فصلنامه فیزیک زمین و فضا
سال چهل و سوم شماره 2 (تابستان 1396)

Topography is usually resulted from the patterns of the plate tectonics and faults in relation to each other. If we can produce a model for these actions and reactions after the complete recognition of the fault charactersitics based on their slip rates in the considered region, an applied model is obtained which reconstructs the topography of the region. A more important fact is that this model which illustrates the relation between topography (as the super-structure) and faults interaction of the considered region (as the infra-structure), can be used as a criterion to recognize the undiscovered fault's structures of the study area. It can provide us a chance to determine the fault parameters such as slip rates. Iran is known as an area which is subjected to the high possibility of the earthquakes as a natural hazard. Thus earthquake studies are important to investigate this hazard. In this study, a model of the topography is constructed in the region which is prone to earthquakes. The model is compared with the digital terrain models (DTM) of the area resulted from the satellite image data sets. This comparison provides us a structural control on the faults of the region. Our case study is modeling the relationship between faulting and the topography in the North Alborz. Results of the study let us obtain criteria for understanding and prediction of the fault structures that have created the topography. In order to achieve this goal, we consider a DTM of Alborz region. With variation of the parameters of the faults and creating various fault models, an estimate of the elevation model of the region is constructed for the life time of the faults. We consider the variation domains for five parameters of the activity period, slope, horizontal slip rates in length and slope directions and the vertical slip rate of the main faults of the region. The optimum values of these parameters are obtained based on the neural network optimization method. Then, the topography of the region is modeled based on the numerical results of the method for the unknown fault parametrs. We use the algorithm of the method with some selected variation ranges for the values of the parameters for each selected fault of the region. For the slope parameter of the faults, a range of 30 to 90 degrees, for the horizontal slip rate on the length direction and the slope direction of the fault, a range of -3 to mm per year and for the vertical slip rate, a range of -0.5 to mm per year have been considered as variation ranges of the unknown parameters in the algorithm. These variation ranges are considered based on the previous studies on geodynamic setting of the region. The parameters of the nine main acitive faults in the North Alborz region make the assumed fault system of the region and the modeled topography of the region to be generated. Comparison of the modeled topography with the real elevation model of the region can be evaluated as how changing the data of fault parameters and the possible reconfiguration of these parameters, such as the slip rate, time activity and the slope fault can obtain more suitable results in the modeling. In other words, the method can be used to determine which configuration and fault structure in study region will lead us to a more consistent model and provides us the possibility of modeling the topography of the region based on the fault structures. The numerical results show the differences from -85 m to 236 m between the model result and the real elevation model. In addition, the root mean square error between the DTM and the model is 61.1 m which is an acceptable result, due to the fact that only five parameters are variable and just nine faults are calculated while ignoring the effects of erosion and sedimentation of soil in the final format of the earth topography. More accurate results can be obtained by increasing the number of faults and their parameters in the model.

In recent years, thousands of people around the world are affected by earthquake. There are many prospects of doing research on earthquake, that the ultimate goal all the researchers want to achieve is the reduction effects caused by this phenomenon. Activities in recent decades in reducing the effects of natural disasters such as earthquake, cause attention on earthquake precursors. Since satellite data have global coverage, suitable temporal resolution and low cost, they are useful for monitoring earthquake precursors. By launching GRACE mission in 2002, the possibility of measuring gravity field variations in weekly temporal resolution is provided. In this paper, 8 years GRACE Level 2 weekly data (have been smoothed by DDK3 filter) have been analyzed in order to detect abnormal gravity field behavior before large earthquakes. We replaced the Earth’s oblateness values (C20) with those from Satellite Laser Ranging because of their poor accuracy. We know that GRACE stripe errors elongated in north-south direction, hence these strips generate fluctuations in east-west direction. Therefore by taking x-axis (north direction) derivative the of these, variations are dramatically suppressed. So independence of these components of gravitational gradient tensor to GRACE stripy errors, cause increase signal to noise ratio. By this consideration we used just components of gravitational gradient tensor for anomaly detection. However, we must note that horizontal derivative operator shifts the phase of the original anomaly distribution in spatial domain. So the positions of time series computation of two selected components are different. In addition second derivative of gravitational potential amplify high-frequency components of the earth gravity field and hence the gravitational gradient changes delineate more clearly in the rupture line, revealling refined mass redistribution features caused by the earthquake. In order to suppress seasonal variations and isolate seismic effects, we removed seasonal variations (annual and semiannual and S2 tidal wave) from time series using least squares analysis. The time of earthquakes are excluded in the least squares fit. Since a large part of the deformation is in the ocean, the hydrological model (e.g. GLDAS) cannot be used to remove seasonal variations. By considering fact that other preseismic anomaly (e.g. ionosphere precursors) does not occur in the vertical projection of earthquake epicenter, we test outskirt of each epicenter in order to detect the anomaly. In order to search for earthquake anomaly from time series a reasonable range of gravitational gradient variations must be determined. We used median and Inter-Quartile Range (IQR) of data as the first method for anomaly detection in time series. Afterwards, Bagging, Boosting and Random forest models has been proposed in the detection process of prominent gravity field anomalies prior the earthquakes. Gravity field depends of many parameters such as location, tidal force, oceanic variation, etc. So distribution of gravity field variation time series is not normally used. By consideration this fact we cannot use mean and standard division of data for anomaly detection. According to obtained results gravity field anomalies occur within time interval of 2-5 weeks before earthquakes. The results in this study indicate that in each case study, the unusual variations of gravity field have had different sign but the signs of two selected components of gravitational gradient tensor for each case study are the same.

