مجله ژئوفیزیک ایران
سال هشتم شماره 3 (پیاپی 22، پاییز 1393)

Precipitable water (PW) is an important meteorological quantity that cloud physics scientists have paid special attention to it. In fact، PW can be an estimation of the total olumn water vapor in the atmosphere which is the result of convergence of water vapor in the lower atmosphere. Awareness of the relationship between this quantity and rainfall amount and intensity is important in predicting the atmospheric conditions. In most part of the world، PW is measured with weather radars and satellites. The first purpose of this research is to find a relationship between precipitation and cloud precipitable water by utilizing Tehran weather radar data. The second purpose is checking the accuracy and skill of WRF model in forecasting and simulating the PW value and its changes. For fullfilling this purpose، particular rain gauges with the ability torecord rainfall in short intervals (e. g. 1 hour intervals) are needed. Thus، three automatic rain gauges of Meteorological Organization (Pakdasht، Kooshk-e-Nosrat and Qom) were chosen. The Rainbow software was used to illustrate the Pakdasht، Kooshk-e-Nosrat and Qom Radar data. Desired products of radar data for this research were Surface Precipitation Intensity (SRI) and Vertical Integrated Liquid (VIL) (Actually VIL product is cloud precipitable water). For running the WRF model، three domains were considered including a 36 km horizontal range of parent domain and two nest domains with ranges of 12 and 4 km. It is worth noting that the relation between cloud precipitable water (CPW) and precipitation of radar measurements was investigated in the time period of 11/1/20 - 11/4/2010. The following results were obtained by comparing the quantity of the cloud precipitable water (CPW) and radar surface rainfall intensity (SRI) in three considered stations. It was found that before the beginning of the rainfall، the amount of CPW of cloud always increases، and then with passing the amount of CPW through the 0. 1 mm at that station، the radar shows SRI product on its display screen; this means that the rainfall is recorded by radar. After starting of the precipitation، the general trends of rainfall amounts would follow the trends in precipitable water values. However، the radar recorded a less SRI product at Kooshk-e-Nosrat and Qom stations compared with Pakdasht station for similar amounts of CPW. Meanwhile، the WRF model predictions for PW were compared with observations of this quantity by the weather Radar. Finally، it should be noted that the outcomes of model predictions for PW and Radar observations for CPW in the region under the cover of Tehran Weather Radar showed a very high accuracy (significant correlation at 5% significance level) in temporal-spatial forecasting and also in model simulation outputs for the changes of PW quantity. The results also revealed that whenever predicted PW by model having values more than 20 mm، radar observations displayed values more than 0. 1 mm for the CPW.

Velocity analysis is one of the most important steps of seismic reflection data processing which estimates the appropriate velocity for stacking and normal moveout correction. Taner & Koehler (1969) introduced the conventional semblance method for velocity analysis which is a normalized coherency measure. Semblance-based velocity analysis generates a velocity spectra for a common midpoint gather. Various parameters such as offset، signal to noise ratio، window length and etc. influence the resolution of semblance velocity spectra. For velocity analysis of a single reflector، if we consider the true velocity، then the hyperbolic reflection event aligns to the horizontal event in an analysis window and computed value of semblance is equal to one. If the operator selects an incorrect velocity، the hyperbolic event does not align to the horizontal event and the semblance value is reduced. If the selected velocity is close to the true value، then the computed semblance has a value close to one. Therefore، the resolution of the velocity spectra is reduced. Various methods have been introduced to improve the resolution of the semblance velocity spectra. Biondi and Kostov (1989) used the eigenstructure method to increase the resolution of velocity spectra. Sachi (1998) obtained the high resolution velocity spectra using the bootstrap algorithm. Roshandel et al (1387) improved the resolution of velocity spectra by multiplying the ratio of the two first eigen values of analysis window and the semblance value. In this study، we used the weighted semblance value introduced by Luo and Hale (2012) to obtain the high resolution velocity spectra. In this method، the weight functions are added to the semblance equation. The weight functions make the semblance value more sensitive to velocity and increase the resolution of the velocity spectra. Therefore، the weight functions must be calculated in a way that it is sensitive to the temporal change in normal moveout correction for every velocity at every offset. To illustrate the efficiency of the weighting function on the resolution of semblance spectra، weighted semblance and conventional semblance implemented on both synthetic and real CMP gathers. In the first synthetic example، we generated a CMP gather for a 37-layered earth model with linearly increased velocity. We computed its velocity spectra by both conventional and weighted semblance methods. Then، we added random noise to the synthetic CMP gather and re-calculated the velocity spectra. In these two synthetic examples، we have observed that the weighted semblance had a better resolution than conventional form. In the next step، we added multiple to CMP gather with near velocity to the primary event. Comparing the velocity spectra obtained from two mentioned methods showed that the conventional semblance cannot completely distinguish between the primary and multiple velocities. But they were easily separated from each other in velocity spectra of weighted semblance. Finally، both methods were applied to real data relating to the South West of Iran. The obtained result showed that extension of peak in the velocity spectra in the weighted semblance decreased. Thus، we can say the weighted semblance method has a better resolution than that of conventional methods.

