مجله ژئوفیزیک ایران
سال شانزدهم شماره 3 (پیاپی 56، پاییز 1401)

Convective storms are the most atmospheric hazards that can cause significant dangers to sectors such as agriculture, industry, aviation industry and urban and rural facilities in different parts of the world. These storms are often accompanied by thunderstorms, hails, floods or severe winds. Severe dust from some convective phenomena has also been identified as air and environmental pollutant. Therefore, studing their various aspects has provided the basis for many atmospheric studies in different parts of the world.Accurate and timely forecasting of convective storms is still challenging in operational weather forecasting centers. Given that conventionally configured numerical models did not accurately predict this phenomenon in most cases, their nowcasting using satellite data is of great importance. Meanwhile, geostationary satellites have been recognized as a useful tool for identifying areas with potential convection. In this paper, an attempt is made to provide an algorithm using the Meteosat8 data to identify areas prone to the convection initiation and to improve the issuance of timely warnings in a very short time. This work has been done by studying the convective events during the day and night hours of spring and summer of 1397 in Tehran province. The present algorithm is based on  selecting 22 fields using the brightness temperature of infrared channels, the reflectance of visible and near-infrared channels and the differences and their 15 and 30 minute trends. The preprocessing performed on the satellite data are radiometric and geometric corrections. To do this, after calculating the radiance of individual channels from the digital counts, the brightness temperature of the infrared and reflectance of the visible and near infrared channels are calculated. Also to calculate the trend of the fields between two time steps, a spatial low-pass filter using the box-averaging method has been used. Large-Scale atmospheric pattern analysis of the ten strongest dust convective storm events in Tehran in the last decade shows that in most of them, at least five days before the storm, a ridge of geopotential height at the 500 hPa level developed on the area. On the day of the storm, the passage of the geopotential trough and the cold front of the surface destroyed the atmospheric stability and released the energy necessary for the development of the cumulonimbus cloud and the occurrence of the storm. Finally results show that using this algorithm to nowcasting the convective initiation, the probability of detection and correct alarm rate are 62 and 79 percent, respectively.

Estimation of compressional and shear wave velocities is very important in the oil and gas industry. Unlike compressional wave velocity, shear wave velocity is not measured in all wells of a field due to its higher costs. Therefore, using an alternative method that estimates the shear wave velocity at a lower cost and with acceptable accuracy is inevitable. In this study, to estimate the response variable in a well, the correlation of several logs in that well (i.e., acoustic logs, density, neutron porosity, resistivity, gamma ray, dolomite volume, quartz volume, and water saturation) with target log investigated. It was found that the compressional wave velocity, density, dolomite volume, and quartz volume logs are more correlated with shear wave velocity. Therefore, these logs were selected as input features for estimating shear wave velocity using different approaches. In the next step, among the various methods, The estimated values obtained from a method that has the best match with the actual shear wave velocity is introduced as the optimal model. Afterward, it is performed to estimate the shear wave velocity in other wells that do not have a shear wave velocity log. In this paper, multiple regression methods and machine learning algorithms (support vector regression, adaptive Neuro-fuzzy inference system, and deep artificial neural network) were applied to predict the shear wave velocity. In this study, data from seven wells were used. Due to the fact that only in well #7 shear wave velocity has been measured, and in six other wells this feature has not been recorded, this field data limitation has caused the data of well #7 to be divided into training, testing, and validation data. In multiple regression methods (linear and interaction models), support vector regression, and adaptive Neuro-fuzzy inference system, Randomly, 70% of the data has been used for training and 30% for testing, but in the artificial neural network method, Randomly, 70% of the data has been used for training, 15% for validation and 15% for network testing. For all methods, the root means square error and correlation between actual and estimated data are calculated. Linear model, interaction model, support vector regression, adaptive Neuro-fuzzy inference system, and deep artificial neural network have provided 91, 92, 89, 94, and 98% correlation in training data, and 88, 89, 86, 90 and 92% in testing data, respectively. Also, the RMSE for each of the mentioned methods is 125.59, 115.86, 148.23, 84.36, and 80.49 (m/s) in the training data and 139.77, 133.44, 166.03, 126.15, and 98.04 (m/s) in the testing data, respectively. Our results show that deep artificial neural network has provided a better solution than other methods. Hence, in this study deep artificial neural network has been proposed to estimate the shear wave velocity in other blind wells. Moreover, the Castagna empirical model was used to validate the obtained results from the deep artificial neural network in these wells, which show a good fit between the two models.