The gravity method is one of the geophysical tools used for geological, engineering and environmental investigations where the detection of geological boundaries, cavities, subsurface karstic features, subsoil irregularities, or landfills are essential. In higher accuracy measurements, the microgravity method has been widely and successfully used for locating and monitoring subsurface materials.
Since microgravity methods measure gravity variations at the surface, they are directly influenced by the density distribution in the subsurface and particularly by the presence of formation material, which may create a mass deficit relative to the density of the surrounding terrain. In many cases, deep or small-scale heterogeneities generating low-amplitude anomalies can be detected and the reliability of further interpretation requires highly accurate measurements which are carefully corrected for any quantifiable disturbing effects. The main purpose of the research, that was conducted in small part of a dam site, is to determine the quality and type of subsurface structures in location of tunnel construction. Study area for collecting microgravity data was located at a small part, considered for construction of Siah Bisheh dam, road Tehran to Chalous. Position of microgravity stations were over a tunnel path which in some parts encountered with collapsing structures. The study area was part of Alborz Mountains. Geology formation(Shemshak formation), consisting of lime beds together with igneous rocks which are severely affected by fractures. Data were collected along 13 profiles with separating distance of 15 m. The stations distance and number of data were 15 m and 148 respectively. Bouguer gravity anomaly was calculated after making corrections such as earth tide, free air, Bouguer, topography and terrain effects. The regional effect obtained using a program that is written in FORTRAN to fit orthogonal and orthonormal polynomials on the observed data and then residuals were estimated. Three negative anomalies were distinguishable in residual gravity map. Data of these anomalies are modeled with a 3-D inversion approach using GROWTH 2.0 software. The GROWTH 2.0 is an inversion tool which enables the user to obtain, in a nearly automatic and non-subjective mode, a 3D model of the subsurface density anomalies based on the observed gravity anomaly data. The current version of the tool has been developed from an earlier code (Camacho et al., 2002). In a nearly automatic approach, the software provides a 3-D model informing on the location and shape of the main structural building blocks of the subsurface structures. Then densities contrast of these anomalies was estimated. Result of the inversion was a 3-D distribution of densities contrast. To show this distribution of the densities contrast, the horizontal and vertical sections at different depth and different horizontal positions were selected and interpreted. From these sections it is indicated that the effective depth of the data, for identifying martial of subsurface structures from the inversion, is about 50 m. In the sections, areas with low densitig contrasts are related to the fractured limestone and those with high contrast ones are related to the compact limestone or igneous rocks. Existence of igneous and lime rocks that have more density and compactness, increase the quality of the structures in the path of the tunnel construction. Areas including fractured limestone, with lower density, decrease the quality of the structure and increase the risk of water permeability and collapsing in the path of the tunnel construction. Thus by interpreting of the results of the microgravity data inversion, areas with high and low compactness and good and bad quality rocks for tunnel construction are recognized, those are related to the fractured or karstic limestone and limestone and igneous rocks. Also boundaries of these formations where densitig contrasts vary suddenly, are related to the existence of faults.

Permeability is one of the main parameters in the oil reservoir evaluation that is usually estimated by using well test data and laboratory measurements from the reservoir core samples. However, these methods are very expensive and time consuming, and usually a few number of wells have such information to obtain permeability and other reservoir parameters. Therefore, the prediction and assessment of the reservoir rock permeability using other non-expensive and indirect methods can effectively reduce the exploration and production costs and give us useful information about the permeability of the hydrocarbon reservoirs. Nevertheless, we have to consider that this kind of information may suffer in resolution and the results may have some unacceptable errors in estimation of the permeability. Thus, using proper prediction methods and comparing the obtained results with the permeability from the well test data and laboratory measurements leads to better and reasonable predictions of the permeability in oil and gas reservoirs. Moreover, the type of the reservoir rocks can also severely affect the estimated permeability. Usually the permeability estimation in the sand stone reservoirs is much easier than in carbonate reservoirs, especially in the heterogeneous carbonate reservoirs. This is mostly because of the porosity type and the conditions of depositional environments.
In this regard, using well log data also has important role in the permeability prediction. This is mostly because the well logging tools run in many wells and well log data are more available. Including more data in the prediction process will result in better constrained permeability estimation. Common methods of permeability prediction use empirical equations based on not always sufficient core data. These equations are usually used for a special type of reservoir and may not applicable to various types of reservoirs.
In this study, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) methods are used to estimate permeability parameter in one the Iranian heterogeneous carbonate oil reservoir using well log data from the 4 wells, located in the given oilfield. These wells have 7 common logs that are incorporated in the permeability prediction process. The well log data firstly are classified into 4 electrofacies based on geological studies carried out on the field. The classified electrofacies are as follow: packstone-wackestone, mudstone-packstone, wackstone-grainstone-packstone, grainstone-packstone-wackstone. The classification is done by using Principle Component Analysis (PCA) and Model Based Cluster Analysis (MCA) methods. Then, each group of elecrtofacies is used as input data for Artificial Neural Networks and Support Vector Machine methods to predict permeability.
Artificial Neural Network (ANN) is trained by using Levenberg-Marquardt back propagation algorithm and Gradient Descent method with Momentum Weight and Bias Learning Function with 10 hidden layers. The Support Vector Machine (SVM) method is implemented using Nu and Epsilon algorithms and different types of kernel functions, such as linear, radial based functions, polynomial and sigmoid functions. Usually, the radial based kernel function gives the best regression with minimum error values. Our results show that, for all of the electrofacies, Support Vector Machine (SVM) method has less error than Artificial Neural Network (ANN) in the regression process. The Support Vector Machine (SVM) errors for the above mentioned Electrofacies are as following: 0.0065, 0.0242, 3.6587 and 0.0195 respectively.

