مجله ژئوفیزیک ایران
سال نهم شماره 4 (پیاپی 27، زمستان 1394)

The Common Reflection Surface stack method parameterizes and stacks seismic reflection events in a generalized stacking velocity analysis. The main drawback of Common reflection surface stack method is that, this method cannot handle conflicting dip situations where more than one event with different dips contributes to one zero offset samples. In this way, as the Common-Reflection-Surface stack method implementation considers just one event contributing to a given zero offset stack sample such that conflicting dip situations cannot be handled. To overcome the drawback of Common Reflection Surface stack method, proposed to consider a discrete finite number of operators for staking. To do this the coherence of operators is obtain in a range of dips then a threshold for the coherence is considered. Finally for the stacking just the operators are contributed that has more coherency than this threshold. The proposed method resolved the problem of contribution more than one event in to a zero offset sample in to some extent. However the big problem of proposed strategy is that, there is not a reliable touchstone for detection of such situations. Finding out such locations is difficult and missed contributions to the stacked section might cause artifacts in a subsequent poststack migration. The inability to determine the exact number of events that contributing to one zero offset sample is another problem of proposed strategy. causes a variation of the number of contributions to neighboring samples which, in turn, cause artifacts in subsequent processing steps. In fact, , the lack of a reliable criterion to identify the number of conflicting dips causes a variation of the number of contributions to neighboring samples which, in turn, cause artifacts in subsequent processing steps. This is deleterious for complex data where prestack migration is no viable option due to its requirements concerning the accuracy of the velocity model, such that we might have to rely on poststack migration. In addition to the handling of a small number of discrete dips, the conflicting dip problem has been addressed by explicitly considering a virtually continuous range of dips with a simplified Common Reflection Surface stack operator in a process termed Common Diffraction Surface stack. In analogy to the Common Reflection Surface stack, the Common Diffraction Surface stack has been implemented and successfully applied in a data driven manner and the problem of contribution of more than one event in a zero offset sample has largely been solved. As this comes along with significant computational costs, this method is only applicable to laboratories and researches and will not practical application in the industry.
We now present a much more efficient model-based approach to the Common Diffraction Surface stack which is intended to fully resolve the conflicting dip problem occurring in complex data and, thus, to allow to simulate a complete stacked section containing all mutually interfering reflection and/or diffraction events to optimized for poststack migration. The method makes use the principles of ray theory to forward calculate the parameters of Common Diffraction Surface operator directly in a velocity model. The required macro velocity model can be generated with any inversion method. This approach only requires a smooth macro velocity model of minor accuracy. We present results for a real land data set at the north of Iran and compare them to the Common Reflection Surface and data driven Common Diffraction Surface stack. Compared to the data driven approach, the computational effort is dramatically reduced with even improved results.

The seasonal forecasts of precipitation play a major role in agricultural and water resource management and also in monitoring extreme events such as drought and flood. The Earth Systems Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP) maintains and distributes a stateof- the-science regional climate model called the ICTP Regional Climate Model (RegCM), which has been successfully used in different regions of the world for a diverse range of climate-related studies. This study was performed with two aims: (1) to evaluate the performance of the RegCM4 dynamic model in forecasting monthly, seasonal and annual precipitation in four selected stations in the northwest of Iran, i.e. Tabriz, Ardabil, Khouy and Urumia; and (2) to examine the accuracy of a stepwise regression technique for post processing of the outputs of the model for a 30-year period from 1982 to 2011. In order to run the RegCM4, the required observed weather data of the study stations were collected from the Iran Meteorological Organization (IRIMO) archive, while the rest of the data were collected from the ICTP database including three sets of the weather data: NCEP/NCAR Reanalysis Product version 1 (NNRP1) with a 6-hour time step and a horizontal resolution of 2.5°×2.5° on the reanalysis data from the National Center of Environmental Prediction (NCEP) of the United States, Sea Surface Temperature (SST) of the Optimum Interpolation Sea Surface Temperature (OISST) type, retrieved from the National Oceanic and Atmospheric Administration (NOAA) database and surface data (SURFACE), which were consisted of three topographic features: Global Topographic (GTOPO), vegetation or land use Global Land Cover Characterization (GLCC), and soil type Global Zobler (GLZB) data, with a horizontal resolution of 30×30 seconds from the United States Geological Survey, for the period 1982–2011. To determine a suitable rainfall scheme, the normal year of 2009 was chosen for running the model using different schemes. Accordingly, the Kuo scheme with a minimum bias compared to the observed precipitation amounts in the entire 36 synoptic stations of the region was selected as the best scheme. The time step was set to 100 seconds, with a spatial resolution of 30×30 km2, and the number of grid points were 152 in longitude (iy) and 168 in latitude (ix) during the study period. The geographical area center was placed at 30.5° N and 50° E. Nine significant variables (excluding, total precipitation; tpr) having the highest correlation with precipitation were determined as q2m, t2m, ps, v1000, v500, u1000, u500, omega1000, and omega500. For post-processing of the outputs of the model, the multiple linear regressions (MLR) approach was used. Except for the warm months, the output of the RegCM4 showed a wet bias, and overestimation. Applying the multivariate linear regression equation (and sometimes two-variables) to the output of the model led to a better agreement between the observed and simulated values of precipitation, such that in 75% of the cases, the bias and relative error decreased for the monthly, seasonal and annual forecasts. At all stations, except for Urumia, performing the post processing improved the accuracy of the RegCM4 output at all time scales. Further scrutiny is recommended for explaining the variations among the stations.

The purpose of this study is to calculate the elastic moduli and compare their changes due to the presence of gas in Kangan and Upper Dalan Formations in the zone of a gas field in the South of Iran. Compressional wave and shear wave velocities are directly related to the elastic moduli. The measurements of the compressional wave velocity, shear wave velocity, and density are necessary to determine the elastic moduliThe presence of gas in porous rocks significantly affects the acoustic wave velocities and Poisson's ratio. Shear wave slowness need to be determined for the mechanical properties of rock. The compressional wave velocity is sensitive to the fluid types. When water is replaced by gas in the porous media of a reservoir rock, the rock density decreases while there is not a significant change in shear modulus, since fluids do not support shear wave velocity. In this study, compressional and shear wave velocities were calculated using compressional and shear wave slowness derived from the Dipole Shear Sonic Imager (DSI). Also, density values were determined using the density log. Elastic moduli (Bulk modulus, Shear modulus, Young's modulus and Lame parameter), Poisson's ratio and the ratio of K/μ were calculated using the relationship between the acoustic wave velocities and the density. In this study, the presence of the gas volume was confirmed using the ratio of Vp/Vsand corresponding changes in the reservoir zone. Through comparing the gas volumes, the changes in Vp/Vs were investigated. The result of this study indicates that when the volume of gas is increased, the ratio of Vp/Vsis decreased. The ratio of Vp/Vs can be considered as an indicator for the detection of hydrocarbons. In carbonate rocks, the range of Vp/Vsbetween 1. 6 and 2 can show very high gas saturation. The Vp/Vs ratio is more sensitive to the fluids than the shear wave velocity and compressional wave velocity. The changes in elastic moduli were studied by investigating the gas volume changes, and a decrease in the elastic moduli was observed when the volume of gas was increased. In carbonate reservoir rocks, the values of the shear modulus range between 13. 5 GPa and 22 GPa and the values of bulk modulus range between 21 GPa and 45 GPa which indicates a high gas saturation. Also, in gas zones, the reduction of the bulk modulus is much more than the shear modulus. In these reservoirs, the values of λparameter range between 10 GPa and 32 GPa and the values of Young modulus range between 17. 8 GPa and 30. 6 GPa, which shows the high level of the gas volume. In this study, one can observe that when the values of the Lame parameters (μ and λ) are close to each other, it can indicate the presence of gas. Poisson’s ratio and K/μ ratio were compared with the gas volume in the log format. As a result of this comparison, one can indicate that the Poisson’s ratio and the K/μ are reduced by increasing the gas volume. In carbonate rocks, the low ranges of Poisson’s ratio (0. 19 to 0. 3) and the K/μ (1. 27 to 2. 33) are dependent on the gas volume.