Precipitable water (PW) is an important part of the water cycle in the atmosphere. This variable plays a significant role in hydrological and meteorological studies to understand the behavior of water vapor and the involved processes. One of the management tools in this area is knowledge of the Total Precipitated Water content (TPW) in the atmosphere. This quantity can also be used as a meteorological index for accurate prediction of atmospheric parameters including rainfall and runoff estimates. PW is the amount of vertically integrated water vapor and can be expressed in g/cm2, or as the height of an equivalent column of liquid water in centimeters. The traditional technique for PW measurement is to launch radiosondes, normally twice a day. This method is not only expensive but also poor in both spatial coverage and temporal resolution. Temporal changes in atmospheric water vapor occur rapidly and water vapor measurements by radiosondes do not satisfy the needs of research for a variety of variation scales of atmospheric water vapor. In past years, the emerging ground-based Global Positioning System (GPS) has opened the possibility of improved monitoring of atmospheric humidity. Beginning from the 1990s, an observational technique based on the Global Navigation Satellite System (GNSS), which is sensitive to the spatial and temporal distribution of the water vapor content in the atmosphere, has made it possible to retrieve precise and continuous estimates of PW. In recent years, studies have been conducted on the use of satellite data and GPS data to estimate this parameter. The purpose of this research is to evaluate the post-processing of WRF model for PW using meteorological satellite data, neural network and Kalman filter. This parameter cannot be easily calculated without the use of equipment such as GPS, satellite, radiosonde station and radar. In this research, we attempt to eliminate the systematic error of a PW parameter obtained through the WRF numerical model in the absence of measurement data. In the first step, we compare the rainfall water for two stations in Iran including Mashhad and Tehran with the TPW parameter measured by MSG1(IODC) and FY-2E satellites for about two years. Then, the WRF model results are post-processed based on the observational data to educate the neural network model using the ANFIS implementation with genetic algorithm training. In second step, the Kalman filter was applied to the satellite data modified by genetic algorithm. The results were improved and the correlation coefficient was to 0.987 and the RMSE error reduced to 1.1. Results show that by implementing this approach and converging on the correlation values of the selected satellite and the post-processed model to the correlation values of the modified model, the results showed that the meteorological satellite data METEOSAT8 (IODC) can be used as an alternative of radiosonde data in places without high atmosphere station to teach the proposed model for post-processing of PWV parameter in WRF numerical model.