The ground vibration is one of the blasting negative effects on the environment, which could cause damage to various structures in some cases. Allowable vibration criteria in both group of environmental criteria and structural criteria have been presented which are generally based on the peak particle velocity. Environmental criteria concern the adverse effects of vibration on the human comfort and in the structural criteria the vibration effects on the stability of different structures. Gas pipelines are lifeline structures and their safety is critically important during their operation. There are some instructions and guidelines for blasting and explosion in the vicinity of gas pipelines. In recent years with the development of construction along the existing gas pipelines, some case studies have explored the possibility of an explosion near the existing pipelines and relevant restrictions has been considered. Considering all these research works, the limit of 50 millimeters per second has been used in many pipeline projects. In the current research, vibration due to the explosion in the route of the Arak - Khorramabad freeway in the vicinity of a gas pipeline (minimum distance of 25m) is calculated by the use of empirical methods, and the calculated vibration values were compared to the local seismic recorded motions during a few controlled trial explosions. The site location geologically consists of andesitic rocks from Jurassic period which were strongly altered and converted to serpentinite. The initial blasting plan for excavation of trenches in freeway route consists of 64 mm diameter holes with average depth of 3 m and horizontal distance of 3 m. Usual number of explosive hole is about 60 holes per blast. ANFO explosive material is used and the amount of explosive material in each hole is about 4 kg, hence, the total average amount of explosive material in each blasting is about 240 kg. Based on five empirical relationships, peak particle velocities against the distance were calculated for the conventional blasting plan of the project (240 kg of explosive material). In the critical distance of 25 m, the average predicted peak particle velocity was about 390 millimeters per second which is much higher than the allowed amount of 50 millimeters per second. At the same distance and based on the empirical relationships, the maximum allowable explosive charges have to be up to 13 kg. The acquired data were controlled by the use of seismic data monitoring. In this way two short period seismographs with 3 components 2 Hz sensors and 24 bit digitizer are used during 3 trial blasts. The seismographs were mounted so that the two horizontal components of the seismograph records, namely, the radial and tangential vibrations of the blast were acquired. Totally 18 seismic records (6 three-component records) were obtained during data acquisition. The recorded data were processed using Seisan software after the prior implementations. As expected, the vertical and radial components had the maximum amplitudes and the tangential component had the lowest range. Also the general ranges of vertical and radial components were close to each other. Ground vibration measurements showed that vibration amplitude at a distance of 15 meters for the explosion with charge of 4 kg was about 19 millimeters per second and for the explosion with charge of 8 kg ti was about 21 millimeters per second. Comparison generally showed that the measured values of amplitudes in recorded vibrations were lower than the predicted motions by the empirical relationships. It also showed that vibration values derived from calculations based on empirical relations were generally conservative at this site, and a local seismic data monitoring would be necessary to optimize the blasting program in civil engineering projects.

Alborz mountain belt in the North of Iran is known as a tectonically and seismically active region. Determination of shear wave velocity structure is important to interpret the tectonic activities. In this study, we determine 1D shear wave velocity structure beneath 12 seismic stations in the Eastern part of Alborz and also 2D shear wave velocity structure along to two profiles (one is along to the trend of Eastern part of Alborz and another one is perpendicular to its trend), based on the joint inversion of P-wave receiver function (PRF) and dispersion curves of Rayleigh waves. To obtain the PRFs of each seismic station, we lonsider three-component body wave seismograms of 177 teleseismic earthquake events with magnitude Mw>5.2 and epicentral distance range 30° to 95°, related to the study region. Also the dispersion curves of Rayleigh waves in the vicinity of each station are extracted from surface wave tomographic study reported by Rahimi et al. (2014). Then these two group data are regarded as the input data for the joint inversion process using “joint96” program (Herrmann and Ammon, 2007). ). In this study, the initial models are taken from shear wave velocity models reported by Rahimi et al. (2014), based on tomographic inversion of Rayleigh wave dispersion for various tectonic region of Iran. We regard the maximum depth of investigation about 300 km (upper mantle) in this joint inversion process based on sensitivity kernels of the dispersion curves of the Rayleigh wave fundamental mode with respect to the shear wave velocity at different periods (Rahimi et al., 2014). To find the most robust final velocity model for each station, we regard two stability tests: first, searching for the optimal parameterization for the joint inversion process; second, simplify of the representative solution of the joint inversion process (Motaghi et al., 2015). According to the obtained results, the depth of Moho boundary beneath the eastern part of Alborz mountain range is relatively uniform and following 47±2 km. By attention to the absolute shear wave velocity structure along the two profiles, depth of lithosphere-asthenosphere boundary beneath covered area is roughly constant and mainly varies around 86±6 km. Also there are high velocity anomalies in depth range 120-180 km. These high velocity anomalies in the upper mantle are consistent with the presence of under thrusting of Caspian lithosphere beneath Alborz. This observation is reported previously by Jackson et al., 2002. These observations may support the remaining question about higher surface topography in the study region without enough supporting crustal thickness. Maggi et al. (2000), using the admittance between topography and gravity in frequency domain mentioned that the only very short period topography could be supported by the flexure of the layer, whilst any longer period topography must be supported by an isostatic response. This result supports our observations, which shows an isostatic compensation for much of the long period topography. On the other hand, for short period topography, the mechanism of elastic flexure layer beneath Alborz, allowing high topographies to be supported by thin crust. We observed almost well correlation between the thickness of high velocity under thrusted layer and surface topography and also our observation could support higher surface topography in study region without enough supporting crustal thickness.