The purpose of the geophysical activity is to recognize the underlying phenomena with a precision as high as possible. The record of noise in seismic data is inevitable. Different noises affect seismic data among which random noise is one of the most important ones، resulting from random oscillation of particles during the sampling. Random noise is present at all times and all frequencies; it decreases the seismic data quality. To achieve a trustworthy interpretation، geophysicists try to prepare data with the least random and coherent noise. There are many methods for analyzing seismic signals. In recent years، discrete wavelet transform has been introduced as a suitable choice in seismic signal processing. Bayram and Selesnick (2009) developed a family of wavelets transforms، which have a wide range of the Q-factor (in bandpass filters، Q-factor is defined as wavelet centre frequency/bandwidth). This research has tested the rational dilation wavelet transform (RDWT) with enhanced time-frequency discrimination. In this research the rational dilation wavelet transform was used for random noise attenuation from synthetic and real seismic data. The capability to apply different Q-factors is the main advantage of the RDWT compared to the dyadic DWT. This transform overcame the limitation of conventional wavelet transform with a constant quality factor. This method is robust in the task of de-noising with fewer signal distortion effects because of its ability to choose an appropriate Q-factor and the degree of overcompleteness. These WTs provide a rich range of redundancy and Q-factors. Moreover، the RDWT used in this study is based on rational (non-dyadic) dilation and attains overcompleteness by increasing sampling in both time and frequency (Bayram and Selesnick، 2009). The RMS error is a measurement of the differences between values of a trace after an RDWT inverse transform without any de-noising and the input trace shown in Table 1 of the present article. The capability to apply different Q-factors let the user choose the appropriate Q-factor and desired frequency and time resolution. The user can choose appropriate Q-factor setting some parameters (p، q، s، j)، wherein ‘q/p'' is the dilation factor and ‘s’ is the sampling factor of the high-pass filter and the number of processing levels is determined by ‘ j’. These parameters must be chosen such that the time frequency representation of each level of decomposition will be enhanced. In an earlier work، this method was used with the purpose of ground roll attenuation (Iranimehr et al.، 2013). Using the appropriate quality factor according to data type، the used wavelet will match well with the seismic signal and the signal obtains a large amount of wavelet coefficients but the undesirable random noise would attain small amount of wavelet coefficients. In the next step، noise was separated from signal using soft thresholding or other authorized thresholding methods. The wavelet transform was applied to synthetic data with different noise levels and a real marine data with high frequency content and the results of the random noise attenuation were compared with the dyadic wavelet transform. Table 2 of the present article compares the results of random noise attenuation by dyadic discrete wavelet transform and rational dilation wavelet transform by different type threshold.