The presence of subsurface anomalies such as cavities, sinkholes, weak subsurface layers, faults, or tunnels that are man-made or natural can pose serious risks to the environment or engineering project. One of the anomalies is weak or loose layer between two hard layers which increases the settlement potential, so detecting the subsurface anomalies such as void and weak layer at different depths and thicknesses plays an important role in engineering designs. In this paper, the multi-channel analysis of surface waves (MASW) is used. It is fast and comfortable among the various methods for identifying subsurface anomalies. In this regards, the effect of weak layer between hard soil layers is evaluated by using the finite element method in Abaqus and MATLAB software. The effect of depth and thickness of weak layer on the phase velocity spectrum and theoretical dispersion curve of Rayleigh (R) wave is compared. Different thicknesses (2, 4 & 8 meters) and depths (2, 4 & 8 meters) for weak layers are considered. In the following, the output results of each simulation are processed in MATLAB software by using the MASW method.The results showed that the presence of jumps in phase velocity spectrum in high and low frequencies are related to the existence of weak layer. The location of the jump is moved to the low frequencies of the phase velocity spectrum and also shifted to higher modes of theoretical dispersion curve of R-wave due to the increasing depth of weak layer. For example, the jumps are accrued at the frequencies of 37, 27 and 17 Hz in the phase velocity spectrum due to the presence of the weak layer at depths of 2, 4 and 8 meters, respectively. This event is caused by the more energy of the higher modes at special frequencies related to the depth of the weak layer. By increasing the thickness of the weak layer, the number of jumps is increased in the phase velocity spectrum and the phase velocity spectrum is shifted from fundamental mode to the first, second and other modes at the location of each jump. The number of jumps for a weak layer with a thickness of 8, 4 and 2 meters is equal to 6, 2 and 1, respectively, which indicates a higher number of jumps for a thickness of 8 meters. Considering these changes in inversion analysis gives rise to more acceptable S and P wave velocity profiles.

In the recent decade, some effort is expended in deviating and attenuating seismic waves, following the sophisticated electromagnetic-wave (photon) control by a well-known synthetic-material technology: metamaterial. There are many reports in the literature on successful control of mechanical waves (phonons) in acoustic and thermal frequency ranges by phononic crystals and metamaterials; however, they fail to efficiently control waves over low-frequency ranges, like seismic and earthquake waves. The bottleneck is the very long wavelengths of the earthquake waves which inevitably necessitates large-scale structures for seismic metamaterials. This research intends to dampen destructive shear waves before reaching the target structures by designing metamaterials with small dimensions and easy portability and execution. The rationale of this study is that in earthquakes, a significant part of the damage to the building is caused by the excitation of the first vibrational mode, and if the waves with frequencies near to the frequency of the first vibration of the building are filtered out or damped from the frequency content of the input excitation, the resonance phenomenon may not occur, and damage to the building is expected to be significantly reduced. In this study, with the aim of protecting typical two- to six-story buildings, the frequency range of the first vibrating mode is determined based on the experimental relationships presented in Section 6 of the Iran National Building Code. Then, utilizing the proposed dispersion relation for the one-dimensional metamaterial, the geometry and material of the metamaterial base resonator are designed so that the metamaterial stop-band corresponds to the target frequency range which is supposed to be filtered out. In order to facilitate the implementation, efforts have been made so that the dimensions of the base resonator are not large. The metamaterial designed for placement in medium clay, is a cylinder with a diameter and a length of 0.5 m of lightweight and high-strength concrete in which the interior lead cylinders are located in an intermediate filling fluid. Using the simplified continuous model of the proposed metamaterial, its performance against the shear wave with the frequency located in the target stop-band has been investigated. Based on this study, the proposed metamaterial is able to quickly reduce the amplitude of shear waves of an earthquake. This metamaterial can be used to protect urban buildings built near faults. It might also be used for cases where earthquake-resistant systems cannot be installed in the body of structures or foundations (e.g. historical and sensitive buildings, rural or old urban areas, etc.).

Recording, processing and identifying the parameters of independent earthquakes larger than Mw = 4 using advanced equipment, software and professional experts, can be calculated nowadays in less than 10 minutes automatically in the Iranian plateau. Double earthquake of November 14, 2021 with a small time difference (less than 90 seconds) occurred at a very close distance with magnitudes of 6.1 and 6.4, respectively. The calculation of the parameters of location, time and magnitude of the first event, in addition to the calculation of its focal mechanism parameters, was made possible with the help of semi-automatic or automatic software. However, the interference of the seismic phases of the first event with the second one in farther stations poses a serious challenge to the automatic and semi-automatic calculation of location, origin time, magnitude, as well as the parameters of the focal mechanism of the second event. In this study, by merging recorded seismograms and accelerograms in seismic stations within the country, the parameters of this double earthquake and their two main aftershocks with magnitudes of 5.1 and 5.4 have been calculated. In addition, the calculation of the focal mechanism of these four events is carried out based on the polarization method of the first P-wave polarity, the amplitude ratio of P and S waves and the modeling of seismic moment tensor. The results of this study show that according to the locations and focal mechanisms calculated for the double earthquake and both major aftershocks, the activity of the Handun basement fault, which is probably related to the formation of the Handun salt dome, caused double earthquakes of November 14, 2021 and its two main aftershocks in the northwest of Bandar-Abbas, south of Iran.