There has been wide interest in ambient seismic noise studies for determining earth’ internal structures in the recent years. Ambient seismic noise contains waves with random amplitudes and phases which propagate in all directions (Van-Tighelen, 2003; Gorin et al., 2006). Therefore determining information of waves propagations is possible by extracting coherence signal. This information of propagation path is equal to Green’s function (Shapiro et al.,2005; Roux et al., 2005; Sabra et al., 2005). Ambient seismic noise method is applied in various researches such as acoustic, helioseismology, seismology, etc (Duvall et al., 1993;Rickett and Claerbout, 1999; Malcolm et al., 2004; Roux et al., 2004).
The isotropic and random noise source distribution is the basic assumption underlying retrieving empirical Green’s functions (hereafter EGFs) using this method (Weaver and Lobkis, 2001; Gouédard et al., 2008). Recent studies surrounding noise sources demonstrate the dominant presence of noise sources in oceanic regions (Stutzmann et al., 2009; Landes et al., 2010). Ambient seismic noise spectra contains two broad spectral peaks, one at the period of 17 s (the primary microseism), and the other at the period of 7 s (the secondary microseism) (e.g., Gutenberg, 1936; Berger et al., 2004).
Regarding the dominant presence of noise sources in oceanic regions and also sharp seasonal variations, noise sources distribution is non isotropic and directive (Stehly et al., 2008). Nevertheless, distribution of noise sources homogenizes when considered over long times (Snieder, 2004).
The randomization of the wavefield is enhanced by the scattering of the seismic waves on the small scale heterogeneity within the Earth (Shapiro and Campillo, 2004). Scattered coda waves, sampled randomly and repeatedly parts of wave propagations, similar to ambient seismic noise (Yao et al., 2006). Therefore scattered coda waves, contain valuable information about propagation properties of the media. Additionally these waves are also independent from distribution of noise sources (Stehly et al., 2008; Froment et al., 2011). Scattered coda waves energy flux, is equiparitioning of ambient seismic noise and are independence from distribution of noise sources (Shapiro et al., 2000; Margerin et al., 2009). Stehly et al. (2008) studies, illustrate that retrieving EGFs is possible from scattered coda waves part of noise correlation functions (hereafter NCFs), which was assigned as C3 method in brief. The C3 method is an efficient way, facing poorly oriented station pairs with directional energy flux of ambient seismic noise. Therefore the accuracy of estimating arrival times of the different parts of EGFs is improved by C3 method in the presence of inhomogeneous noise source distribution (Garnier and Papanicolaou, 2009; Froment at al.,2011).
The purpose of this study is retrieving EGFs by C3 method in the period bands of 1-3 and 3-10 s in Azerbaijan region. We processed vertical component recording of continuous data from 7 stations which are equipped with short period sensor (Kinemetrics SS-1) in Azerbaijan region (Figure 1). We use 1 year (Dec. 2011-Dec. 2012) of recording at these stations which are operated by the Iranian Seismological Center (IRSC) of the University of Tehran. NCFs were determined by preparation of raw data (i.e. removing the mean and trend, decimation, segmenting, time and frequency domain normalization). Rms-stacking method (see Shirzad and Shomali, 2013) was applied for all NCFs calculated for retrieving daily and total EGFs from ambient seismic noise method (C1). In this study, we investigate three types of NCFs including: (a) a coda wave signal window selected from NCFs which was calculated from raw data (b) a coda wave window identified from the subset of NCFs, which contributed to the rms-stacking method (c) a coda wave signal window selected from the subset of NCFs, which was subsequently used in daily EGFs from C1 method, in retrieving optimized EGFs by C3 method. We compared two parameters (including correlation coefficients and arrival time of Rayleigh waves fundamental mode) between extracted EGFs from C1 and C3 methods. Table 2 shows the results of this investigation. Analysis of this table shows that the standard deviation of the arrival time Rayleigh waves and correlation coefficients are 0.21, 0.98 in positive lag-time and 0.35, 0.96 in negative lag-time respectively. The results showed that all extracted EGFs using three types of coda wave signal windows were significantly similar in character. However, to save time and reduce the amount of calculations, we selected the first case i.e. using NCFs which was calculated from raw data for further processing (see table 1). In the similar way with C1 method, coda wave windows were stacked with rms-stacking method in monthly and yearly time intervals. Figure 8 shows, the monthly EGFs retrieved by C3 method which illustrate negligible (no) directionality in the region of study. Yearly (total) EGFs versus interstation distances in the period bands of 1-3 and 3-10 s, were depicted in Figure 9. Arrival time of Rayleigh waves fundamental mode is equal (to 2.09±0.04 (km/s) in the region of study.

Nowadays, the effect of an earthquake in triggering of other events in the surrounding areas is completely accepted. This effect in triggering future events and spatial distribution of aftershocks can be explained using the Coulomb stress changes theory. Occurrence of April 9th, 2013 earthquake with moment magnitude of 6.3 in Bushehr province that followed by an aftershock with 5.4 magnitude after 14 hours in its vicinity, convinced us to examine Coulomb stress change theory for this region of Iran related to this event using the Coulomb 3.4 software. We calculated Coulomb stress changes associated with the Kaki-Shonbe earthquake on surrounding faults and investigated the effect of transferred stress on spatial distribution of aftershocks. We also calculated the seismicity rate changes in the study area and investigated its correlation with Coulomb stress changes. For calculation of Coulomb stress changes, we used a half-space with Poison ratio equal 0.25 and shear modulus about of 800 kbar. The effective coefficient of friction in our calculations was 0.4 that is appropriate for these kinds of faults. We also used a number of about 1,100 earthquakes with magnitude more than 4, from 1913 to October 2016, to calculate the seismicity rate changes.
The Kaki-Shonbe Mw 6.3 earthquake occurred on 9 April 2013 (11:53 UTC, 16:23 local time) in the Zagros Simply Folded Belt in south-western Iran and its largest aftershock was triggered after 14 hours. The epicenter location was 20 km northeast of the town of Kaki, and the earthquake resulted 40 fatalities and 860 injured. Reverse slip on two along-strike, southwest dipping fault segments were found by analyzing satellite interferometry data. The main shock rupture initiated at the lower northern end of the larger northwest segment and slip on the smaller southern segment is likely aseismic. At first, to investigate the effect of the Kaki-Shonbeh earthquake on occurred aseismic slip on the southeast fault plane, we calculated the Coulomb stress changes related to this event on this fault plane by applying slips on the parts of causative fault of main shock. Our results showed that the transferred stress on most part of this fault plane is positive especially in the places that experienced aseismic slip. The aseismic displacement on this fault can be due to the displacement on the causative fault of Kaki-Shonbe earthquake and it is acceptable because of the tectonics of the study area and prevailing stress system. Investigation of the effect of Coulomb stress changes on the distribution of aftershocks showed that more than 80 percent of aftershocks have occurred in places where stress changes were positive. In other word, lots of the aftershocks have occurred in places where the transferred stresses due to co-seismic slip on the northwest fault segment and aseismic slip on the southeast fault segment were increased.
We calculated the Coulomb stress changes due to April 9th earthquake and aseismic slip on the southeast segment on the active faults in the study area. The obtained results indicate that the occurred slips on these fault segments increased the stress in some part of the Zagros Mountain Front Fault (MFF), Zagros Fore-deep Fault (ZFF), and the northern part of the Borazjan Fault. Coulomb stress changes due to these slips show a good correlation with calculated seismicity rate changes in the study area. The Borazjan earthquake epicenter, occurred on November 28th, 2013 with moment magnitude of 5.6, is located in the region that both Coulomb stress changes and seismicity rate changes increased and had positive amounts.

