotites with chlorites and also, feldspars altered. Comparing to migmatites, they have higher SiO2 and less Al2O3 (Table 1). Corundum-bearing rocks have different mineralogy. They mainly composed of corundum, chlorite, white mica (tin muscovites) and feldspar (albite), with rare rutile, ilmenite, and apatite. Albitites are mainly composed of albite (more than 80%) and quartz, sericites, potassium feldspars as minor minerals. Their chemical composition is entirely different from migmatites, with a low concentration of SiO2 and higher Al2O3 and MgO (Table 1). Albitites are deformed and make dikes and small patches in migmatites. They mainly composed of albite with rare muscovite, quartz, and k-feldspar. Their chemical composition is following high– Na plagioclases (Table 1. Figure 1. Schematic illustration of field relations between migmatites, corundum-bearing rocks and albitites (without scale) Table 1. Major elements analysis of selected samples, based on Figure 1 Sample BM-96. 5 BM-96. 8 BM-96. 9 BM-96. 102 BM-96. 7 SiO2 63. 85 71. 08 43. 25 43. 73 61. 45 Al2O3 17. 35 14. 65 28. 38 26. 55 19. 54 TiO2 0. 82 0. 73 0. 69 0. 48 0. 67 Fe2O3 7. 49 3. 6 4. 57 6. 94 2. 02 MgO 2. 2 1. 95 8. 7 8. 68 2. 75 MnO 0. 16 0. 05 0. 05 0. 07 CaO 0. 64 0. 8 0. 54 0. 92 0. 51 K2O 3. 35 2. 39 5. 48 2. 79 0. 32 Na2O 1. 53 1. 74 1. 82 3. 59 10. 09 P2O5 0. 22 0. 1 0. 06 0. 09 0. 16 LOI 2. 31 2. 91 6. 46 6. 09 2. 48 4-Mineral chemistry Selected mineral analysis from corundum-bearing rocks is represented in Table 2. Corundum crystals in a mica matrix, have a relatively pure chemical composition (Table 2) and their aluminum content is above 1. 99 a. p. f. u, only iron is a unique element that it reaches up to 0. 006 a. p. f. u. Chlorite is one of the most abundant minerals in corundum-bearing rocks, which forms a large part of the rock's matrix. Chlorites usually contain rutile and or ilmenite inclusions. Chlorite are Mg-rich (Mg/Mg+Fe from 0. 75 to 0. 76; Table 2). In white micas, iron and magnesium are low and close to a pure muscovite composition. Feldspars are sodic in chemical composition, in which the XAb is 0. 99 (Table 2). In other words, feldspars are pure albite. Cordierite is a rare mineral between feldspars and chlorites, and due to petrographic similarities, it is difficult to detect under the microscope. They are Mg-rich (Mg/(Mg+Fe)=0. 81; Table 2). Rutile as inclusions in chlorite are relatively pure, and their iron and manganese are deficient (Table 2). Ilmenite is also found in some parts in the chlorite or the rock matrix, and its chemical composition is close to the ideal ilmenite, with little impurities (Table 2). Table 2. Chemical analysis of minerals in corundum-bearing rocks of Broujerd area. Only selected analyzes are provided Mineral Chl Chl Ms Ms Crn Crn Crd Crd Fsp Fsp Rt Ilm Point 1 2 1 2 1 2 1 2 1 2 SiO2 27. 31 27. 29 47. 70 45. 98 0. 02 0. 01 48. 21 48. 18 68. 67 68. 03 0. 42 0. 00 TiO2 0. 05 0. 06 0. 09 0. 00 0. 01 0. 05 0. 00 0. 00 0. 00 0. 00 98. 78 53. 97 Al2O3 21. 50 22. 51 34. 86 37. 72 99. 82 99. 71 33. 04 33. 12 19. 42 19. 41 0. 25 0. 00 Cr2O3 0. 04 0. 15 0. 00 0. 00 0. 06 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 Fe2O3 0. 06 0. 00 0. 00 0. 00 0. 00 0. 00 1. 21 1. 23 0. 00 0. 00 0. 00 0. 00 FeO 13. 34 14. 08 0. 65 0. 33 0. 16 0. 20 4. 35 4. 44 0. 00 0. 00 0. 17 43. 67 MnO 0. 08 0. 06 0. 00 0. 00 0. 01 0. 00 0. 65 0. 61 0. 00 0. 00 0. 00 1. 85 MgO 24. 14 23. 36 1. 22 0. 39 0. 00 0. 00 10. 36 10. 45 0. 00 0. 00 0. 18 0. 10 CaO 0. 00 0. 01 0. 00 0. 60 0. 00 0. 00 0. 00 0. 00 0. 15 0. 09 0. 05 0. 00 Na2O 0. 04 0. 00 0. 61 1. 04 0. 00 0. 00 0. 00 0. 00 11. 23 11. 58 0. 