جستجوی مقالات مرتبط با کلیدواژه « سرعت موج برشی » در نشریات گروه « زمین شناسی »

This article deals with the strength evaluation of concrete obtained by adding different percentages of three types of nanominerals, including nanocalcite, nanobarite and nanofluorite. To measure the velocity of ultrasonic waves and compressive strength of concrete, 15×15×15 cm cube samples were prepared with 7-, 28- and 90-days curing. 10 types of mix  designs with 0.39 water-cement ratio, including the control sample (without additives) and the samples with 0.5, 0.75 and 1% nanominerals were subjected to the mentioned tests. The results showed that the addition of nanocalcite, nanofluorite, and nanobarite with values of 0.75%, 1%, and 0.75%, respectively, have the highest compressive strength compared to the control sample. Although these do not have pozzolanic properties, they play a positive role in increasing the concrete strength by filling concrete voids and due to their high specific gravity, increasing concrete density.

Shear wave velocity is one of the important parameters for determining mechanical and petrophysical properties in hydrocarbon reservoirs. Shear waves are usually obtained from dipole sonic imager (DSI) tools or core analysis in the laboratory. However, these methods as common sources for shear wave estimation are time-consuming and costly and thus can only provide information on shear wave in a few drilled wells. To overcome these limitations, different artificial intelligence methods are used to estimate the mentioned parameter through the conventional well logs. In this study, the shear wave velocity was estimated using ensemble learning methods in Asmari Reservoir in the Mansouri oilfield. In this study, shear wave velocity was estimated using ensemble learning methods such as voting, stacking, bagging, and boosting in the Asmari reservoir, and the results were compared with conventional models such as Linear regression (LR), support vector regression (SVR), nearest neighbor algorithm (KNN), decision tree (DT), neural network (ANN) and hybrid methods such as combining neural network with genetic algorithm (ANN-GA) ), particle swarm (ANN-PSO) and fuzzy systems (ANFIS). In order to evaluate and validate the models, correlation coefficient (R2) and root mean square error (RMSE) were used. A comparison of conventional models and hybrid methods with ensemble learning methods showed that ensemble algorithms perform better in shear wave estimation. Considering the testing phase, among the ensemble learning methods, the Catboost model has provided the lowest error (RMSE=0.058) and highest correlation coefficient (R=0.983) can determine shear wave with high accuracy.

Dynamic and static properties of the rocks are very important for designing geotechnical structures and modeling rock foundations. The main purpose of this paper is to present the regional and global relationships between the static and dynamic elasticity modulus with an experimental approach and to estimate the shear wave velocity of limestone by statistical methods and artificial neural network (ANN). For this purpose, petrographic, physical and mechanical experiments were first conducted on 80 limestone cores from the Karun 4 dam site. A database was then created using the literature data and compared with the results of this study. The results of statistical analysis show that the ratio of dynamic to static modulus of elasticity for the studied samples is 2.5. Also, the ratio of dynamic to static Poisson for these rocks was 1.41. The average value of the dynamic modulus obtained from the literature was equal to 19.90 GPa, which is less than the average value of the dynamic modulus of the present study (31.20 GPa). Due to the most accurate fit, the global relationship (R2 = 0.98, RMSE = 7.9, MAPE = 1.67) and the regional relationship (R2 = 0.96, RMSE = 5.24 MAPE = 0.91) were presented with very high accuracy between the dynamic and static modulus of elasticity. The results of artificial neural network and multivariate regression showed that estimation of shear wave velocity (Vs) based on P-wave velocity, water absorption and density is possible with high accuracy. The results showed that the ANN accuracy (R2 = 0.98, RMSE = 0.27) was higher than the multivariate linear regression (R2 = 0.86, RMSE = 0.39). The neural network also acts conservatively in predicting this variable.

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.

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.