The structural index depends on the source types and the rate of the field decay with distance from the source. The structural index plays an important role in two interpretation techniques, i.e. the Euler deconvolution and Extended Euler deconvolution. This quantity in Euler deconvolution is an assumed quantity. In this approach, one can calculate the target depth and location using the prescribed structural index. A wrong structural index affects the target depths and locations. This is while the structural index in the Extended Euler deconvolution will be calculated. The Extended Euler deconvolution is a generalization of the 2D Euler deconvolution and Werner deconvolution that helps to stabilize the Euler deconvolution by providing three equations rather than one at each point. The noise in a data set corrupts the signal that the Extended Euler deconvolution searches for. Thus, the accuracies of locations, depths, and the structural index will decrease with the noise level. For noisy data, it is common to use a low-pass filter to suppress the noise effects before applying interpretation techniques. We considered the effect of applying a low-pass filter to magnetic data and calculated the structural index. The low-pass filter that we used was the Butterworth filter which has no ripple and is mathematically simple. In this study, we showed that for typical Euler deconvolution applications, the effect of the low-pass filtering will decrease the determined structural index from the theoretical value. To this end, we began with the magnetic potential of a vertical dipole with an SI of 2. The magnetic field had a structural index of 3 as it was the first order derivative of the potential. Then, we obtained the 2D Fourier transform of the vertical dipole potential. After that, the transformed potential was multiplied by the transfer function of the Butterworth filter in the wave number domain. The filtered potential of the dipole in the spatial domain was obtained by the inverse Fourier transform. We also did the above operations for the magnetic potential of the horizontal line of dipoles with a structural index of 1. In that case, a one-dimensional Fourier transform of the profile was applied. It was seen that the filtered field decayed slower than the unfiltered field. We studied the structural index in two horizontal locations, i.e. one directly over the target and another away from the target. Over the target, the structural index values were always equal to or less than 2 for the vertical dipole and 1 for the horizontal line of dipoles, depending on the cut-off wavenumber. Solutions at horizontal distances much greater than the depth to the target had structural indexes greater than 2 with an upper limit of 3 for a dipole and 1 with an upper limit of 2 for a horizontal line of dipoles. Upward continuation is also a low pass filter which does not change the structural index of a magnetic anomaly. We showed in this study that filtering the magnetic data by the Butterworth filter would decrease the structural index. Parameters of the Butterworth filter such as the order and cut-off wavelength directly affected the estimated structural index solutions. Once the wavelength values increased, it began to filter the field and the structural index. For higher orders of the filter, the structural index decreased more rapidly to the point at which the structural index was less than 1 for the vertical dipole for wavelengths more than 6.28 m. The results proved that one must take into account filtering for the application of theEuler deconvolution to locate magnetic dipole anomalies.Application of the low-pass filter to Chah Sorb magnetic data showed that the structural index decreased.