The Magnetotelluric (MT) method is an electromagnetic geophysical exploration technique that images the electrical conductivity distribution of the Earth crust and upper mantle. The source of energy in the MT method is natural. When the external energy, known as the primary electromagnetic field, reaches the Earth's surface, part of it is reflected, whereas the remainder penetrates into the Earth, which by interaction with the conductors, induces an electric field (known as telluric currents) and at the same time produces a secondary magnetic field which can be measured at the surface and the impedance tensor is calculated.
In the fall of 2014 MT measurements were carried out at northern Aqqala of Golestan plain in the northeast of Iran, close to the southeastern shore of the Caspian Sea. It was carried out in a wide frequency range to recognize the Conductive layers in depths of less than 2000 m in the region. Determining the potential of the area in terms of electrically conductive layers which represent the iodine bearing saltwater structures was our objective.
The electric and magnetic field components were acquired along two EW profiles (with 1500 meter distance) at 20 stations with a 900 meter distance between stations using GMS05 (Metronix, Germany) systems. Three magnetometers and two pairs of non-polarizable electrodes were connected to this five-channel data logger. The experimental setup included four electrodes distributed at a distance of 100 m in north-south (Ex) and east–west (Ey) directions.
In the MT method, conductive structures are ideal targets when located in a considerably resistive host. They produce strong variations in underground electrical resistivity. A robust single site processing followed by the one dimensional and two dimensional modeling that were performed for the MT data along profiles A and B. Analysis of the MT data-set suggests signatures of salt water reservoirs in the area which are distinguished potentially positive to contain iodine. We could recognize the more conductive zones in the less conductive host as layers of saline water.
Aqqala of Golestan plain geologically is a part of the Kopeh-Dagh sedimentary basin. Kopeh- Dagh was formed by the last orogeny phase of Alpine and the subsequent erosion. Topography relief is very smooth and basically it is a flat plain consisting of loesses occurring naturally between the Alborz mountain range and the desert of Turkmenistan. Quaternary sediments including clay and evaporates and particularly salt are impenetrable.
The MT data were processed using a code from Smirnov (2003) aiming at a robust single site estimate of electromagnetic transfer functions. 1D and 2D inversions were conducted to resolve the conductive structures. 1D inversion of the determinant (DET) data using the code of Pedersen (2004) as well as the 2D inversion of DET mode data using a code from Siripunvaraporn and Egbert (2000) were performed. The data were calculated as apparent resistivity and phases. The determinant mod provides a useful average of the impedance for all current directions. Since the quality of the determinant data was acceptable, 2D modeling of the determinant data would be expected to provide a more reasonable approximation of the true subsurface structure. Therefore, we used the model obtained from the DET mode data as a final interpretation model
The purpose of this study is to evaluate the possibility of using surface MT measurements on the very conductive sediments to monitor the underground salt water bearing layers or bodies. In this study one and two dimensional interpretations for recognizing conductivity structures were performed. The resistivity sections showed a clear picture of the resistivity changes both laterally and with depth. The inversion results revealed a highly conductive layers iodine bearing saltwater structures which are at the depths of over 450 meters along some profiles. One of the sites was proposed for exploratory excavations.