00 0. 00 K2O 0. 00 0. 00 9. 76 9. 22 0. 00 0. 00 0. 00 0. 00 0. 05 0. 04 0. 00 0. 00 Totals 86. 56 87. 52 94. 89 95. 28 100. 08 99. 97 97. 82 98. 03 99. 52 99. 15 99. 85 99. 59 Oxygens 14. 00 14. 00 11. 00 11. 00 3. 00 3. 00 18. 00 18. 00 8. 00 8. 00 2. 00 3. 00 Si 2. 73 2. 70 3. 15 3. 03 0. 00 0. 00 4. 94 4. 92 3. 01 2. 99 0. 00 0. 00 Ti 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 99 1. 02 Al 2. 53 2. 63 2. 72 2. 93 2. 00 2. 00 3. 99 3. 99 1. 00 1. 01 0. 00 0. 00 Cr 0. 00 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 Fe3 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 09 0. 10 0. 00 0. 00 0. 00 0. 00 Fe2 1. 12 1. 17 0. 04 0. 02 0. 00 0. 00 0. 37 0. 38 0. 00 0. 00 0. 00 0. 92 Mn 0. 01 0. 01 0. 00 0. 00 0. 00 0. 00 0. 06 0. 05 0. 00 0. 00 0. 00 0. 04 Mg 3. 60 3. 45 0. 12 0. 04 0. 00 0. 00 1. 58 1. 59 0. 00 0. 00 0. 00 0. 00 Ca 0. 00 0. 00 0. 00 0. 04 0. 00 0. 00 0. 00 0. 00 0. 01 0. 00 0. 00 0. 00 Na 0. 01 0. 00 0. 08 0. 13 0. 00 0. 00 0. 00 0. 00 0. 95 0. 99 0. 00 0. 00 K 0. 00 0. 00 0. 82 0. 78 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 Sum 10. 00 9. 97 6. 93 6. 96 2. 00 2. 00 11. 02 11. 03 4. 97 4. 99 1. 00 1. 98 XMg 0. 76 0. 75 0. 79 0. 79 Mg/(Mg+Fe) 0. 76 0. 75 0. 81 0. 81 Or 0. 31 0. 20 Ab 98. 96 99. 40 An 0. 73 0. 40 5-Phase equilibria in the corundum-bearing rocks Phase equilibria for corundum-bearing rocks were modeled in the system Na2O-K2O-CaO-FeO-MgO-Al2O3-SiO2-H2O-TiO2 (Ti-NCKFMASH) using the THERIAK-DOMINO software v. 03. 01. 12 (Capitani and Petrakakis, 2010) with the internally consistent thermodynamic dataset of (Holland and Powell, 1998). The activity models of (Baldwin et al., 2015) are used for feldspar, those of (White et al., 2002) for corundum, and (Coggon and Holland, 2002) for micas. The water content is taken from the 'loss of ignition' (LOI) during XRF analyses, following (Sarkar and Schenk, 2014), although the effects of variation in the amount of water calculated. The fluid phase is assumed to be pure H2O. The final calculated pseudo section is represented in Figure 2. Figure 2. Calculated pseudosection for corundum-bearing rocks in Broujerd area (Sampled BM-96. 102 in Table 1 and Fig. 1). Gray region is following the mineralogy of corundum-bearing rocks. Dashed red and green lines represent equal values of Mg/Mg+Fe for chlorite and cordierite, respectively and their intersection show 605 ° C-3. 3 kb as T and P, based on the mineral analysis in Table 2 6-Conclusion In the Ab-Bakhshan area, corundum-bearing rocks appeared as small patches, located in albitites or albitite-migmatite contact. Based on calculated pseudosection for migmatites, their composition is not suitable for corundum formation, even in high T. Using whole rock composition of corundum-bearing rocks, T and P estimated as 605 ℃ in 3. 3 Kbar. Field relation between metasomatic rocks (albitites) and corundum-bearing rocks show that metasomatism was effective during corundum formation. The albitites occur most commonly in conjunction with other types of metasomatic rocks including scapolitised metagabbros and Mg-Al-rich lithologies such as orthoamphibole-cordierite schists (Engvik et al., 2014; Engvik et al., 2018). During Na metasomatism and albitite formation, Mg-Al rich fluids generated and cause chemical changes in migmatites, lead to appropriate whole rock composition for corundum formation. Na metasomatism could generate Mg-Al rich rocks, as corundum-bearing rocks of Broujerd area. So Mg-metasomatism and corundum formation either in migmatites or albitites, are consequences of Na-metasomatism in the area or are not evidence of very high T metamorphism.