Different local and temporal distribution of rainfall across the Earth has been amongst the determining parameters in many civilizations’ fate through history. Rainfall fluctuations and changes in climate phenomena are the monumental causes of these anomalies. Scientists have done a large number of researches to find the causes of the onset of widespread precipitation and its effects on agricultural crops، forecasting the onset of widespread rainfall، the teleconnection on widespread rain and the presentation of proper cultivation calendar for winter cultivation. Cold season brings about a great amount of widespread rainfall in many places in Iran. However، the basic consumption of water is during the warm season not only in civil but also in agricultural sectors. Therefore، being aware of the onset of widespread winter precipitation (OWWP) and its year to year fluctuations is a pressing issue. To examine and analyze the pattern of atmospheric circulation for the earliest OWWP، a 33-year period of rainfall data from 50 synoptic stations and one rain gauge station within four areas of Iran were analyzed. To find the early onset of the widespread rainfall، the average daily precipitations of all stations through each area were calculated. Furthermore، OWWR has two particular features per year. Firstly، the day having the higher standard deviation than the average rainfall of all the areas’ stations would be the onset day. Secondly، there should be at least two consecutive days having precipitation in more than 50% of all stations in the given area. As a result، there was a specific onset of rainfall for each area during 33 years. Moreover، if the OWWR in one year had started before the rainfall onset of that year، it would have been the year with the early onset. Consequently، the geopotential and vorticity maps of 500 and 1000 hPa showed the presence of a significant mridional component of atmospheric circulation in the earliest years with abundance of blocking highs and cut-off lows especially in the East Mediterranean and Iran. In addition، the anomalous positive relative vorticity in the earliest years lasted until the end of winter but the negative anomaly of relative vorticity showed the lowest amount compared with the other years. Finally، the result of Tukey’s test (HSD) showed a meaningful correlation (at 5% confidence level) between the OWWP and the total amount of rainfall in those above mentioned earliest years. In conclusion، 1974 in the northwest part، 1976 in the west، 1991 in the south and southwest and 1982 in the center and east parts of Iran presented the earliest widespread winter rainfall during those 33 years. In those mentioned years، the polar vortex had the highest positive anomaly and the high pressure of Saudi Arabia had its most optimal condition on the Arabian Sea which caused a wet year for Iran. Also in the mentioned years، heavy rain and flood have been seen in the semi arid areas of the East Mediterranean، Saudi Arabia and Sienna desert.

In the present survey، it has been tried to demonstrate a significant correlation between air pollutant concentrations and meteorological parameters، by the study of the atmospheric conditions in an air pollution episode. Carbon monoxide and nitrogen dioxide were selected as air pollutants. Temperature، wind speed and direction and the dimensionless Richardson number were considered as the meteorological parameters in this study. To this aim، a period of eleven days from 30 November to 10 December 2012 was considered. In this period، a severe temperature inversion and consequently a long-term stable situation was seen in the Tehran weather. The governing stability classes were studied using the Turner algorithm and vertical temperature gradient scheme. For the mentioned period، the sun altitude، wind speed at the height of 10 meters، total cloud cover and ceiling were extracted from the U. S. Naval Observatory web page (http: //aa. usno. navy. mil/data/docs/AltAz. php) to be used in the Turner algorithm. The stability analysis showed that for this time interval، stable conditions prevailed in 89% of cases. The sounding data was collected from the Mehrabad weather station. By the sounding data، the daily maximum mixing depth was calculated and the vertical trend of the temperature was plotted in each day during this period. The morning vertical temperature gradients showed a strong temperature inversion in these eleven days. Also، the maximum mixing depth decreased to the minimum value of 600 meters on the 6th day of the time interval. The maximum mixing height and visibility graphs also confirmed that the atmospheric mixing decreased and consequently more pollutants were trapped in the middle of the period. Once confirming critical conditions by comparing the stability classes، maximum mixing depth and the visibility، the correlation between atmospheric parameters and pollutants concentration was obtained by multiple regression method. Among the meteorological parameters that were considered، temperature، wind speed، wind direction and the Richardson number had a greater correlation with the pollutant concentration. The bulk Richardson number was calculated from the sounding data and used in the regression. The CO and NO2 concentrations were gathered from the pollutant concentration measuring station which is located on the region-10 municipality building، 2500 meters far from the Mehrabad weather station. The coefficients of correlation between the normalized CO concentrations and temperature، West-East component of wind، South-North component of wind and the Richardson number were 0. 707، 0. 078، 0. 028 and -0. 019، respectively. And for NO2 concentration، the correlation coefficients were 0. 353، 0. 016، 0. 015 and -0. 019، respectively. It could be observed that the coefficients of temperature and surface wind for the CO concentration were almost twice the corresponding coefficients for the NO2 concentration. The reason is that the NO2 concentration has been influenced by chemical and photochemical reactions and the mixing depth، while the CO concentration is only affected mainly by the rate of transportation and the mixing depth. In fact، the CO concentration had a stronger functionality to temperature and wind speed than the NO2 concentration. In the most cases، the Richardson number had a positive value and by increasing its value (in the early morning hours or night hours)، the pollutant concentration were reduced. Therefore، as expected، its coefficient were negative in both regressions.