Daily precipitation records of Mehrabad synoptic station based in Tehran, for the period 1951–2013 was used to identify moderate to heavy cold weather precipitation events in the mainly rainy season of Iran which starts in October and ends in May. Mehrabad is one of the oldest stations in the country that holds the longest and most complete precipitation records available in the country with very few missing values; thus being suitable for identifying the types of precipitation events for the region and the associated atmospheric circulations. Following the Iranian Meteorological Organization definition, we identified moderate and heavy precipitation events for Tehran Province as the events for which total daily precipitation ranges from 5 to 20 mm and from 20 to 50 mm, respectively; but being characterized with anomalous cold weather conditions. This screening approach has resulted in a set of 133 days of moderate to heavy precipitation events featured with cold weather conditions, which is adequate for implementing a multivariate analysis. The 500 hPa geopotential height and relative vorticity, sea level pressure (SLP), 850 hPa wind field and advection of specific humidity at 00 UTC over the time period considered (October–May), covering a large geographical domain centred on Iran (20°E–70°E, 20°N–55°N) with a 2.5° latitude × 2.5° longitude spatial resolution were retrieved from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis archive (Kalnay et al., 1996; Kistler et al., 2001).
In the present study the S- and T-mode Principal Component Analysis (PCA) were used for classifying the 500 hPa atmospheric circulations associated with the 133 precipitation events. The S-mode PCA with correlation as a similarity measure was used as a data reduction tool and pre-processor of K-means clustering method, while a T-mode PCA with correlation as a similarity measure was employed to classify 500 hPa atmospheric circulations independently. Based on the scree plot (Cattel, 1966) and the sampling errors of the eigenvalues (North et al., 1982) five and six PCs were retained for, respectively, for the S- and T-mode PCA applications. The retained PCs were orthogonally and obliquely rotated using varimax and promax criteria, respectively. For an S-mode PCA, we used varimax rotated PC scores as input for K-means clustering, resulting in 5 circulation types (CTs). But applying a T-mode PCA coupled with varimax (promax) rotation classified all the considered days into six CTs. The skills of K-means clustering and un-rotated, varimax and promax rotated T-mode PCA in classifying atmospheric circulations were examined using some indicators measuring the separability and equability of the identified groups of each classification method. The results suggest that the obliquely rotated T-mode PCA outperforms both K-means clustering and orthogonally rotated T-mode PCA in classifying atmospheric circulations.
Each of the six CTs identified are capable of producing significant precipitation in Tehran, but all cases of heavy daily precipitation above 40 mm belong to the CT1 and CT2. Although various forms of tilt in mid-tropospheric geopotential trough are observed among the CTs, but the dominant tilting is that of the northeast–southwest direction, indicating the anti-cyclonic wave breaking. Except CT5, the CTs are associated with a dipolar structure in surface temperature anomaly consisting of a pair of negative and positive anomalies to the west and east of the country, respectively.

Land use planning based on capabilities, abilities and suitabilities of each region with regard to uniformity and coordination of the effects of their national operation results at the national level, assigns specific role and responsibility to each area. However, one of the integral components in land use planning is considering the potentials and meteorological and climatological limitations of different regions. In the way that many social and economic activities, such as the impact of the climate in agriculture, locating factories, industry and airports, and its role in identifying areas with potential for solar and wind energy is dependent on long-term behavior and pattern of this important indicator. Apart from the role of climate in above-mentioned applications, a lot of activities and industries such as tourism and even supply and demand level of the cooling and heating energy of human settlements are dependent on the behavior and patterns of every region climate. This is in line with a particular branch of meteorology called biometeorology and tourism-climate. On the other hand, everybody knows the importance of this issue that the assessment of ecological potential in any area for land use is based on tourism-climate potential and on the estimation of supply and demand level of the heating and cooling energy which unfortunately, have rarely been considered by managers and authorities. Despite the fact that there have been some studies in the field of bio-climate for different zones of Iran, the example of major weaknesses of these activities is relying on monthly data and short-term time series.
In order to analyze the thermal comfort conditions, the daily and long-term data of temperature, relative humidity, wind speed and cloud cover from 1960 to 2010 were used. Since access to the 50-year long-term data is only available for a limited number of Iran stations. These assessments have been done based on 40 selected stations having the most complete statistical period (Figure 1). It should be noted that the reconstruction of missing data was performed by linear regression, and the results were confirmed after validating the reconstructed data. In addition, the randomness of the observed data and their homogeneity were investigated using Run Test and drawing histogram. In this study, in order to monitor the conditions of human biometeorology, the method of Predicted Mean Vote was used as one of the most important indices of Physiology-temperature. PMV is a 7-point thermal sensation division ranging from less than -3.5 (too cold) to higher than .5 (hot) changes (Table 1). To compute this index easier and faster, some software have been designed within which RayMan is one of them. It should be noted that for calculating PMV index, four sets of data and variables are used:1- Situational variables include latitude and altitude, position and height of the city.
2- Meteorological variables include dry air temperature in Celsius degree, vapor pressure or relative humidity, wind speed and the amount of cloud.
3- The third set of variables includes Individual variables as effective Physiological characteristics in the model. In this regard, the individual characteristics such as height, weight, age and gender should be considered.
4- The fourth set of variables includes the type of clothing and activity. Clothing and activity are determined respectively based on Clo and Watts. It should be noted that the third and fourth sets are considered as default models.
The result of this study showed that in different seasons, several inhibiting factors act on thermal comfort. In hot seasons of the year, the very warm and hot conditions and in cold seasons of the year, the cold stress events have been introduced as inhibiting factors. The results based on long-term monthly averages showed that the percentage maximum of stations having bioclimatic conditions from very warm to hot belongs to July regarding %90 of the stations and maximum of cold to very cold conditions belongs to January with a frequency of 62.5 percent of stations. On the other hand, in October, Maximum stations in Iran with 40 percent of the frequency have experienced a thermal comfort. However, the daily long-term statistics during 1960 - 2010 reflects the fact that Chabahar with % 18, Ahvaz % 28and Hamadan %30.5 in most of the times have recorded respectively as maximum comfort, hot and very cold categories compared to other stations of Iran. Furthermore, the results of this research with the introduction of capacity and thermal comfort inhibiting factors for different parts of the country over the years can play an important role in providing capability and land use planning.