The inversion of gravity data is one of the most important topics in the quantitative interpretation of practical data, since construction of density contrast models could increase the amount of information that can be achieved from the gravity data.     This study shows the gravity response to the assumed model. In this model, , where tthe sub-surface ground has been partitioned into 15×10 prisms ands the dimension of each prism is 20 m × 5 m. As is shown, the 2D model includes 9 prisms whose density contrast is 1000 kg/cm3. The inverted gravity correspondings to the resulted causative body from inverting the observed gravity. This inverted model that is corresponding to the exactly similar of to the original causative body, achieved at 4th iteration, where the L2 norm as the stopping criterion attained the smallest amount. We have used the reweighting focusing inversion method for inverting the gravity horizontal gradient data. The computed density distributions are located on the vertical borders of the sub- surface mass. The gravity horizontal gradient related to the assumed model. The inversion response to the gravity horizontal gradient related to the assumed model is demonstrated, asshows the estimated densitiesy are situated on the vertical edges of the model. The L2 norm of the as shows gravity horizontal gradient is 0.0036 mGal/m. We have applied the reweighting focusing inversion algorithm to invert the 2D gravity and its horizontal gradient data related to a salt dome situated in the Aji Chay, East Azerbaijan Province, Iran.from Iran.The calculated gravity horizontal gradient has been shown, where the L2 norm is 0.0833 mGal. The real gravity is related to a salt dome, situated in the Aji Chay, East AzerbaijanProvince, Iran. Salt domes in this region in terms of potash reserves are significant in terms of potash reserves. The inverted density contrasts for the sub- surface salt dome. We have applied the reweighting focusing inversion algorithm to invert the 2D gravity and its horizontal gradient data related to a salt dome from Iran. The method has been tested for a theoretical gravity set data, with and without added random noise random. The results demonstrate the ability of the reweighting focusing inversion method in recovering subsurface density contrast distribution and detecting the vertical border.

This study investigates the efficiency of different geophysical methods for detecting contaminated zones as well as hidden pipes. There are a number of geophysical techniques that can be useful and beneficial for achieving these purposes. Ground penetrating radar (GPR) and electrical resistivity methods are the most useful approaches for identifying polluted areas and buried features, causing us to utilize such methods in the area. Although GPR and electrical resistivity methods are solely effective for mapping subsurface targets, the integration of these methods might provide us valuable information and valid outcomes. In this paper, electrical resistivity and GPR surveys are conducted and acquired data are modelled to investigate the pollution leakage in the ground. To achieve this aim, a polyethylene pipe with inlet and outlet valves is considered as a tank buried underground in the geophysical test site of Shahrood University of Technology. In the first step of the study, the electrical properties of the empty pipe were measured and modelled using electrical resistivity and GPR methods. In the next step, the pipe was filled by the cupric sulfate solution and electrical measurements were performed again. In addition, the electrical properties were investigated by means of electrical resistivity and GPR methods in another area, sitting on alluvial soil, after and before injecting the cupric sulfate solution. It is worth mentioning that the measurements were performed after drying the surface which was humid due to the injection of cupric sulfate to avoid false conductivity on the ground. The designed profiles include four lines on a buried reservoir and five profiles on alluvial soil. However, the 2D modeling results and interpretation of the acquired data along with two (on the buried target) and three (on alluvial soil) survey lines are just presented in this paper. After collecting and interpreting the electrical resistivity and GPR data in the study area, the results are in the form of two-dimensional models of electrical resistivity and GPR cross sections, which have been obtained by use of RES2DINV and Prism2 softwares, respectively. The obtained results revealed that the electrical resistivity method enjoys a relatively good performance in detailed detection. It is also shown that the GPR method is not able to work properly in alluvial soils with high clay and it suffers from the unsuitability of the transmitter antenna with a frequency of 150 MHz and the destructive effect of moisture on the performance. Regarding the obtained outcomes and using electrical resistivity data, ground details such as polyethylene reservoir, loose and porous soil parts and separation of different areas according to the relative humidity in them have been revealed.