On the 11thMay 2013 at 2:08:08 GMT, a moderate earthquake (Mw=6.2) occurred in Hormozgan province, near the Goharan village (southeast Iran). The largest aftershock of magnitude 5.8 occurred on 12th May 2013 at 12:07:02 GMT near the mainshock, which produced more destruction. According to the reports, one person died and 17 people injured. This earthquake is the first noticeable recorded event in this area. By this time,the most important seismic activity had occurred in 1983 with magnitude Ms=5.7 (ISC). Aftershock sequences provide valuable information about the Earth’s crust and source properties of large earthquakes. This is because a large number of events occur during a short time in a small area. This earthquake had a lot of aftershocks, after 3 months Iranian seismological center (IRSC) recorded 284 events.    Generally, initial location of earthquakes produce inevitable error because of algorithms we use to locate earthquakes. But this error will be decreased by relocating. One of the most common methods used is double-difference algorithm developed by Felix Waldhauser (2000). The double-difference is one of the relative earthquake location methods in these methods, effects of errors produced by deviation from real structure velocity model can also be minimized. According to the theory of double-difference, if the hypocentral separation between two earthquakes is small (compared to the event station distance and the scale length of the velocity heterogeneity), then the ray paths between the two sources and a common station are similar along the entire ray path. In this case, the difference in travel times for two events observed at one station can be related to the spatial offset between the events with high accuracy.    Previous studies have shown that the direction of causative fault (related to mainshock) can be obtained by the distribution of aftershocks, but it does not exactly assess motion on the fault occurred during the earthquake. So obtaining focal mechanism of the mainshock and largest aftershocks could help us to solve this important problaluablem. There are some methods to obtain focal mechanism and one of the most powerful methods is moment tensor inversion. This method has developed for a local earthquake by Zahradnik et al (2005). This algorithm has been written in Fortran language programing named ISOLA. The ISOLA GUI has been created using the Matlab GUIDE tool.ISOLA is based on multiple point-source representation and iterative deconvolution method, similar to what Kikuchi and Kanamori (1991) have done for teleseismic records, but here the full wavefield is considered, and Green's functions are calculated by the discrete wavenumber method of Bouchon (1981). Thus, the method is applicable to regional and local events.   In this study, we use double-difference algorithm to relocate the mainshock and aftershocks of this earthquake. During this process, the error of location improved (compare to initial location). By using spatial-temporal analyse of aftershock sequence we derived that the causative fault of this earthquake is in an east-west direction. All the aftershocks are distributed in the area with length 40 km and width 25 km, but the most of them are being in a cluster with length 15 km and width 10 km. We determined the focal mechanisms of this earthquake, and 15 other large aftershocks by moment tensor inversion method (Isola software) and the results show that all of the events have a predominatinely strike-slip mechanism. According to the distribution of the aftershocks we determined, fault plane is distinguished from the nodal plane. The fault plane is a left-lateral strike-slip in the SW-NE direction. According to the Geological evidence, this earthquake is related to the unknown fault in this region, but topographic map shows that probably this earthquake is related to linear structure parallel to aftershocks distribution and the fault plane that obtained in this study using CMT(Centroid Moment Tensor) method.

The delineation of the elastic, or velocity, structures of the Earth has long been a goal of the world's seismologists. In the first few decades of seismological research, the investigation of velocity structures was restricted to the determination of one-dimensional models of the solid Earth and various regions within it. Seismologists are currently working on three-dimensional velocity models, and they are trying to resolve finer and finer features in the Earth. The knowledge of seismic velocity structure of the crust and the upper mantle is important for several reasons: It includes the accurate location of the earthquakes, it is used in the determination of the composition and origin of the outer layers of the Earth, it improves our ability to discriminate nuclear explosions from earthquakes, it helps to interpret the large-scale tectonics as well as a reliable assessment of earthquake hazards. In this study, we used highfrequency dispersion curves to estimate the elastic properties of Tehran and the suburbs. Tehran city is located in the Alborz major seismic tectonic zone. The Alborz is an arcuate chain of mountains in the Northern Iran that wraps around the Southern side of the South Caspian basin; the boundary is roughly the present shoreline of the Caspian Sea. The range is actively deforming on range-parallel thrusts and left lateral strike-slip faults. The thrusts dip inward toward the interior of the range from both its northern and southern sides, and the GPS-derived shortening across the range is 5 ± 2 mm/yr at the longitude of Tehran (Vernant et al., 2004b). Most are parallel to the range and accommodate the present-day oblique convergence across the mountain belt. Recent large earthquakes occurring in this region suggest that the seismicity is connected with major faults of the recent age that cut across the regional Quaternary Lineaments. In this study, in order to estimate the upper crust elastic structure, we conducted a tomographic inversion of the Love wave dispersion to obtain two-dimensional Love wave group velocity tomographic images in a period range from 2 s to 5 s for the city of Tehran and the suburbs. We used two databases to derive dispersion curves for different paths. In the first dataset, continuous ambient noise in ten stations located in and around the city of Tehran and installed by the Tehran Disaster Mitigation and Management Organization network was used to explore the inter-station Green’s Functions. In the second database, forty seven earthquakes recoded by the Parsian stations were prepressed and rotated to be used as single station records to estimate the Love wave group velocity. In the next step, the inter-station Green functions and single-station records were used to estimate the Love wave dispersion curves by applying a multiple filtering technique. All dispersion curves were used to estimate the two-dimensional Love wave group velocity models. For this purpose, Tehran region was divided into 0.1° × 0.1° cells. According to the ray coverage, the minimum dimension of distinct heterogeneity was 6 km. In our study, topographical features and near-surface known geological structures were two main criteria to assess the credibility of the estimated Love wave group velocity variations. There was a strong correlation between the estimated group velocities and topographical features. The prominent surface geology units at the mountain range consisted of varying structures including sand-stone, siltstone, claystone, and massive limestone.