Magnetic method is an important passive surface geophysical method which uses the Earth’s natural magnetic fields to investigate the susceptibility structure of the subsurface material. The geomagnetic field measurements can be used to determine the structure of the earth، because the rocks often contain magnetic minerals. The interpretation of data of this nature، in some cases، will determine geological characteristics that would help contribute to the success of mining or oil exploration. In this study، for the review of more details and achieving an idea about accurate location، dip، depth and mineralization spread، geomagnetic surveys were applied and then data processing method were used. Preparing a suitable map which contains the informative data without interference from noise is the main purpose of the data processing. To reach this aim، the acquired data should be corrected for different non-geological effects. These are: 1. Diurnal correction to remove time varying parts of the magnetic field. 2. IGRF correction to remove the field of earth core and upper mantle response. Many interpreting methods، that estimate the depth، location and the shape of a potential source، are based on using the gradients of potential fields. Derivatives are high pass filters. They intrinsically amplify any noise and shallow anomalies present in the data. Therefore، using high order derivatives would be less common. Tilt angle has many interesting properties. For example، due to the nature of arctan trigonometric function، all tilt amplitude are restricted to values between -90° and +90° (Salem et al. 2007). Another property of tilt angle is that the value of tilt angle above the edges of the contacts is 0°، and it is equal to 45° when h = zc and -45° when h = zc. This suggests that contours of magnetic tilt angle can identify the locations of contact-like structures. The studied area is located in Yazd province and it is part of micro continent Iran central. The intrusive rock in this area is genais and the sedimentary rock is schist and thehost rock of studied area is limestone. Igneous units of the trust are located on limestone. Also outcrop of hematite has been observed in this area. Designing the field operation grid were carried out based on geology and primary studies of region and therefore 2400 points were along 60 profiles. After diurnal correction، total magnetic intensity has been corrected by using IGRF 2000 so that components of Earth''s magnetic fields have been eliminated. Also reduction to pole operation was done. In this paper، first derivative، second derivative، analytical signal tilt angle and Euler deconvolution methods were used to study fractured structures. The edges of these faults were detected by using analytical signal، first derivatives، second derivatives and the tilt angle method. Then، we use the standard Euler deconvolution method. Reid et al (1990) used this method as Grade analysis. Simple geometric models for magnetic field source (Balkely، 1995) and determination of structural index with earlier data are disadvantages of the Euler method. However، Thompson (1982) and Reid et al (1990) determined the optimum structural index for different structures; nevertheless the Euler method estimated quite well the location and depth of magnetic sources (Aboud et al.، 2005) The results of the first vertical derivative showed three major faults in this area strikes of which were east-west in north، northeastern-southwestern in middle and northwesternsoutheastern in south of the area and the other interpretation methods have an acceptable degree of reliability. The faults specified by using these methods that are offsprings of mineralization have good compatibility with geological information. Also، the depths of the faults in the area were estimated by the Euler method. The estimated depths of the top of major faults in the area were similar at 5 m and 15 m.

In this study، the local site amplification characteristics are investigated at 10 accelerometer stations organized by Tehran Disaster Mitigation and Management Organization (TDMMO) in Tehran city. The microtremor data were selected from 40 days contious recording at each station. The site amplification functions are calculated using horizontal to vertial spectral ratio (H/V) in the frequecny domain. The results revealed large sensitivy to the microteremor sources. When compared the results with previous studies using earthquake data، the computed amplification functions showed the peaks not related to the site or different amplification values at predominant peak frequencies at the sites. To find the reasons، the amplification function at the sites was calculated using microteremor data recorded at weekdays and holidays. we calculated Meanwhile، the results of calculated amplification functions for holidays showed a good agreement with those of previous studies that meansthe industrial noise largly affect the results، especially in the south of Tehran. Furthermore، It was found that the industrial noise mostly affected the frequency range of 1 to 1. 5 Hz. This also led to increase the amplification coefficient in an order of 1. 5 to 2. 2. This emphesized on the fact that the microtremor data recorded in the mega-cities like Tehran should be carefully analyzed to prevent misleading results. Furthermore، it is suggested to use microtremor data recored in the holidays to avoid such heavy industrial noise contamination.

