JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS
Year:2020 | Volume:6 | Issue:1 |Papers Count:12

Summary Velocity analysis is one of the most time-consuming and important stages of seismic processing. Semblance, as the most commonly utilized coherency measure for velocity analysis, is defined as a normalized ratio of output-andinput energy. In the presence of amplitude variation with offset (AVO), however, this measure is not effective enough. In order to compensate for this shortcoming, an alternative AB semblance is used. Both semblance and its AB variant need summation of the amplitudes along hyperbolic trajectories for a range of velocity values; which is computationally expensive and limits processing large data sets. In this paper, we use fast hyperbolic Radon transform for fast computation of seismic velocity analysis based on the traditional semblance and its AB variant. We test this method on both synthetic and field data sets with different sizes to show the improvements in term of calculation speed in the proposed method. Introduction Building velocity model is one of the most significant topics in seismic data processing and interpretation. In seismic data processing, executing the stages of normal moveout (NMO) correction, proper stacking, depth and time migration, and so on require an appropriate velocity model. There are several methods for building a velocity model from seismic data. Most of these methods are based on criteria which describe the consistency between the velocity model and the seismic data, but they differ in the way these criteria are defined, calculated and utilized for estimating the velocity model. The most conventional method for velocity analysis is based on moveout of reflection events, which uses the coherency measure for building a velocity model. Semblance is the commonly used coherency measure. Although it is effective in most practical situations, this measure faces problems in the presence of strong variations of amplitude along seismic events or polarity reversals. An algorithm, called AB semblance, has been introduced for solving this problem. This method, like other methods of semblance, also requires amplitudes of events to be summed along hyperbolic trajectories in the time gates, which can be very time-consuming for processing large data sets. In this paper, fast hyperbolic Radon transform in log-polar coordinates is employed to speed-up the calculations of semblance-based velocity analysis. Methodology and Approaches Recently, an algorithm with complexity O(N2 logN), where N denotes the number of data samples, has been introduced for evaluation of the hyperbolic Radon transform. It is based on rewriting the Radon operator in log-polar coordinates, with which the main computational parts reduce to computing convolutions. This allows to use the Fourier domain for fast calculation of it. In order to apply fast Fourier transforms (FFTs), samples in log-polar coordinates must be chosen on an equally spaced grid. Since data is sampled in the time-offset domain, a resampling is required for switching between coordinates. In this paper, we use this algorithm in the computation of AB semblance, for summing along hyperbolic trajectories in each time gate. In this method, the time gate width will be one sample. The final result thus requires convolution with an appropriate Gaussian window. Results and Conclusions Velocity analysis based on direct computations could be time-consuming in the presence of a large data set. In this paper, a fast algorithm with complexity O(N2 logN) is used for velocity analysis based on AB semblance. Field and synthetic data examples have been used in order to examine the proposed method. The results from the tests show large speed-ups of the method compared to other similar velocity analysis methods.