1-Introduction One of the conventional field methods for determine permeability of rock masses is lugeon test. The absorption is specific with division water permeability test (lugeon) on the number of joints. The Secondary Permeability Index (SPI) relative to the specific joint that included the degree of joints, consistency joints, and void filling. The secondary permeability index obtained based on dams foundation classification for improvement dams foundation and rock masses classification. The secondary permeability index relative to rock mass permeability. The lugeon test is a way for determination secondary permeability index in dams foundation. The geological strength index that the results of this test were used for evaluation groutability of rock masses. In the classification of rock masses, geological strength index is better than other classification of rock masses because geological strength index noticed the roughness of surface joint in rock masses and structure rock masses with interlocking components. The roughness has a direct relation with hydrology parameters in rock masses — the geological strength index preference for groutability. The geological strength index is not noticing to the in situ stress and underground water — the estimation efficiency joints by log excavation and outcrops. The surface condition rating concluded: roughness, weathering and filling. This parameters collection thogether and determination of SCR. The structure rating determination by rock quality designation (RQD( equations (3, 4). The geological strength index classification of rock masses can be used for evaluation the permeability and groutability. The groutability determination by absorption specific. This estimation is easy, rapid and without additional cost to the project and provide an estimation will be important role in allocation cost exploration, project management and programming. 2-Methodology In this paper, permeability and groutability are evaluated by Lugeon and geological strength index in several dams. In this regard the groutability estimations are more or less in this relation, it is possible to zone the GSI chart based on lugeon number. In this research used of 200 lugeon test and geological strength index classifications. Their projects selected for estimation. These projects include Karun 2 dam, Jamishan reservoir dam, and Veniar reservoir dam. The 120 data used for the main chart and 80 data for validation. The structure rating (SR), surface condition rating (SCR), and lugeon determination in geological strength index chart. The permeability zones determined and the groutability zones evaluated. The zonation in the chart has a suitable correlation. 3-Findings In the chart of Figure 1, for GSI more than 55, the Lugeon between 1 to 3 or impermeable, the range of GSI between 45 to 55 equal to Lugeon 3 to 10 or low permeability, the range of GSI between 35 to 45 equal to Lugeon10 to 30 or medium permeability. Also, GSI less than 35 is Lugeon between 30 to100 or high permeability. The three boundary zone determination on geological strength index. The boundary one zone is SR=30 or Blocky/Disturbed/Seamy to Blocky middle boundary in the vertical axis, and horizontal axis included total SCR. The two-zone is disintegrated to middle very blocky in the vertical axis, and horizontal axis included total SCR. The three-zone is boundary Laminated/Sheared to middle boundary Blocky/Disturbed/Seamy in the vertical axis and boundary Good to Very poor in the horizontal axis. After the zoning in geological strength index chart, groutability and ground treatment of rock masses estimated. The first zone is without groutability (None groutable), the second zone has the local groutability (Local groutable, the third zone has the medium groutability (Medium groutable) and the fourth zone has the high groutability (High groutable). This chart application in sedimentary and igneous rocks. This rocks include: limestone, sandstone, gabbro and diabase. This chart not application for fragmentation and impermeability rocks. For example shale and schist. The boundary of zonation has some vacillation it can improve in research future. Figure 1. Zonation in chart of geological strength index with groutability and ground improvement 4-Conclusion In this chart, for GSI more than 55, the Lugeon between 1 to 3 or impermeable, the range of GSI between 45 to 55 equal to Lugeon 3 to 10 or low permeability, the range of GSI between 35 to 45 equal to Lugeon10 to 30 or medium permeability. Also GSI less than 35 is Lugeon between 30 to100 or high permeability. Based on the zonation in the chart, four zones namely none groutable, locally groutability, medium groutable and high groutable were identified. The zonation in chart also have a suitable correlation. This estimation is easy, rapid and without additional cost to the project and provide an estimation that will decrease exploration cost, project management and programming. In this paper, permeability and groutability are evaluated by Lugeon and geological strength index for sealing and consolidation rock masses. The recommended that examination accomplished in zonation of geological strength index and groutability for more exactly boundary zonation.