Refraction profiles usually need to be between five and ten times as long as the required depth of investigation (Keary, 2002). By increasing the investigation depth seismic source waves travel long trajectory and because of attenuation cannot record strong head-waves. Sometimes it is not possible to use the appropriate source according to profile length. Whenever the seismic source energy is underestimated for long seismic refraction profiles/deeper targets or in case of highly absorptive media because of wave attenuation, the identification of first breaks becomes complicated particularly at far offsets. This can lead to inaccurate estimates of the deeper velocity distribution. Supervirtual refraction interferometry (SVI) based method is proposed to improve first arrival detection. The benefits are that traveltime picking errors can be greatly reduced for noisy head-wave arrivals, velocity and depth calculations will be correct subsequently. Application of the method will improve the signal to noise ratio of the first arrivals by a factor of ,where  is the number of postcritical source points used in SVI. In this study, supervirtual interferometry is applied to a numerical example and the field data from the south-west of Iran and its efficiency in enhancing of signal-to-noise ratio (SNR) of the first arrivals is presented.    Interferometry formula in x-ω domain in crroscorrelation method, based on wavefield reciprocity and time reverse invariance principle (Wapenaar and Fokkema, 2006), is: where  is position vector of source/receiver in Cartesian coordinates, A and B are sources in the media surrounded by  and  are Green’s function between the receiver A and the source B, Green’s function between receivers AB and source x with angular frequency ω respectively. C, ρ,  and  denote compressional wave velocity, density, real part and complex conjugate, respectively.    The product of Green’s function complex conjugate at  and Green’s function at  in frequency domain is equivalent to correlation in time domain. Thus by integrating cross correlation of recorded wavefield at  and  around the closed surface , which the sources are located on it,  becomes recoverable. There are two stages to apply SVI: Correlation every receiver record with the first to generate virtual refracted traces (correlations gather) and stacking the result for all post critical sources to enhance their quality.    Convoluting the first receiver data with the virtual traces to create supervirtual head- wave arrivals for each source. Since the arrival time as travel geometry of virtual head-waves for all post critical sources is the same, stacking can improve them and attenuate other waves. Because of random nature of incoherent noise, they are reduced by summation of virtual refracted traces. The difference in arrival times makes coherent noise weaker and migrates.  Thecomparison the SNR graphs of artificial and real data before and after using SVI provides the evidence for the success of the method. All the supervirtual traces SNRs are bigger than one and have sensible difference with initial traces especially for far offsets receivers.    In the interferometry formula the sources/receivers take place all around the closed surface , but in this work the sources are linear. The linear sources arrangement and their limited number cause unwanted effect on supervirtual records.