Sharifabad copper deposit is located on the Cenozoic volcanic belt in the northwest of Bardeskan, Khorasan Razavi Province. The host rocks of the mineralization are pyroxene andesite, tuff, agglomerate, limestone, sandstone, and micro conglomerate of the volcanic rocks of Eocene and Oligocene age. Alteration includes propylitic, silica and carbonate materials. Mineralization is restricted to the veins and veinlets and is controlled by the local faults. Mineralization mainly consists of chalcosite, magnetite, hematite, pyrite and malachite that occur in the cavities and fractures of the host rocks. Geophysical surveys using resistivity (Res) and induced polarization (IP) methods have been carried out along 5 survey lines. The results indicate high resistivity and IP values in the depth levels of 0-25 and 32-49 meters, and thus, relevant highly resistant horizons are identified. On the basis of the geological and geophysical studies in the area, eight locations for drilling exploration boreholes are proposed that probably contain highly concentrated copper mineralization. Introduction The Sharifabad copper deposit is situated in the Sabzevar zone and Eastern Iran Cenozoic volcanic rocks. The area is covered by a detailed exploration program, which includes preparation of the geological map of the area at 1: 5, 000 scale, and drilling several hundred meters of trenches. This study focuses on the copper bearing veins system and determination of subsurface mineralization by conducting geological and geophysical operations. For carrying out geophysical operations, 5 survey lines have been designed, and then, surveyed in the area. In this paper, the results of modeling and interpretation of geophysical data acquired along two survey lines DD1 and DD2 have been presented. The geophysical data include IP-Res measurements at 460 points along these two survey lines using dipole-dipole array with the electrode intervals of 20 meters that have been conducted by Omid Geological Engineering and Geo-Science Company. Methodology and Approaches The raw field geophysical data along the two survey lines DD1 and DD2 have been modeled using two-dimensional (2D) smooth Inversion by RES2DINV and ZONDRES2D software packages. The 2D model sections are displayed in threedimensional (3D) form using RockWorks software. The inverse modeling results and geological studies lead to the recognition of the copper mineralization zones and local faults in Sharifabad area. Results and Conclusions The results of geophysics studies in Sharifabad area show high resistivity and IP values in two depth levels 0 to 25 m and 32-49 m. On the basis of the geological and geophysical studies, eight locations for drilling exploration boreholes are proposed, and as a result, three boreholes have been drilled up to a depth ranging 33-40 m. Drilling studies confirm copper mineralization with high copper values in the depth of about 39m. These studies have also led to the identification of four local faults near the mineralization zones.

Abundant sedimentary structures and tectonic conditions in Iran have an important role in the concentration of hydrocarbon resources. There are various direct and indirect methods such as seismic methods that are used to identify these structures. Identifying smaller faults and fractures are possible only by surface geological investigations. Several uplift of mud volcanoes and gas and hydrocarbon have been occurred in the study area in Gorgan plain, Golestan Province. Therefore, faults and complex geological conditions have been created. The detailed study of the area indicates that the places of main reservoirs have certain conditions, and also, increasing the emitted fluid causes the noise in the seismic data to be increased. Seismic techniques can identify faults, but using attributes, we can obtain more information, but in most cases, they are unable to provide a comprehensive model of regional faults lonely. To achieve more comprehensive information from the fractures and faults in the study area, we can use methods such as principal component analysis for the seismic data analysis. Combination of attributes can also enhance the quality of seismic sections enabling us to identify the faults better. Introduction In this study, using principal component analysis on seismic attributes, the main component with a high percentage of variance are prepared and presented in the form of appropriate sections. These sections can demonstrate smaller fractures and faults with better resolution. Although these images and sections diagnose faults well, presentation of these images in RGB color enhances the resolution of fractures. Methodology and Approaches To raise quality levels of seismic sections, the attributes that have the optimal band combination of using statistical indicators are selected and by applying principal components analysis on these attributes, we obtain images that contain more information. Results and Conclusions The resulting images, using the methods proposed in this study, have more details, and are more accurate, than the images obtained from other methods in showing faults and fractures.