Pade-Bid iron occurance is located at northeast of Kashmar-Kerman zone, which is one of the most important iron metallogenic zone in Iran. Lithology of the area includes alternation of metamorphosed carbonate rocks, slate and phyllite, which are intruded by dioritic and gabbroic intrusions. Based on structural controls of orebody, metasomatic replacement and formation of low temperature H2O-beraring minerals and occurrence of magnetite and pyrite associated with chlorite, epidote, calcite and quartz, the iron mineralization is low temperature skarn-type. Magnetite chemistry and Ti, Ca, V, Al, Mn, Ni, and Cr contents are similar to those of skarn deposit. REE patern indicate positive Eu and Ce and negative Gd, Yb and Sm anomalis. The source of Fe mineralization is probably a younger intrusive body at depth due to field observations. Based on principal analysis on REE, the source of ore fluid is mostly magmatic water, which was ascended within fault zone and reacted with carbonate units to form the orebody. The northeastern part of Kashmar-Kerman zone has a great potential for Fe skarn-type deposits, which should be given more attention.

1-Introduction Separation of geochemical anomalies from background has always been a significant concern of exploration geochemistry. The search for methods that can make this analysis quantitative and objective aims not only at the reduction of subjectiveness but also at providing an automatic routine in exploration, assisting the interpretation and production of geochemical maps (Nazarpour et al., 2016). Arak 1: 100000 geological sheet is located in the north of Malayer-Aligoudarz-Efsahan Pb-Zn metalogenic belt, so this sheet has been studied. In this study, we compared the methods of classical statistics (Mean+2SDEV), exploratory data analysis (MAD), concentration-number (C-N) and concentration-area (C-A) fractal models. Also, singularity index models were used to separate the Pb and Zn anomalies in Arak 1: 100000 geochemical sheet. The results of mentioned methods, showed that the singularity index method has a higher accuracy. Also, indicates the higher concentration of Zn in area of study. 2-Methodology 2-1 Classical statistics Various statistical methods have been used to process geochemical data in order to determine threshold values. Statistical quantities, such as the mean, standard deviation (SDEV) and percentiles, have been used to define threshold for separating anomalies form background. For example, geochemical anomalies have been defined as values higher than a threshold defined as the 75th or 85th percentile, and Mean+SDEV or Mean+2SDEV (Nazarpour et al., 2015). 2-2 EDA (Exploratory Data Analysis) In exploratory data analysis (here after named EDA) of geochemical exploration data, the median+2MAD value was initially used to identify extreme values and act as threshold for further inspection of large data sets (Carranza, 2009). The EDA was first established by Tukey (1977), was developed further by, and then was used by many researchers in modeling of geochemical anomalies (Carranza, 2009). The MAD is the median of absolute deviations of individual dataset values (Xi) from the median of all dataset values (Tukey, 1976): (1) MAD = median 14 [â ” ‚ Xi-median Xiâ ” ‚ ]"> 2-3 Multifractal Fractal and multifractal models have also been applied to separate anomalies from background values. These methods are gradually being adopted as an effective and efficient means to analyze spatial structures in metallic geochemical systems (Afzal et al., 2017). The concentration-number (C-N), concentration-area (C-A) multi-fractal methods has been used for delineation and description of relations among mineralogical, geochemical and geological features based on surface and subsurface data (Nazarpour et al., 2015). Fractal/multi-fractal models consist of the frequency distribution and the spatial self-similar or self-affine characteristics of geochemical variables and have been demonstrated to be useful tools for decomposing geological complexes and mixed geochemical populations and to recognize weak geochemical anomalies hidden within strong geochemical background (Cheng et al., 1994). 2-4 Singularity index The singularity technique is another vital progress for fractal/multifractal modeling of geochemical data (Zuo et al., 2012). It is defined as the characterization of the anomalous behaviors of singular physical processes that often result in anomalous amounts of energy release or material accumulation within a narrow spatial– temporal interval. The singularity can be estimated from observed element concentration within small neighborhoods based on the following equation (Cheng, 2007): (2) 14X=c · Î µ a-E"> The singularity index is a powerful tool to identify weak anomalies, but it is influenced by the selection of the window size. When applying this method, one should use different window sizes to process the geochemical data and find an appropriate window size which can highlight the interesting results (Zuo et al., 2012). 