Earthquakes are one of the natural hazards that have caused many casualties and financial losses around the world. This is why earthquake risk analysis studies need to be conducted more seriously. Iran is located in one of the seismogenic regions of the world, the Himalayan-Alpine belt, which is subject to many earthquakes every year. The Arias intensity, as one of the important seismic parameters, helps in seismic hazard analysis, and can be used to estimate structural performance, slope stability, and liquefaction during an earthquake. The region under study is in Khoy area in the northwest, near the Iran-Turkey border. The geographical area covers 38.5°N, 43.5°E to 39°N, 45°E. The main features of the region are the presence of the major lakes of Van and Ercek in the west, and also the Iran-Turkey border in the middle of the map.Arias (1970) created an equation for measuring the intensity of an earthquake based on the time-integral of the of the square of the ground acceleration, which was later used by some researchers to evaluate the damage potential. Harp and Wilson (1995) found out that the Arias Intensity is reliably linked with the distribution of landslides caused by an earthquake. Later on, Kayen and Mitchell (1997) proposed a method for evaluating the soil liquefaction during an earthquake’s strike with the help of the Arias Intensity. In another work (Cabañas et al., 1997), a noticeable correlation was found between the Arias Intensity and MSK scale. It was also revealed that for certain structures, such as the ones found in villages and adobe and brick buildings, the damage could be linked to the Arias Intensity. More recently, Borja et al. (2002) used the Arias Intensity as a measure for comparing the results of two seismic response analyses. Additionally, for determining damage indexes, such as the value of destructiveness potential factor , where la is the Arias Intensity, and V0 is the amount of the zero crossings of the acceleration–time history, this parameter is used (Araya and Saragoni, 1984).

The Arias Intensity is one of the seismic parameters which is usually used in seismic hazard analysis to shed light on the potential damage earthquakes may cause. The Arias Intensity, as a scale of the shakings linked with an earthquake in terms of the amount of cumulative energy, is defined as an infinite set of single degree-of-freedom oscillators of unit weight with the frequency of zero to infinity (Elnashai and Sarno, 2004). This parameter correlates with the integral of the square of the module of ground acceleration over the time history of an earthquake. The Arias Intensity is a parameter which includes characteristics such as the domain and duration of ground motion for a wide range of recorded frequencies. Thus, compared to the parameters depending on the maximum value of ground motion, it is far more effective for evaluating earthquake impacts on engineering targets. In fact, evidence (Khademi, 2002; Mamseyedeh et al., 2021) supports that the Arias Intensity is to correlate proportionately with the damage caused by an earthquake, making it a reasonable choice when describing shakings capable of causing instability in structures, landslides (Chousianitis et al., 2014; Travasarou and Bray, 2003), and liquefaction (Kayen and Mitchell, 1997; Orense et al., 2015). 

The a and b values were calculated using the Gutenberg and Richter law (Gutenberg and Richter, 1956) in MapSeis software (Figure 3.). To complete the information about the regions seismic sources, the iso-potential maps shown in Figure 3 were used. As is seen in the figure, the a-b values of the center of the region of the study were higher. These higher values indicate more seismic activity in the area (Kanamori, 1981; Kanamori, 2013; Wyss, 1975; Wyss et al., 2001). On the other hand, the accumulation of energy in the areas with a low b value shows their potential for future earthquake events. Of course, along with the consideration of this variable, other variables such as a and λ, in addition to the seismotectonics of the region, must be observed to locate future earthquakes with minimized error (Jarahi, 2017). Observing Figure 2 and Figure 3, along with noting the location of seismic sources in the region, reveals that three areas (A, B, and C) are to be expected to be subject to future earthquake events. Altogether, the mentioned revelations prove that the region is  highly seismic. The information formed the basis for the analyses done by Ez-Frisk for further calculations. Figures 4 to 6 show the Arias Intensity iso-potential maps for probabilities of 20%, 10%, and 5%, respectively in 100 years.