In the present study, probabilistic seismic hazard assessment (PSHA) and disaggregation of seismic hazard in Golestan and adjacent areas have been conducted. The study area includes Golestan province and surrounding areas to about 150 km away from the geographical borders of the province. For this purpose, at the first stage, geological and seismotectonic studies, including the tectonic features, faults dimensions, and faults mechanism in the study area have been studied. The seismicity parameters including b-value, annual mean occurrence rate (λ) and maximum possible magnitude (Mmax) of the study area are evaluated based on the uniform catalog containing historical and instrumental earthquakes from 400 BC to the end of 2021 AD with magnitudes ranging from 3.5 to 7.5 on the Mw scale. Based on the geological and seismological information of the study area, 21 areal seismic source zones have been identified. Using the spatial distribution function, the annual mean occurrence rate of earthquakes of each seismic source has been determined in different magnitude intervals. The latest three local ground motion prediction equation models of the Iranian plateau, have been employed to assess the peak ground acceleration (PGA) and spectral acceleration (SA) in periods of 0.2, 1, and 2 seconds for return periods of 50, 475 and 2475 years. A three branches logic tree with the same weight has been used to apply the GMPEs. The GMPEs are specific to Iran and used for the first time in the study area. The entire study area is divided into points that are 5 km apart. Subsequently, the PGA and SA have been calculated for each point with the OpenQuake software package. The results are presented in the form of seismic hazard zoning maps in Golestan province and spectral acceleration curves for Gorgan city. The calculations are on the bedrock site condition according to the Iranian code of practice for seismic resistant design on building (Standard No. 2800 4th edition). In order to determine the most effective seismic source in the PGA of the Gorgan, the disaggregation of PSHA has been conducted on the parameters of distance, magnitude, the number of standard deviations (ε), and latitude and longitude. The most probable scenario of maximum acceleration in Gorgan has been introduced. The results show that some areas of the Gorgan, Kord-Kouy, Agh-Ghala and Ali-Abad in southwest and Maraveh-Tapeh in northeast of the province can be affected by more ground acceleration than other areas so that the maximum ground acceleration in these areas for the return period of 475 years is 0.21g to 0.25g. The disaggregation of PSHA for Gorgan city shows that the Caspian and North Alborz faults have the most substantial contribution in the PGAs of Gorgan.