Recent progress in seismology has demonstrated that empirical Green’s functions (EGFs) of inter-station distances can be extracted using cross correlation of ambient seismic noise recorded in the similar time at two stations (Weaver and Lobkis، 2002; Shapiro and Compillo، 2004; Wapenaar، 2004). Consequently، this method provides a great set of data even in low seismicity regions to apply in the tomographic studies. Thus، the resulted tomographic images using the ambient seismic noise method (hereafter ANT) can show interior earth structures with a higher resolution compared to classical tomography methods (Shapiro et al، 2005; Lin et al.، 2007; Shirzad et al، 2013). Diffused signals are the main assumption in the ANT method (Snider، 2004). Ambient seismic noise sources generate a coherent and transient noise wavefield with random amplitude and phase in a medium (Van-Tighelen، 2003; Gorin et al.، 2006). Reconstruction of the propagating path information using the amplitude of the recorded noise wavefield is impossible، but coherent information provided by propagating path can be extracted using cross correlation of long time ambient seismic noise recorded (Weaver and Lobkis، 2004; Gorin et al.، 2006). This coherent information is called elastic response of medium or empirical Green’s functions (Shapiro and Compillo، 2004; Roux et al.، 2005; Sabra et al.، 2005). Generally، the ambient seismic noise recorded for each station is composed of surface waves (Rayleigh and Love) with random amplitude and phase (Aki and Richards، 1980). Cross correlation function of these data will be symmetric if the ambient seismic noise wavefields generated by random sources are distributed uniformly (Snider، 2004). Earth structures can be studied using travel-time of extracted EGFs such as Rayleigh wave fundamental mode (Shapiro et al.، 2005). Some studies (e. g. Stehly et al.، 2006; Pedersen et al.، 2007) indicate that the inhomogeneous distribution of the signal energy in various azimuths، which results in directionality of ambient seismic noise، produces deviation in tomography results and causes incorrect interpretations. Consequently، optimization of extracted tomographic maps based on the ANT method needs comprehensive knowledge of spatial and seasonal distribution of the noise wavefield in study areas (Stehly et al.، 2006; Pedersen et al، 2007). Gutenberg (1936) suggested that the sources of primary and secondary oceanic microseisms observed throughout the Europe are located in the northeastern Atlantic Ocean. Primary and secondary microseisms dominate the noise wavefield in certain frequency ranges. The interaction between the swell and the sea bottom generates the primary microseisms which are dominated by periods of 12–25 s. Also، interfering water wavefield components travelling in opposite directions generate the secondary microseisms which are dominated by periods of 5-10 s (Gutenberg، 1936). In this study، we analyzed three-component recordings of continuous data from 30 stations in the Central Alborz region depicted in Figure 1. The Alborz Mountain range in the southern margin of the Caspian Sea is a part of the Alpine–Himalayan orogenic belt. The Alborz Mountain range resulted from a stress state derived from the horizontal compressive forces of the Central Iran Plateau has been induced by the collision of the Arabian plateau and the Asian continent (Berberian and King، 1981; Zanchi et al.، 2006). The dataset used in this study consisted of 10 digital accelerometers with CMG-5TD sensors operated by the Tehran Disaster Mitigation and Management Organization (TDMMO)، 18 digital narrow-band seismometers with SS1 seismometer sensors (corner frequency ≥1 Hz) operated by the Iranian Seismological Center (IRSC) at the University of Tehran، and two digital broadband instruments with a CMG-3T sensor operated by the International Institute of Earthquake Engineering and Seismology (IIEES). For the TDMMO acceleration network، the IRSC and the IIEES seismic networks continuous data from 2010 were analyzed. In the case of azimuthal distribution of the ambient noise، normalized amplitude of the cross-correlations versus azimuth (rose-diagram) constrained the direction to the sources of the ambient seismic noise، based on all available station-pairs. The average fractions (the number of Love/Rayleigh path with a SNR>10 in a given 20° azimuthal bin was normalized to the total number of Love/Rayleigh paths in that given bin) of the Love and Rayleigh yearly empirical Green’s functions with a SNR>10 were in the orders of 0. 78 and 0. 73، respectively، at the period band of 1–10 s. Our final results indicated that the average fractions per cent of Love and Rayleigh paths with SNR>10 were above 68% and 64% on a yearly scale، and never decreased to 45% and 50% on a monthly scale at the period band of 1-10 s، respectively.