The Arias Intensity map can be used as one of the most important indexes for measuring the destructiveness of an earthquake. In this study, for the three return periods of 475, 975, and 2475 years, the Arias Intensity was examined and analyzed. During the study, the main seismic sources of the region were identified and their seismic parameters were calculated. Three areas, marked A, B, and C, were introduced as the areas with potential for future earthquake events. In the Arias Intensity of the region, some specific points were noticed. In general, the further the area is from the seismic source, the less is the intensity of the earthquake there. Nevertheless, it is not always true. That is because another important parameter, known as shear wave velocity, can control this pattern or even reverse it. In various regions, such as the land surrounding the Shkriazy fault, this reverse pattern was detected and that displays the effect of shear wave velocity on the Arias Intensity distribution. Considering the history of liquefaction during earthquakes in this region, the results of the current study could serve as the basis for liquefaction and landslide studies conducted there. From among the three recommended areas, area B is adjacent to a relatively large sedimentary basin (northeast of Salmas), where the very low shear wave velocity makes it prone to liquefaction. Therefore, the potential for the occurrence of future earthquakes, on the one hand, and the conditions increasing the likelihood of liquefaction and resonance, on the other hand, make area B the most dangerous one in the region. Moreover, it is recommended that in the three areas discussed in this study, measures be taken for reinforcing and strengthening structures to prepare them for future earthquake events.

Most rock physic models are developed for sandstone hydrocarbon reservoirs, which differ significantly from carbonate reservoirs. In this study, two rock physic models, Kuster-Toksoz and Xu-Payne, considering the inclusion models, have been investigated in two wells in one of the carbonate reservoirs in south of Iran. The application of both models has been evaluated as effective considering the results of shear and compressional wave velocities extracted from the models compared with the same measured logs in the wells. Moreover, the pore percentage and aspect ratio estimations using inversion process have been made as well as sensitivity analysis for models. In general, the Xu-Payne model provides more relevant results, within the framework used in this study. In fact, the Xu-Payne has improved the Kuster-Toksoz model which is basically a high-frequency model for ultrasonic studies. It means that Xu-Payne approach contains a workflow which is designed to improve Kuster-Toksoz model for carbonates, and thus, the model is used in low frequencies domainssimilar to well log data.

Nowadays, Zonation Maps can be used as a main guiding document, control and supervision of construction and building in urban areas. Using these maps prevents the unnecessary interference and inappropriate land uses and improving the quality and efficiency of the urban Areas. Great efforts are done to develop zonation maps such as earthquake hazard zoning, determining the occurrence of earthquake hazard with different probabilities including landslide and liquefaction hazard zoning. Using these maps, depending on the type and objectives based on existing standards and extensive data bases, we can reduce costs and increase the speed of decision-making engineering judgments. Qom as a populated city of the country, due to the extending and increasing of development activities on the one hand and the sources of risk, including Khedher and Qizqaleh faults undoubtedly is subject to geological hazards such as earthquakes.
In seismic microzonation studies, estimating the velocity of seismic waves in soil layers is very important to be evaluated directly and indirectly. Dynamic probing test is one of the indirect methods for evaluating shear wave velocity.
Since the relationship between the shear wave velocity and the experimental results of the dynamic probing test has not been used to compile zoning maps, so far, in this study, the results of the dynamic probing experiments have been used to estimate the wave velocities of soil layers in some areas of the city. Using geotechnical data and 3500 dynamic probing tests from 650 boreholes in 250 different locations in Qom and also using the relationship between calculating shear wave velocity and dynamic probing test results, shear wave velocity profiles at depths up to 30 meters was produced, and development of land zoning map based on the 2800 standard in the range of Qom, is considered. The results of this research indicate that most of the soils of the Qom area up to a depth of 30 meters are silty fine grains. Except for the part of the northern and southern regions of the study area, soil type is type 3 with a mean shear velocity of 375-500 m/s. The proposed zoning map with acceptable accuracy in order to reduce costs and increase the speed of decision making in engineering judgments for site classification, seismic analysis and seismic design of structures of medium importance in alluvial zone The city of Qom is widely used. It should be noted that site specific studies should be carried out separately for sensitive buildings such as hospitals and medical centers, and the cases mentioned in part 5-4-2 of Iran's 2800 Code, fourth edition.