3-Results and discussion Threshold values obtained using mentioned methods were used to map the spatial distribution of element concentrations. These interpolated maps were produced by means of inverse distance weighted (IDW) method (Nazarpour et al., 2016). In classical statistics and MAD methods, anomalies are usually detected, regardless of the location of each instance, and only by formulating relationships (Hashemi marand et al., 2018). In these methods, it is possible that some of the proposed ranges are false anomalies (Tukey, 1977). The geochemical anomalies of the Pb and Zn elements were separated using fractal methods of concentration-number (C-N), concentration-area (C-A), and according to the fitting line of each element on the logarithmic graphs. The singularity index estimated through a small window mainly reflects the fluctuation of the element concentration (Afzal et al., 2017). The singularity index estimated through a large window mainly reflects regional changes but it does not focus on the local weak anomalies (Zuo et al., 2012). There is probably a significant effect of the contact between exposed bedrock and covered areas, or there could be other deterministic trends as well, which should be studied further (Cheng, 2007). The results of the named methods are shown in Fig. 1. 4-Conclusions Singularity index analysis, indicated that the hidden anomalies are better coincidence with indices and mineral deposit occurrence in the study area. In general, the comparison between these methods indicate that the concentration of Pb and Zn increased toward the and southwest and south-northeast parts, respectively. In these regions there is high potential for the occurrence of promising mining areas. Moreover, the obtained Pb and Zn anomalies have a reasonable correlation with the exposure of limestone in the study area, which is a suitable host rock for the formation of MVT type Pb and Zn deposits.

1-Introduction The geological, structural, and seismic characteristics are not the same in various regions in Iran. The difference in the magnitude and frequency of seismic events in this area indicates such variations. Different researchers have provided various studies and maps of the geologic, tectonic, and seismotectonic situation in this area over the past years (Stocklin, 1968; Berberian, 1981; Alavi, 1991; Alavi Naini, 1972; Nowroozi, 1976; Berberian, 1976; Nogol Sadat, 1993; Mirzaei et al., 1998; Tavakoli et al., 1999; Zare and Memarian, 2000; Ansari et al., 2009). The similarity of such maps indicates the close correlation of the geologic, tectonic, seismotectonic situation in this area. Among the seismic parameters, the seismicity coefficients such as a and b in the Gutenberg-Richter (1954) equation are the most important parameters in determining the seismic pattern as well as tectonic and geologic characteristics of different regions. Determining these coefficients is necessary in the studies on the risk analysis and earthquake hazard zoning, and the value of these coefficients are assumed to be constant in the seismotectonic states. Therefore, providing a zoning map based on these coefficients will play an important role in better identification of the seismic characteristics of the region and applied studies. Providing a zoning map of the seismic coefficients of b-Value and a-Value and the ratio a/b-Value for Iran region is the main objective of this research. In order to calculate these coefficients and provide zoning maps, initially a complete catalogue of earthquakes occurred from 1900 to 2007 in Iran region has been prepared. Then, the entire Iran region is divided into very small and regular zones, and the seismicity coefficients (b, a) from the Gutenberg-Richter (1954) equation are calculated individually from this network for each cell. Based on the numerical value calculated for each cell in the range of Iran, the zoning maps of a-Value, b-Value and a/b-Value have been provided. The changes in the value of seismic coefficients indicate different tectonic situations in the region. According to the b-Value and a-Value zoning maps, the regions with various seismicity coefficients values can be separated. In the study and comparison of the maps, the zoning map a/b-Value shows more comprehensive information on the seismicity and tectonic situations of the region. As a matter of fact, using this map, the effect of coefficients such as a, b can be seen together in a map. Accordingly, Iran zone is divided into three general regions; low seismic potential, moderate seismic potential, and high seismic potential regions. 