The Curie point (approximately 580C for magnetite at atmosphere pressure) is the temperature at which spontaneous magnetization vanishes and magnetic minerals exhibit paramagnetic susceptibility. The depth at which temperature reaches the Curie point, is assumed to be the bottom of the magnetized bodies in the crust. Curie point temperature varies from region to region depending on the geology of the region and mineralogical content of the rocks. Therefore, one can normally expect shallow Curie Point Depths (CPD) at regions which have geothermal potential, young volcanism and thinned crust (Aydin and Oksum, 2010). The assessment of the variations of the Curie depth of an area can provide valuable information about the regional temperature distribution at depth and the concentration of subsurface geothermal energy (Tselentis, 1991).    There are two basic methods which have been used in the examination of the spectral properties of magnetic data to estimate the magnetic basement depth. The first is the method of Spector and Grant (1970) and the second is the method of Bhattacharyya and Leu (1975, 1977). Spector and Grant (1970) showed that the expectation value of the spectrum of an ensemble model was the same as the average depth to the top of an ensemble of the magnetized rectangular prism. The second method estimates the depth to the centroid of the body by using the interpretation of a single anomaly. This method is useful when no spectral peaks occur on the amplitude spectra (Li et al., 2010). Following this, a joint method was developed by Okubo et al. (1985) who combined and expanded the ideas of the methods to the purposes of geothermal exploration.    CPD estimation (Zb) is obtained in two steps as suggested by Okubo et al. (1985). First, the centroid depth (Z0) of the deepest magnetic source is estimated from the slope of the longest wavelength part of the spectrum divided by the wavenumber, where P(k) is the power density spectrum, k is the wavenumber, and A is a constant. Second, the average depth to the top boundary (Zt) of that distribution is estimated from the slope of the second longest wavelength spectral segment (Okubo et al., 1985), where B is a constant. The equation Zb = 2Z0 - Zt(Okubo et al., 1985) is used to estimate the depth to the bottom (Zb) of the inferred CPD from the centroid (Z0) and the top depth (Zt) estimated from the magnetic source for each sub-region.    The study area was divided into eleven overlapped blocks for the purpose of spectral analysis. Center of blocks are shown on the residual field map (Figure 3). Each block covers a square area of 140 km by 140 km. Power spectral analysis was conducted on the RTP values of each of the blocks by plotting the logarithm of spectral energies against the wavenumber. The CPD estimation procedure as suggested by Okubo et al. (1985) was carried out for the eleven blocks to obtain the Curie point depth for each block. Figure 5 shows Curie depth values and thermal springs of the study area. In this figure, Curie depth varies between 9.42 and 18.92 km. For the blocks of 3, 6 and 7, Curie point depth is significantly less than the other blocks, which could be due to the presence of high temperature hot springs in this area. According to the geological information and Curie point depth values, A, B, C and D area are recommended for more geothermal investigation.

Since human systems such as agriculture and industry, which depend on climatic elements, are designed and created based on compatibility and stability of climate, it is essential that the long-term changes of temperature and precipitation, which constitute the most important chanllenges in the environmental sciences, are identified and considered. In ordet to long-term forecast climatic elements for future periods, the use of Global Climate Models (GCMs) is inevitable. Typically, GCMs have a resolution of 150-300 km in each horizontal direction. Many impact applications require the equivalent of point-wise climate observations and are highly sensitive to fine-scale climate variations that are parameterized in coarse-scale models. Due to the coarse-resolution of the computational cell of GCMs, it is essential to use a downscaling procedure in order to convert large-scale data to regional/local-scales data. Downscaling aims to obtain fine-resolution climate or climate change information from relatively coarse-resolution GCMs. In general, downscaling is divided into dynamical and statistical categories. Dynamical downscaling fits output from GCMs into regional meteorological models such as Weather Research Forecasting (WRF) model. Thus, due to the fine-resolution (20-60 km) of the limited area models, it is possible to simulate some regional climatic features such as orographic precipitation, cloudiness, and some exetrem events. In climatological and meteorological researches using dynamic downscaling, a researcher can achieve both global-scale projections down to a regional/local-scale and the effect of global patterns on local weather conditions. The amount of computations involved in dynamical downscaling makes it computationally expensive to produce decades-long simulations with different GCMs or multiple emissions scenarios. The statistical downscaling method is created based on statistical relationships that link the large-scale atmospheric variables with local/regional climate variables. This method has many advantages such as being easy to apply, and computationally economical. As a result, in most regional/local researches, statistical downscaling is used to consider potential impacts on specific regions or stations. In this method using appropriate statistical relationships between