It is necessary to have enough data from a reservoir to predict its performance and develop it as accurate as possible. Nowadays, it is a common practice to combine direct and indirect methods to achieve the optimal process of data gathering while considering time, cost and precision. Empirical relationships and optimization algorithms are the two most used indirect methods and recently, numerous researches have focused on the latter one. One of the newest and most powerful optimization algorithms is artificial bee colony (ABC) algorithm. In this paper, we have explained its application for reservoir characterization by estimating shear wave velocity (Vs) using some series of recorded well logs. We have carried out a study on a sandstone and a carbonate reservoir using the ABC algorithm and Greenberg-Castagna relationships. We have chosen three logs among the available ones and used a polynomial to derive their relationship with Vs. In both cases, the ABC has acted more efficiently, indicating that it can be employed to estimate Vs in reservoirs with the lithology similar to the one in our cases when we have no recorded data. Vs is a useful quantity for interpreting seismic data, and is used for identifying lithology and calculating some important mechanical, petrophysical, geophysical and geomechanical properties of the reservoir rock. Hence, it is intended to be measured/caluculated by either direct (e.g. DSI tool) or indirect (e.g. experimental Greenberg-Castagna relationship and artificial intelligence) methods. One of the most novel and robust artificial intelligence algorithms is the ABC algorithm. It is a swarm-based metaheuristic global optimization algorithm based on the behavior of bee colonies when they are looking for food. In addition, one of the most used experimental relationships for predicting Vs is Greenberg- Castagna relationship. The accuracy of these two methods, i.e. the ABC algorithm and the Greenberg-Castagna relationship, to predict Vs in sandstone and carbonate case studies is compared in this paper. The ABC algorithm is implemented in 4 phases of initialization, employed bees, onlooker bees and scout bees to find the optimal point in a constraint search. In this study, a first-order multivariate polynomial relates Vs to neutron porosity, bulk density, and P-wave velocity logs, and the objective function is mean absolute error or MAE (based on the values measured by DSI tool) because the data contains numerous spikes. The algorithm is coded and run in MATLAB®. The derived polynomial is then used to estimate another set of data to evaluate its ability to be generalized.
A modified form of Greenberg-Castagna relationship is also used to estimate Vs in brine saturated multi-mineral rocks.
The values of Vp used in this relationship must be corrected based on fluid saturations by Gassmann’s equation. Finally, a comparison between the results of these two methods is made both graphically and quantitatively. After implementing the written code to our specified problem, we found out that the MAE of the result vector in training phase was 0.023 for the sandstone case and was 0.077 for the carbonate one, therefor, relative errors were in range of [1.3%, 1.7%] and [2.9%, 4%], respectively. In the evaluation phase, the MAEs were 0.028 and 0.070, corresponding to relative errors ranging [1.9%, 2.1%] and [2.5%, 3.6%] for sandstone and carbonate case studies, respectively. On the other hand, the MAEs for predictions obtained from the experimental Greenberg-Castagna relationship were 0.043 and 0.091 for sandstone case and carbonate one, respectively. From these results, it can be concluded that the ABC algorithm is capable to be used for the purpose of our study here. Hence, these obtained relationships for predicting Vs can be used in other reservoirs with the same lithology in the lack of measured data.

In this study, Simulated Annealing algorithm is applied on Rayleigh wave group velocities to image the density variations and shear and compressional wave velocities structure of the crust and upper-mantle of Makran subduction zone. Based on previous studies, surface wave dispersion measurements are primarily sensitive to seismic shear wave velocities. However, it has been proved that the sensitivity to compressional wave velocity is significantly smaller than the sensitivity to shear wave velocity. Also the sensitivity function for the density is smaller than the one for the shear wave velocity. Therefore, shear wave velocity variations are mainly the model parameters in surface wave dispersion analysis. Simulated Annealing is a probabilistic technique for finding the global optimum of a given function. It is especially useful to approximate global optimization in a large search space. The Simulated Annealing method like the Monte-Carlo method, samples the whole model space and can avoid getting stuck in local minima.To evaluate calculation efficiency and effectiveness of Simulated Annealing algorithm, two noise-free and two noisy (10% of white Gaussian noise) synthetic data sets are firstly inverted. Then, a real data from Makran region is inverted to examine the applicability and robustness of the proposed approach on real surface wave data.
In next step, gravity data inversion was applied with a priori information based on surface wave analysis results to obtain Moho depth variations and crustal density structure. The reason for using gravity data set is that surface waves group velocity is sensitive to average velocity variations and has a good lateral sensitivity, whereas gravity anomaly is sensitive to depth variations of discontinuities and has a good vertical resolution.
Our results show that the Moho depth across the Makran subduction zone increases from the Oman seafloor and Makran forearc setting to the volcanic arc. Generally, the crust in the western Makran is thicker than the eastern part and the maximum crustal thickness in the Makran region reaches 46 to 48 km below the Taftan-Bazman volcanos. The Moho map clearly depicts the western edge of the Makran subduction zone, where the Minab fault (representing the eastern edge of the Hormuz Straits) marks the boundary between the thick continental crust of the Arabian plate and the thin oceanic crust of the Oman Sea. Our results show clearly that the high-velocity slab of the Arabian plate subducts northwards beneath the low-velocity overriding lithosphere of Lut block in the western Makran and Helmand block in the eastern Makran.