The Iranian Plateau is a part of the Alpine–Himalayan orogenic belt located in the western part of Asia. Convergence of the Arabian Plate and Eurasia from the late Cretaceous to the present has generated significant lithospheric deformations such as crustal shortening and thickening in the Plateau and surrounding mountain ranges including the Zagros Fold–Thrust Belt, Alborz and Kopeh Dagh. The convergence across Zagros is accommodated through different mechanisms of diffused shortening and/or thrusting of the Arabian lithosphere beneath Central Iran. This study focuses on the velocity structure of the eastern part of the Iranian Plateau which has not been studied well yet. Our study region also contains Binalud and Kopeh-Dagh deformation domains which were built up by the north-eastern collisional boundary between the Plateau and Eurasia and a small part of the Urumieh-Dokhtar magmatic arc which was the volcanic arc of the past Neotethyan subduction. The lithosphere-asthenosphere system beneath east of Iran is investigated by employing earthquake surface wave tomography. A total of 5862 teleseismic Rayleigh waveforms from 368 events recorded at three permanent networks during a period of three years were used to produce 2-D high-resolution phase velocity maps. We employed a two-plane wave tomography approach to generate phase velocity maps at period ranges of 25–111 s. From a published study of ambient noise tomography, we extracted Rayleigh wave dispersion data at 8–20 s periods to improve resolution in the crust and then inverted them for a 3-D S-wave velocity model. A 3-D velocity model was then constructed by a nonlinear Bayesian Markov chain Monte-Carlo algorithm of local node-wise dispersion data into S-wave velocity models down to a depth of 200 km. The most prominent resolved feature by our 3-D velocity model is a low-velocity asthenospheric channel at 70 and 150 km depths overlaid by a thin lithosphere. We believe that in the lack of an isostatic compensated crustal root in the Iranian Plateau, this feature is supporting high elevation (~1000 m) topography covering the Iranian Plateau. A Moho map for the study region is obtained by mapping the geometry of 4.0 km/s S-wave velocity contour in the 3-D velocity model. It shows that most of the study region is covered by a less deformed crust with a thickness of ~36 km. Two crustal roots are observed, one beneath the Urumieh-Dokhtar magmatic arc and the other beneath the north-eastern part of the study region where an array of the Neotethys suture zones is marked by ophiolite outcrops. Lithospheric scale deformation in a sequence of Neotethys suture zones is high probably responsible for the crustal thickening in NE Iran.

Seismic interferometry is a method that allows the retrieval of the seismic response at one receiver from a virtual source at the position of another receiver. In recent years, seismic interferometry has become a common tool in seismological studies. With continuous recording data in seismic stations, new opportunities are available to seismologists to present new hypotheses and studies. Continuous data contains ambient noise which has random amplitudes and phases and can propagate in all possible directions, so by using ambient noise, it is possible to retrieve responses between two seismic stations. In most applications, long-duration vertical and horizontal components of continuous time series recorded at two stations are cross-correlated to approximate the Green’s function between the two stations. After stacking all available data for each station’s pairs, group velocity dispersion curves are measured using multiple-filter analysis for each EGF signal. In our research, group velocity dispersion measurements are used for tomography. The radial anisotropy can be inferred from the Love–Rayleigh (L–R) discrepancy, that is, from the difference between the travelling speeds of horizontally and vertically polarized surface waves. Seismic anisotropy in the Earth’s crust is the signature of past and ongoing crustal deformation. Thus, observation of this anisotropy reflects the nature and distribution of the deformation bearing on the crustal rock. In conventional seismic tomography techniques, it is necessary to determine the dimensions of grids or other parameters such as smoothing and damping. A two-step inversion algorithm is employed to solve the tomographic inverse problem. In this study, transdimensional tomography is applied, which adapts to a non-uniform data coverage without requiring any arbitrary regularization such as damping or smoothing. It is a one-step non-linear tomographic algorithm. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps. The study area in this research is the Central Alborz area (latitude 34.5-38 N degrees and longitude 48.5-54.5 E degrees). We process all available contentious vertical and horizontal seismic data from the Iranian Seismological Center (IRSC) and the International Institute of Earthquake Engineering and Seismology (IIEES) in this region for the three years from 2013 through 2016. Also, we use data from earthquakes with magnitude above 3.5, which were recorded on these stations between 2006 and 2016.The results obtained from seismic tomography and radial anisotropy show the possibility of separation between different tectonic seismic regions based on the velocity anomalies and radial anisotropy. Moreover, radial anisotropy shows the cooled Damavand magma at a depth of about 20 km.