predictor and predictand variables, it is possible to determine the relationships for future periods. In general, if the long-term data exist for the desired station, the best method is statistical downscaling. To determine the best statistical method for downscaling in each region, it is necessary to investigate the capabilities of various statistical methods. The aim of the present research is to investigate the capability of Statistical Downscaling Model (SDSM) in a hot and dry climate to downscale temperature and precipitation as output from Hadley Centre Coupled Model, version 3 (HadCM3) under scenario A2. Several modeling tools are employed in generating the sets of Intergovernmental Panel on Climate Change (IPCC) emission scenarios. The scenario A2 is one of the IPCC emission scenarios. This scenario is based on the following assumptions; a- relatively slow demographic transition and relatively slow convergence in regional fertility patterns, b- relatively slow convergence in inter-regional gross domestic product per capita differences, c- relatively slow end-use and supply-side energy efficiency improvements, d- delayed development of renewable energy, and e- no barriers to the use of nuclear energy. As mentioned earlier, characteristically dry and hot climate is considered to evaluate the performance of SDSM model. Therefore, daily NCEP/NCAR reanalysis and station data during the 1961-2001 period and output from HadCM3 under scenario A2 for 1961-2001 period containing temperature and precipitation for Yazd and Tabas synoptic stations are used. Comparing the results obtained from statistical analyses for observational and downscaled data indicates that the SDSM model can downscale correctly temperature output from HadCM3 in hot and dry climates. Daily precipitation resulted from downscaling using SDSM model has marked differences with observational precipitation in most of the statistical quantities used such as maximum and minimum precipitation. Only some statistical quantities such as the sum of the monthly precipitation and maximum consecutive dry days are consistent with the observed data.

The importance of estimation of mechanical and physical characteristics of rocks is not negligible for designing and analyzing rock structures such as rock slopes, deep trenches, and caverns. Also, these characteristics are necessary for studying rock bursts in underground mines, designing pillars and evaluation of rock failures. There are not any direct methods by which rock characteristics can be determined without a laborious, costly and time-consuming process. Therefore, a simple but reliable method is needed to determine the mechanical and physical properties of rocks in an indirect way. Non-destructive methods of measurement of physical and mechanical characteristics of rocks are considered as indirect methods. Among these, ultrasonic methods, as a low cost, simple and quick approach, are employed to determine the characteristics of rocks. In the ultrasonic method, it is possible to determine other indicator parameters and the quality of a rock using the measurement of wave velocity in the rock. The measurement of the P-wave velocity can be carried out in both field and laboratory environments. The P-wave velocity of rock is closely related to the intact rock properties and measuring the velocity in rock media interrogates the rock structure and texture. Pulse velocity measures the passing time of an ultrasonic pulse within a material, and hence it measures the pulse velocity of the mediumIn the ultrasonic method, some of the electro-mechanical transducers are used for transmitting and receiving elastic waves. The velocity of wave is obtained by dividing sample length to wave transmit time which is measured for the sample that has placed between transmitter and receiver transducers. In this research, in order to find the influence of these parameters on the pressure wave velocity, it was attempted to fulfill a series of laboratory experiments on eighteen samples of travertine rocks by using an ultrasonic device which was able to transmit and receive pressure wave. These experiments were focused on four parameters, i.e. the sample length to diameter ratio, frequency and amplitude of the wave, and pulse repetitions per unit time. An ultrasonic device used in this research had the ability to transmit and receive ultrasonic pressure waves by using two types of transducers 75 and 125 KHz. Other features of this device can be cited as the ability to change the frequency, amplitude and repetition of pulse per unit time of the transmitted pressure wave. Ateach stage of the testing process, only one parameter was changed, and the rest of the parameters were considered constant to make the assessment of the impact of evaluated parameter possible.For each transducer, the sample length to diameter ratio was changed at six levels from 0.5 to 3, the submitted wave frequency from 50 to 210 Hz, the amplitude from 0.1 to 4 volts and the wave pulse repetition from 0.5 to 16 Hz. The results were recorded.It is worth mentioning that the results of each test were the outcome of averaging three tests at each level. The purpose of averaging was to minimize the effect of different errors during the tests.To analyze the results, a software developed code was used to obtain the possibility of determination of the wave velocity in the samples by plotting the wave envelopeIn general, it was shown that by increasing the length to diameter ratio and amplitude and decreasing the frequency, the pressure wave velocity decreases. However, with changes of repetitions per unit time, the pressure wave velocity was maximized at a certain value of this parameter.