Today, due to the accumulation of resources in urban areas, urbanization has been increased and consequently, city limits have been increased. Thus, considering the population distribution in these areas as well as the existence of a large number of these areas in sedimentary region, the significance of the study of seismology and earthquake engineering for earthquake retrofitting and reduction of the risk of earthquakes has been increased. A phenomenon that enhances damage during the earthquake, even at great distances is the site effect. Mexico City earthquake, occurred in the epicentral distance of 300 km and caused damage, is a clear example of the enhanced damage caused by site effect. Thus, this study emphasizes on the site effect and underground structures affected by shear wave velocity in earthquakes.

In many rock engineering projects, accurate identification of rock strength properties is very important. Uniaxial compressive strength is one of the most important features to describe the resistive behavior of rocks which is used as an important parameter in the design of structures especially underground openings. Determination of this parameter using direct methods, including uniaxial compressive strength tests is costly and time-consuming, and also sometimes preparation of standard samples in many rocks is difficult. In such cases, the implementation of some simple and non-destructive tests and using empirical relations can increase the evaluation speed and reduce costs. These relations even regional or local (For example within a geological formation or a single lithology) can help in the estimation of these parameters in order to be used in geotechnical projects. In this study, samples of existing limestones in south west of Tehran (Capital of Iran) were prepared and uniaxial compressive strength, point load, Schmidt hammer and Shear wave velocity tests on which have been performed. Then by the statistical evaluations of the results, the empirical relations between uniaxial compressive strength and the results of other tests are obtained. The comparison between the predicted and observed values of uniaxial compressive strength represents the validity of obtained empirical relations. The application of the proposed relations for limestones in the study area and those with similar geological conditions will provide acceptable results.

Based on the extensive studies, undoubtedly, the shear wave velocity (Vs) plays a fundamental role in hydrocarbon reservoir evaluation. Using Vs often allows us to identify the seismic signatures of lithology, pore fluid type and pore pressure, efficiently. Unfortunately Vs data is not available in all reservoirs and it is necessary to predict it. To date, intelligent systems have been utilized as powerful and routine tools for this purpose. In this study, PSO algorithm that is one of the artificial intelligence system used for Vs estimation. The algorithm is utilized in linear and nonlinear ways by intelligently derived Equations. This study have a total 3190 data points from Asmari reservoir from two wells in one of the oilfields, SW Iran. All data points have porosity log and measurement Vs by Dipole Shear Sonic Imager. These data are divided into two parts, one part included 2090 data points are used for constructing model and the other part included 1100 data points are used for testing and validation model. Shear wave velocity is predicted by PSO algorithm and results compared with real Vs measurements by DSI. Regression between predicted Vs by Linear PSO algorithm and measured Vs is about 0.92, whereas it is about 0.95 by using nonlinear PSO algorithm. Results of this study and comparison with other studies confirm that particle swarm optimization is suitable for predicting Vs in other oilfields.