Rainfall is final result of complex global atmospheric phenomena and long-term prediction of rainfall remains a challenge for many years. An accurate long-term rainfall prediction is necessary for water resources management, food production and evaluation flood risks. Several large scale climate phenomena affect the occurrence of rainfall around the world; of these large scale climate modes El Nino southern Oscillation (ENSO) and Multivariate ENSO Index (MEI) are well known. Many studies have tried to establish the relationship between these climate modes for daily, monthly and seasonal rainfall occurrence around the world but the majority of these studies did not consider the effect of lagged climate modes on future monthly rainfall predictions.
This study focuses on investigating the use of combined lagged teleconnection patterns as potential predictors of monthly rainfall. Direct Multi Step Neural Network (DMSNN) approach was used for this purpose. Four regions (east, center and west) of Central Plateau Basin of Iran were chosen as case studies, each having many rainfall stations. Hence, precipitation data in a common statistical period of 1981-2014 in 20 synoptic stations in the study area were selected and that the data during 1981-2004 were considered to develop the model and the data during 2004-2014 were used for validation the model in order to predict the next 6 months in monthly time scale. Based on the cross correlation function (CCF) results, MEI (Multivariate ENSO Index) and SOI (Southern Oscillation Index) had strong impact on precipitation of the region.
Direct Multi Step Neural Network (DMSNN) modelling was also conducted for the 20 stations of Central Plateau Basin of Iran using the combined lagged MEI and SOI. Multilayer Perceptron (MLP) architecture was chosen for this purpose due to its wide use in hydrologic modeling. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg–Marquardt and backpropagation algorithms were utilized to train the network, tangent sigmoid equations used as the activation functions and the linear equations used as the output function.
The values R2 (Correlation Coefficient), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error) parameters were used to explore the efficiency of the model.
ANN models generally showed lower errors and are more reliable for prediction purposes. After calibrating and validating the models they were tested on out-of-sample sets. ANN was able to perform out of sample test with correlation coefficient of of 0.81 for the South, and 0.4 for West of Central Plateau Basin of Iran. Although the effect of SOI and MEI in the west is quite weak, however with the use of combined lagged SOI–MEI sets Direct Multi Step Neural Network (DMSNN) modeling, long term rainfall forecast can be achieved. Thus, the results showed that the predicted data preserved the basic statistical properties of the observed series.
The results of this research showed that teleconnection indices are suitable inputs for intelligent models for rainfall prediction. Computing the best structure of artificial neural network models showed that DMSNN can predict rainfall most accurately.
Accurate long term rainfall forecasting can contribute significant positive impacts in water resources management. Central Plateau Basin of Iran climate is greatly fluctuating; at times it goes through severe drought years, then suddenly it experiences wet periods and dry. During drought periods, water supply and irrigation sectors are affected severely; proper prediction of such drought period helps water managers and users to have well-planned, coordinated allocation of resources. Also, prediction of the wet years helps flood management authorities to have well-planned flood disaster management. In addition to predicting rainy month in advance, the developed ANN models are also capable of predicting the intensity of seasonal rainfall.

Climate classification has a long history dating back to the ancient Greek scientists and philosophers, but the first quantitative classification of world climates was presented by the German scientist Wladimir Köppen (1846–1940) in 1900 (Kottek et al, 2006). Being trained as a plant physiologist and realizing that plants are indicators for many climatic elements; Köppen (1900) established a climate classification system which uses monthly temperature and precipitation to define boundaries of different climate types around the world, i.e., linking climate and natural vegetation. This system has been further developed (e.g. Köppen and Geiger, 1930; Stern et al., 2000) and widely used by geographers and climatologists around the world. Although there have been many efforts to find alternative ways to classify the climate, the Köppen system remains one of the most widely used climate classification systems. In this research, monthly precipitation and temperature of 155 Iranian synoptic weather stations with relatively regular distribution over the country were used to provide an updated map of climate classification for Iran which is one of the largest countries with diverse climates in the world. Missing values of the used data were estimated and replaced using inverse distance weighed method. Monthly averages of precipitation and temperature for the considered time period (1990-2014) were then interpolated at a network of grids with 0.1 spatial resolutions using ordinary Kriging method. Subsequently, the climate types of the used stations as well as of the predefined grid points were determined using Köppen-Geiger classification method (Kottek et al, 2006; Chen and Chen, 2013). Additionally, following Rubel and Kottek (2010), monthly mean temperature of the Climatic Research Unit (CRU) of the University of East Anglia and monthly total precipitation of the Global Precipitation Climatology Centre (GPCC), both covering 1901-2014 time period and having 0.5 spatial resolution were used for computing Köppen-Geiger climate classification for different time sections of present time, in order to examine if the Iranian climate types have experienced any shift due to global climate change.
Based on the observational data for 1990-2014 time section Iran composes of 9 climate types out of 31 possible Köppen-Geiger climate types. Most parts of central, eastern and southern Iran is characterized with BWh and BWk climate types. The coastal areas of the Caspian Sea and most parts of mountainous areas of Zagros and Alborz in west and north of Iran have moderate climate type (Csa). However, the eastern slope of Zagros and southern slope of Alborz that are connected to the central arid and semi-arid climate of central Iran are distinguished with BSk climate. The southern parts of Zagros region is mostly dominated by BSh climate. Dsa and Dsb climate types are found in some parts of mountainous areas of Zagros and Alborz, while Csb and Cfa are the localized climate types that can be found in coastal areas of the Caspian Sea. Using CRU and GPCC datasets for 1951-2000 time section the same climate types were found for Iran although the sources of the data and its spatial and temporal resolution differs from that of observational data. The identified climate types in this study using observational data are in agreement with those of Kottek et al. (2006) and Chen and Chen (2013) for Iran. The identified climate types for different time sections of 1901-1925, 1926-1950, 1951-1975, 1976-2000 and 1990-2014 revealed that some Iranian climate types were not stable during these five time periods. Comparison of climate classification using observational data for 1990-2014 with those of gridded datasets for 1901-1925, 1926-1950, 1951-1975, 1976-2000 and 1990-2014 revealed that Dfa and Dfb climate types have disappeared from Iran in the map of 1990-2014 climate classification, suggesting that the number of Iranian climate types have decreased from 11 to 9 in most recent years. It was found that the area of BWk climate in central-eastern Iran is continuously retreated by time and it replaced by BWh climate. The Ds climate types were found to be very vulnerable to change and shift. It was also found that the Dsb climate type tends to shift into Dsa climate types in recent years. Most importantly, it was observed that the Ds climate types in western Iran tend to be replaced by Csa climate type. However, the obvious shift from Ds or Csa climate types into BSk climate type is observed in northwestern Iran. This result indicates a rapid and widespread desertification in northwestern Iran due to global climate change.