A determinant factor in generating qualified seismic images of subsurface layers is the coherency of reflection events in pre-stack seismic data. Accordingly, this level of coherency could be reduced due to the complexity of propagation media or operational errors during seismic acquisition. It will be challenging to process acquired data if these incoherencies are not compensated for. In mountainous seismic acquisition projects, displacement of the source is one of the more common causes of errors. Such displacement could be happened for receiver points as well, due to the access or operational difficulties in seismic acquisition. In the processing stage, there are some methods for improving coherency based on the assumption that incoherencies are stationary both in time and offset. In particular, it is assumed that all reflection events shifts are static in the time domain and predictable in the offset domain. Thus, they can be compensated by conventional static and dynamic corrections. Nevertheless, there are some violent circumstances where the shifts will no longer be predictable. The displacement of source or receiver stations without resurveying of them and the presence of velocity anomalies in wave propagation media are some examples. These often occur simultaneously in mountainous seismic terrain. Conventional static and dynamic corrections cannot properly rectify such non-stationary inconsistencies in the mentioned circumstances. Although applying the residual static correction to compensate for such incoherencies can improve coherency for some reflector’s events, especially those with good amplitude and frequency content, for the others it can actually make it worse, especially for deep reflectors. For precise seismic reflection imaging, the magnitude of non-stationary shifts plays a key role. Small shifts will result in a reduction of the frequency content of the final stack section. For bigger size of these shifts, the quality of stacked reflections will be reduced and then can be eliminated, consequently. As a well-known fact, in seismic operations, logically increasing frequency content in order to get a better resolution usually involves spending significant time and money. However, with the mentioned operational errors, this is essentially wasteful. To avoid such operational errors, the activities of different crews of the seismic project should be thoroughly checked, in particular, the survey and drilling crews for the accuracy of elevation and coordinates of the shot points. Using both synthetic and real data, this paper attempted to explain how reflection event incoherencies frequently occur during mountainous seismic acquisition, and then proposed an algorithm for compensating those incoherencies with a non-stationary time shift correction in pre-stack seismic data.

Landslides are natural hazards that cause severe fatalities and financial losses. Various methods are used to analyze landslides. Among those, geotechnical and geophysical methods are used due to their accuracy and low cost, respectively. In geophysical methods, electrical resistivity tomography (ERT) is widely used for near-surface exploration of landslide areas characterized by a complex geological environment. In this study, two-dimensional electrical resistivity tomography (ERT) studies have been used to define the subsurface structure and landslide geometry of Chaybagh. Over the past decade, technological advances in field data acquisition systems and the development of new algorithms for tomographic inversion have made this technique more suitable for studying landslide areas. In order to reduce the possible damages in the preliminary studies of construction projects, especially linear structures, it is very important to study the areas with slip potential.Following the widening the road in a part of Sari-Shirgah road in the Chaybagh area of North Savadkuh County, which overturned a freight train downstream due to bending of railway tracks, a large gap was created. In order to determine the cause of this accident and the possibility of landslides in the area, 2D geoelectric tomography was performed and necessary considerations were taken into account for reconstruction of the new road to prevent further damages.According to the geological map of Ghaemshahr 1/100000, the study area includes conglomerate, silty marl, sandstone and siltstone. At the bottom of the landslide area is the Talar River bed, which includes alluvial fans, floodplains, old and young rivers and streams, alluvial floodplains, older coastline deposits, and non-hardened alluvium of the present age in the bed of rivers. In this study, the data obtained from the profiles with Dipole-Dipole and Pole-Pole arrays and geoelectric sounding were interpreted by two-dimensional inversion method in Res2dinv software.The results of the study show that on the upper slope of the road, a small landslide surface is observed at a depth of 4 to 5 meters. This landslide occurred at a small level and has nothing to do with landslides and road gaps, but there is a large-scale landslide potential in the area with landslides at a depth of 30 to 35 meters above the road surface in the future so that groundwater flux accumulation can trigger landslides. Therefore, designing of drainage for engineering structures in this part of the road is essential to prevent road collapse, accidents and possible losses. Moreover, the possibility of landslides in the designs should be considered.