The southeast region of Iran includes the western part of Makran as an active subduction zone on the North side of the Oman Sea. The velocity structure of this region is not well understood because of its low-level seismicity and a small number of permanent seismic stations in this region. The main purpose of this study is the estimation of shear wave velocity structure and Moho depth variations in the southeast of Iran. To this end, we apply the joint inversion process of Rayleigh and Love waves dispersion curves and the “P-wave receiver function” (PRF) around four permanent broadband seismic stations of the region named ZHSF, KRBR, CHBR, and BNDS. To find the group velocities of surface waves in the southeast of Iran, the “frequency–time analysis” (FTAN) is applied to the waveforms of 40 local earthquakes, which occurred in Rigan region and recorded by the four permanent broadband seismic stations. These local earthquakes include two main shocks of 20 December 2010 (ML=6.4) and 27 January 2011 (ML=6.2) accompanied by their foreshocks and aftershocks in Rigan region. Therefore, the group velocities of fundamental modes of Rayleigh and Love waves are calculated in the period range from 5 to 60 sec in the paths among the four seismic stations and the sources of Rigan earthquakes. Also, to calculate P-wave receiver functions around the four permanent broadband seismic stations, 485 teleseismic earthquakes with suitable signal-to-noise ratios and epicentral distances between 30° and 95° related to the region, are selected. The radial PRFs are computed by deconvolving the vertical component from the radial component based on the iterative deconvolution method (Ligorria and Ammon, 1999). After preparing these two groups of data, we can determine the shear wave velocity structure and Moho depth vicinity of each seismic station by applying the joint inversion process to the dispersion data and the PRF data related to each seismic station (using the joint96 program; Herrmann and Ammon, 2007). Based on the results obtained in this study, the average group velocity of surface waves was estimated at less than 3.5 kms-1 in the period range from 5 to 60 sec. The lowest average group velocity of surface waves was obtained in the paths between the CHBR station and the sources of the Rigan earthquakes. Also, the Moho depths beneath the ZHSF, KRBR, CHBR, and BNDS stations were estimated 38±4, 46±6, 26±2 and 56±5 km, respectively. The minimum thickness of crust beneath CHBR station as well as the higher velocity of shear wave estimated beneath this station, are consistent with the shallow subduction of a high-velocity oceanic crust of Arabian plate beneath the south side of Makran. Furthermore, the thicker crust beneath the KRBR station and the lower velocity of shear wave estimated in this area, when compared with the area encompassing the CHBR station, is due to the existence of magmatic assemblage in the vicinity of the KRBR station. These results are consistent with the crust thickening from the south to the north of Makran. The maximum thickness beneath the BNDS is due to the location of this station being in the southeast of Zagros mountain belt, where the thick continental Arabian plate collides with Zagros. This collision leads to thickening of crust in Zagros.