Through modifying the number concentration and size of cloud droplets, aerosols have complex impacts on radiative properties of clouds, which consequently change the radiation balance of the Earth, and modify the atmospheric air temperature. By conducting numerical experiments for a mid-latitude cloud system in April, the indirect effects of aerosols on shortwave and longwave radiation, and subsequent impacts on the near-surface air temperature are investigated over Tehran. To this end, three numerical experiments (control, clean and polluted) with initial identical dynamical and thermodynamic conditions, but different cloud-nucleating aerosol concentrations were conducted using the Weather Research and Forecasting (WRF) model. Simulations were conducted over three nested domains with two-way interactions (nesting ratios: 1:3:3; horizontal resolutions: 21, 7 and 2.333 km). A two-moment aerosol-aware bulk microphysical scheme, recently developed, discussed and tested by Thompson and Eidhammer (2014), was used. In the control experiment that represents conditions of the current era in terms of the aerosol number concentrations, concentrations of atmospheric aerosols were derived from 7-yr global simulations of the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model which include mass mixing ratios of sulfate, dust, black carbon (BC), organic carbon (OC), and sea salt. Hygroscopic aerosol number concentrations were reduced to one-fifth in the clean experiment, and increased by a factor of 5 in the polluted experiment. The meteorological initial and lateral boundary conditions in the three experiments were derived from the National Center for Environmental Prediction final analysis (NCEP/FNL) data at 1˚ horizontal resolution and 6 h temporal intervals. Results indicate that increasing (decreasing) cloud-nucleating aerosol concentrations in the polluted (clean) experiment is associated with more (less) numerous cloud droplets of overall smaller (larger) size. Indeed, mean cloud droplet number concentrations (effective radius of cloud droplets) in cloudy grid points averaged over the innermost domain and during the simulation period were found to be approximately 46, 158 and 417 cm-3 (8.5, 6.1 and 5.2 μm) in the clean, control and polluted experiments, respectively. Thus, the total droplet cross-sectional area of cloud droplets increases in the polluted experiment, leading to an enhancement in the shortwave cloud radiative forcing (or cloud albedo), such that less shortwave radiation reaches to the Earth surface. In contrast, the total droplet cross-sectional area of cloud droplets decreases in the clean experiment, leading to a reduction in shortwave cloud radiative forcing (or cloud albedo). In contrast to the significant changes in the shortwave cloud radiative forcing by aerosols, results indicate that changing the number and size of cloud condensation nuclei in the polluted and clean experiments has little impact on longwave cloud radiative forcing. Values of shortwave and longwave cloud radiative forcing indicate that as the positive longwave cloud radiative forcing in all experiments are nearly half of the negative shortwave cloud radiative forcing, clouds have an overall cooling effect on the climate system, counteracting the warming caused by increases in concentrations of the atmospheric greenhouse gases. Comparing the near-surface air temperature of the three experiments reveals that the enhancement of cloud albedo in the polluted experiment leads to a reduction in the near-surface air temperature, while reduction of cloud albedo in the clean experiment leads to the enhancement of the near-surface air temperature.

Clear Air Turbulence (CAT) refers to a micro scale turbulence which normally happens in upper troposphere and lower stratosphere when there is neither cloud in the sky nor any significant convective activities. The turbulence intensity is from low to sever and due to invisibility; it can cause irreparable damage to passengers in flights.
Many factors are effective in formation of clear air turbulence, including: wind shear, the waves, the tropopause, jet streams, the high level fronts, perturbations and breaking of the gravity waves caused by obstacles (such as Lee waves) or gravity waves caused by convection.
As stated, perturbations of micro-scale to meso-scale Lee waves are among the main factors in development of CAT. According to many studies which were done in mountainous areas throughout the world, mountain waves may form when any of these specific meteorological conditions happens: 1-The wind blows to the peak of the mountain in direction of 30° to perpendicular line; 2- The wind speed for the high mountains and hills is more than 30 and 15 knots, respectively; and 3- The stability around the peak is much greater than other levels of atmosphere. These conditions are used as some techniques for prediction of clear air turbulence associated with the mountain waves.
To the best of our knowledge, few studies on occurrence of clear air turbulence in Iran have been done. Hence, according to the necessity of knowing much more about this phenomenon in Iran and considering the influences of mountain waves on flights over country, we have conducted this study for Zagros Mountains. The position of Zagros Mountains is a north-west to south-east over west of Iran. Prevailing winds in this area are from the west and south-west. Therefore, the formation of mountain waves and the chance of occurrence Clear Air Turbulence is favourable in this area. In this paper, the Dena peak as the highest peak in Zagros Mountains is considered as the study area.
According to the mentioned meteorological conditions of the Lee wave formation, the days with Lee waves over Zagros Mountain is estimated for a period of 3 years from 2010 to 2012 using the actual maps and SKEW-T diagrams. The formation of Lee waves in the studied days is double checked by considering the Scorer Parameter and dimensionless Froude number. Furthermore, in order to indicate the range of wave formation, horizontal divergence is calculated and plotted using the WRF model output. Finally, the gradient Richardson number is calculated as an index for the CAT occurrence. Based on these results, the vertical momentum flux of Lee wave in the ridge axis is obtained typically in the range of 0.1-7.3 . In addition, the turbulence caused by the presence of the mountain waves is well indicated by Richardson number. The probability of the mountain wave formation in the Dena peak region during the 2010-2012 is higher in winter. We have also shown that the probability of turbulence occurrence with “moderate to severe” intensity in both 00 and 12UTC can happen in 550, 600, 650, 700hPa levels. Furthermore, turbulence with the same intensity occurred mostly at 12UTC in 650hPa level which is equal to just above the height of the Dena peak.