Integration of 3D seismic data with petrophysical measurements gives a better vision to reservoir characterization. The integration of well-logs and seismic data has been a consistent aim of geoscientists which become increasingly important and successful. In recent years، because of the shift from exploration to development of existing fields with a large number of wells penetrating them، improving reservoir study has been the most important pre-drilling activity. One type of integration is forward modelling of synthetic seismic data from the logs. A second type of integration is inverse modelling of the logs from the seismic data. It is called seismic inversion. Seismic inversion، in geophysics، is the process of transforming seismic reflection data into a quantitative rock-property description of the reservoir. Another method is to estimate the log properties by seismic attributes. In this study، linear multiattribute transform، and non-linear multi-attribute transform were used for predicting porosity in one of the Iranian hydrocarbon fields. The analysis data consisted of the target log (in this study، the porosity log) from wells tied with 3D seismic volume. From the 3D seismic volume، a series of sample-based attributes was calculated. The objective was to derive a multi-attribute transform، which was a linear or nonlinear transform between a subset of attributes and target log values. The selected subset was determined by a process of forward stepwise regression، which derived increasingly larger subsets of attributes. In the linear mode، the transform consisted of a series of weights derived by least-squares minimization. These weights are coefficients of the selected attributes in a linear multi-attribute transform. In the nonlinear mode، a neural network was trained using the selected attributes as the input. Two methods of neural network used in this study include probabilistic neural network and multi-layer feedforward network. The basic idea behind the general regression probabilistic neural network is to use a set of one or more measured values، called independent variables، to predict the value of a single dependent variable. The multi-layer feed-forward network method consists of a set of neurons، arranged into two or more layers. There is always an input layer and an output layer، each containing at least one neuron. Between them، there are one or more ‘hidden’ layers. The neurons are connected in the following fashion: inputs to neurons in each layer come from outputs of the previous layer، and outputs from these neurons are passed to neurons in the next layer، and each connection represents a weight. To estimate the reliability of the derived multi-attribute transform، cross-validation was used. In this process، each well was systematically removed from the training set، and the transform was rederived from the remaining wells. Then، the prediction error was calculated for the hidden well. The validation error، which is the average error for all hidden wells، was used as a measure of the likely prediction error when the transform was applied to the seismic volume. There was a continuous improvement in predictive power as it was progressed from a single-attribute regression to a linear multi-attribute prediction. This improvement was evident not only in the training data but، more importantly، in the validation data. In addition، the neural network did not show a significant increase in resolution over that from the linear regression. As a conclusion، the best result of porosity estimation in this field was provided by the linear multi-attribute transform.

The behaviour of Ground Penetrating Radar (GPR) electromagnetic field can be simulated using Maxwell’s equations and associated boundary conditions. So far a number of numerical methods for modeling GPR data have been proposed including the popular Time Domain Finite Difference (TDFD) technique. The popularity of TDFD is mainly due to being relatively simple to implement، its high flexibility and capability to simulate complex subsurface geology. Also، the TDFD approach is not only conceptually accurate for complex geological models but also enables us to design realistic antenna and to study physical electromagnetic phenomena such as dispersion in electrical properties. Despite having these advantages، the finite difference method has pitfalls such as becoming very time consuming in simulating the most common media especially with high dielectric permittivity causing the forward modeling process to become very time consuming even by modern high-speed computers. Synthetic GPR responses are useful for predicting expected GPR data over known geometries such as horizontal cylinders and prisms. The GPR data and subsurface lithological and hydrogeological properties are related through a numerical forward modeling engine. Therefore، the GPR forward modeling engine can transform the subsurface electrical properties into expected GPR responses which in turn can be used for optimizing data acquisition procedures on pre-defined subsurface targets. Since any efficient inversion routine requires a fast forward modeling engine، this study aimed the development of a fast forward modeling algorithm capable of being implemented in any inversion routine. To have efficient numerical forward modeling algorithm، we have adapted a leap-frog، staggered-grid approach introduced by Yee، which incorporates offsetting the electric and magnetic field components in both space and time in such a way that the finite difference approximations of the governing partial derivatives in each equation are centered on the same spatiotemporal location. Obviously 2-D modeling is limited and cannot fully account for antenna behaviour and out-of-plane variations in material properties; however، many important features common to most GPR responses can be identified via employing a computationally cost effective 2D algorithm. In the current study، the patterns of GPR responses that are well known to be hyperbola in shape are used as leading models in order to reduce the execution time. A GPR system collects the reflected pulses coming from different depths in the form of traces which when gathered along with a profile، they make a GPR section called radargram. In general، the simulated GPR traces of common reflected objects are time shifted like the Normal MoveOut (NMO) traces encountered in seismic reflection responses. This property suggests the application of Fourier transform to the GPR traces and the use of time shifting property of such transformation to interpolate traces between the adjusted traces in frequency domain. Therefore، the lateral resolution of GPR traces computed along with any profile is enhanced using a linear interpolation in the Fourier domain resulting in an increased speed of the forward modeling algorithm. Selecting the minimum lateral trace to trace interval with the appropriate sampling frequency of the signal، prevent any aliasing to occur. It is shown that such methodology can significantly decrease the computing time by more than 12. 5 times.