2-Methodology The first step in examining the seismicity of any region is collecting earthquakes that have previously occurred in that region. In this study, valid domestic and foreign sources were used for preparing the earthquake catalog. Before processing the seismic data, it is essential to refine the data of dependent events in order to obtain a poisson distribution of the data. In the present study, the window elimination method which is a standard method based on logarithmic time drawing of aftershocks based on the magnitude of earthquakes (Gardner and Knopoff, 1974). Finally, a catalog with 8090 earthquakes recorded from 1900 to 2007 (107 years) was prepared, serving as the basis and preliminary data of this study. Zoning of Iran based on seismic coefficients a-value, b-value, and a/b-value is the main objective of this study. These coefficients were prepared for the entire zone of the country, and then regions with equal values were zoned together. Thus, to perform numerical calculations and determine seismic coefficients, the entire zone of the country and adjacent regions were divided into 2° *2° cells with 1. 5° overlapping. This overlapping among cells plays a vital role in the continuity of data. A total of 1354 cell were resulted, for each the a-and b-values were calculated and attributed to the central point of that cell. In fact, across the entire zone of Iran, there are 1354 points with 0. 5° distance from one another, and each point has its specific a-and b-value. Due to the large number of earthquakes (8090 records) and the large volume of computations (1354 cell keys), a computer program was written using Visual Basic using which: 1) The recorded seismic data were read; 2) the data belonging to each cell were separated; 3) seismicity computations were performed for that cell, and 4) results were saved in a separate file. Therefore, for all the 1354 cells specified across Iran and for the data constrained in each cell, the Gutenberg-Richter line was drawn and a-and b-values were computed for them. Finally, after entering the preliminary data (seismic coefficients) in Arc GIS, zoning maps were prepared using the inverse distance weighting (IDW) method which is a common and frequently used method. 3-Result and discussion Based on zoning maps, the a-and b-values of regions with different seismic activities and seismic coefficients were separated. In each map, nine zones with varying seismic coefficients are evident (Fig. 1). In the zoning map of a-values, the largest numerical value of the frequency of earthquakes belongs to the Zagros region, East Alborz and Kopet Dag, and parts of Western and Northwestern regions, with the numerical value of 5. 6 to 8. 1. The b-value zoning map also showed the largest numerical value for the Zagros structural trend, East Alborz and Kopet Dag, and Western and Northwestern regions with the numerical value of 0. 49 to 1. 7. These high-coefficient regions surround regions including parts of the Central and Eastern Iran with low seismic coefficients. The increase and decrease in b-value of various regions of Iran indicate the different tectonic conditions and behaviors of this zone (Scholz, 1968; Mori and Abercombie, 1997; Manakou and Tsapanos, 2000). Figure 1. Right, the a-value zoning map across Iran; Left, the b-value zoning map across Iran Figure 2. The a/b-value zoning map across Iran Next, a zoning map of the a/b ratio was also prepared (Fig. 2). According to Bayrak et al. (2002), this map demonstrates the seismic condition and regions with seismic potential better than a and b maps. Based on this map, the Iran zone includes regions with high (6. 4 to 7. 4), moderate (5. 9 to 6. 3), and low (0. 0 to 5. 8) seismic activity. The high density of colors (high numerical value of a/b) in Zagros, Strait of Hormuz, some Eastern parts, Kopet Dag, Eastern and Western Alborz, and Azarbaijan indicate connected seismic belts of tectonically active regions surrounding regions with a low numerical value of a/b (low-density colors) or those with a low seismic potential (Fig. 2). 4-Conclusion Based on zoning maps of a-value, b-value, and a/b value prepared for Iran, regions with varying seismic coefficients can easily be distinguished from one another. These seismic zones can be compared with sedimentary-structural zones of Iran separated based on geological characteristics. This can also indicate the close relationship between seismic and tectonic features in Iran. Accordingly, Iran zone is divided into three general regions; low seismic potential, moderate seismic potential, and high seismic potential regions. The areas having high seismic potential have surrounded low seismic areas; of course, in form of a large belt of high seismic areas such as: 1-trend northwest to southeast of Zagros, 2-northern Strait of Hormoz with a northern southern trend corresponding to the Nayband-Sistan faults belt, 3-northeastern part of the Makran coast, 4-widespread sections of Azerbaijan, 5-Western Alborz, 6-Eastern Alborz, 7 Kopet Dagh, and 8-parts of the east of the country in the upper part of the Lut Desert (Khorasan).