Regarding to the importance of the industrial region in engineering project execution, assessment of Geotechnical characteristics is essential to prevent undesired problems. Because of the uprising number of oil and gas industry structures, the Mahshahar Special Industrial Region is very important. So, the Geotechnical investigation in this region is necessary. Executing these investigations guaranties the implementation of any construction projects and prevents any unwanted detrimental events.
The study area is located in Mahshahar Port near in southwest of Iran. This port is one of the important industrial ports of Iran. Mahshahr port is located in area between 49º04´-49º06´E and 30º25´-30º31´N.
Based on Sedimentary-structural division of Iran, this area is located in Zagros zone. This zone is limited to Sanandaj-Sirjan zone from the northeast, to the Makran Zone from the southeast, to the highlands of northeastern Iraq and southeast Turkey from the northwest and to Arabian Plate from the south and southwest. Based on geological data, the properties of Zagros zone varies in different areas and this zone is divided to some subzones that Abadan Subzone, in which the study area has located. This subzone is located in the southwestern part of Zagros.
To investigate the Geotechnical characteristics of Quaternary sediments in the study area, eight boreholes data, performed by continuous coring method, with depth 11 to 25m were studied. The seismic downhole test and Standard Penetration Test (SPT) were done in all boreholes. The laboratory tests are included the index tests on soil samples (particle size distribution, Hydrometer, Atterberg test), consolidation, direct shear, uniaxial and triaxial tests. Strata were classified using index tests.
To reveal the stratification of site, the Seismic Downhole test was performed in all boreholes, in which the source was in surface (blow on a plate placed in 2-3m distance from borehole) and the P-wave was measured in geophones installed in different depth of boreholes (1m intervals), so whole depths of borehole were investigated. To measure the S-waves, the blows were stroked in a horizontal direction on timber to generate S-waves. Finally, the relationship between elasticity modulus in downhole and SPT tests and also the relationship between friction angles in triaxial tests with NSPT values were assessed.
The plasticity of soil has investigated using Atterberg test results, performed applying standard ASTM D4318 on all samples. The results show that the Liquid Limit and plasticity index vary between 25-40% and 10-20%, respectively, showing the low to medium plastic characteristic of soil.
The elastic modulus and also the relationship between the elastic modulus on downhole and NSPT tests were investigated in the study area. Based on the results, the elastic modulus of quaternary sediments of Mahshahar varies between 100-350 Mpa. In general the relative density and the seismic wave velocity of soil strata were increased with depth in this area.
Also, the fitting curve of NSPT values and elastic modulus in downhole tests show the R2=0.80, in which the NSPT values were corrected to N60.
Soil classification of the site, regarding the average S-wave velocity of soil up to 30m depth, was performed based on the Iranian Building Code (IBC) 2800. The recorded mean S-wave velocity in all boreholes is less than 375m/Sec so the ground type III (based on IBC 2800) is recommended to be taken into account in the design.
Estimating of friction angle of soils, some researchers like Hunt (1984) have been published some reference tables. In this study, to have a simple equation to estimate the friction angle of quaternary sediments, the results of NSPT and consolidated undrained triaxial test (CU) have been used. The result shows the R2=0.85 for fitting curve. Regarding the results, it can be concluded that because of overestimating of friction angles, the presented tables by Hunt (1984) is not applicable to Mahshahr quaternary sediments. It seems that the main reason comes from remarkable existence Clay soils in the sediments.
In this research, based on data from boreholes, the general characteristics of quaternary sediments were revealed and also the strata were classified based on the Unified System as ML, CL and SM. To determine the elastic modulus of soil, the results of the seismic downhole test were used. Based on these results the elasticity of soil varies from 100 to 350MPa. The relationship between mentioned above elasticity and NSPT was assessed and an equation was determined. In general the relative density and seismic velocity increase with depth in this site. S-wave velocity in Seismic downhole test results were used to determine the site or ground type. This parameter was less than 375 m/Sec in all boreholes. So, the ground type III (IBC2800) is recommended for the whole site. Finally, an equation was concluded for relationship between NSPT and UC triaxial friction angle which can be more useful than other published relationships (like those by Hatanaka & Uchida) and Hunt’s tables, because these published ones overestimate the friction angle; the main reason of this case is the existence of the great amount of Clay in soil in this site.

Optimization of reservoir parameters is an important issue in petroleum exploration and production. The Ant Colony Optimization (ACO) is a recent approach to solve discrete and continuous optimization problems. In this paper، the Ant Colony Optimization is used as an intelligent tool to estimate reservoir rock properties. The methodology is illustrated by using a case study on shear wave velocity estimation from petrophysical data by the linear and nonlinear ACO models. The results of this research show that the ACO is a fast، robust and cost-effective method for rock properties estimation. It is proposed that ant colony optimization aids in future reservoir characterization studies.

The purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have complicated structure، direct determination of this parameter takes time، spends expenditure and requires accuracy. On the other hand، there are no precise equations for indirect determination of it and most of them are empirical. In this study، by using data sets of several dams of Iran adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methods، model and equation are rendered for prediction of shear wave velocity in limestone. Totally، 170 sets of data were used for modeling. From these data sets، 136 ones were utilized for building the intelligent model، and 34 were used for their performance evaluation. Compressional wave velocity، density and porosity، were considered as input parameters. In the relevant predictions، the values of R2 and RMSE for ANFIS model were consecutively 0. 958 and 113. 620. These values for GEP equation were 0. 928 and 110. 006. Because these results have accuracy، they could be used for prediction of shear wave velocity for limestone in the future.