Summary In this study, for inversion of surface wave dispersion curves, a new modified flower pollination algorithm (MFPA) is introduced. The goal of the proposed algorithm is to find the unknown parameters of the problem, i. e., the thicknesses and shear wave velocities of the soil layers. The performance of the algorithm has been evaluated by synthetic models and also actual dataset. The results, in both of synthetic models and experimental data, represent the acceptable performance of the proposed algorithm. The MFPA inversion method is a suitable technique for reducing the non-uniqueness of the surface wave inversion task. Introduction The inversion of the surface wave dispersion curves is one of the practical issues in identifying the subsurface layers and shear wave velocities structures. Shear wave velocity is one of the most important parameters in geotechnical studies that is used to evaluate soil properties, including site effects and seismic microzonation. Typically, surface waves are used to estimate the shear wave velocity. Linear inversion methods are not very reliable due to the nonlinear nature of the problem. With the development of computer sciences and the development of intelligent optimization methods, rapid and easy techniques for inversion of surface waves could be used. In this paper, a new modified flower pollination algorithm (MFPA) for inversion of Rayleigh wave dispersion curves is introduced. In the proposed algorithm in comparison to standard flower pollination algorithm (FPA), the exploration ability of the algorithm is improved. Methodology and Approaches In order to process surface waves and find an adequate shear wave velocity structure, a new hybrid metaheuristic algorithm that adds a dynamic factor to mutation operator of the standard FPA, called MFPA, is applied. In this study, the mutation rate increases gradually from 𝑊 𝑚 𝑖 𝑛 (1/number of flowers) to 𝑊 𝑚 𝑎 𝑥 (1/ number of subsurface layers) as the number of iterations is increased. The MFPA approach could accelerate the convergence speed in comparison to the standard FBA. The code of the MFPA inversion method has been written in MATLAB environment. Then, the proposed technique has been tested on a synthetic dataset. To more explore the reliability of the applied method, 10 percent noise has also been added to the synthetic dataset. The results of synthetic dataset show the capability of the MFPA technique in the absence and presence of noise. For further evaluation of the proposed method, the MFPA has been applied on an actual dataset for geotechnical assessment in an area in the city of Tabriz, northwest of Iran. The results of the experimental data indicate a three-layer model that is in a good agreement with the geological evidence of the study area. Results and Conclusions In this study, a new surface wave inversion algorithm, i. e. MFPA is proposed. Then, capability of this technique is tested by synthetic and actual datasets. The results show that the applied method is a fast and powerful technique in the inversion of surface wave dispersion curves. Moreover, the performance of MFPA has been compared with standard FPA. Because of strong exploration ability of MFPA, this algorithm in estimation of the model parameters has higher convergence and accuracy than FPA.

Summary In this paper, we investigated optimum height in upward continuation of potential field data to separate local anomalies from regional ones. Cross correlation of the data at different heights was used to achieve this goal. In order to obtain optimum height of upward continuation, two methods were introduced. In the first one, cross correlation of the known regional anomalies and upward continuation of the total magnetic field at different heights was used. In this method, the height at which maximum of correlation coefficient happens is the optimum height of upward continuation. The second method is based on cross correlation of upward continued of two consecutive heights of total magnetic field. The height at which the maximum deviation of the first point and the second point from the curve of cross correlation versus height occurs is the optimum of height. The second method is preferable as we always have total magnetic field data at different heights. We applied the second method on the magnetic data from Kalshaneh area in Tabas region. In this area, the optimum height for upward continuation is 60 m in which we can separate local anomalies from deep ones. Introduction For processing and interpretation of potential field data, it is necessary to remove shallow anomalies from deep ones. For doing this, different methods were used. One of the methods for separating local and regional anomalies is upward continuation, which decreases the effect of local anomalies. In terms of wavelength, upward continuation separates and supresses short wavelengths while the long wavelengths remain in the data. The optimum height of upward continuation of data is important as if it is not proper, a part of local anomalies effects are still in the rest of data that causes false interpretation. In this paper, we used cross correlation of magnetic data at different levels to find optimum height for upward continuation. The method applied on the real data from Tabas Region. The optimum height in upward continuation for this region is 60 m that enables us to remove the local anomalies. Theoretical basis Cross correlation between regional anomalies and upward continued of total field at a specified level is defined as follows: 0 0 2 2 0 0 0 0 (, ) (, ) (, ) (, ) r u M M r i j u i j i J g g M M M M r i j u i j i J i J g X Y g X Y r g X Y g X Y                    In this relation, M and N are, respectively, numbers of data in x and y directions. If cross correlation of two functions, total field and regional field, are plotted versus height of upward continuation, the height at which cross correlation peaks, is the optimum height of upward continuation. As in real data, we do not know about regional anomalies, thus, cross correlation of potential data at consecutive heights is used for optimum height determination. For the latter method, the height at which maximum deviation of the line between first and end points from the curve of cross correlation versus height is the optimum height of upward continuation. We applied the methods on artificial and real data. For the artificial data, we used gravity data while for the real data, magnetic data were applied. The considered artificial models comprised of local anomalies at the depth of 400 m for and regional ones at the depth of 1500 m. The optimum height of upward continuation for the models was 500 m using both methods. As a result of applying the second method on the real data from Tabas region, the optimum depth of 60 m was obtained in which shallow anomalies were suppressed well. Conclusion To obtain the optimum height of upward continuation, we used cross correlation of potential field data. In this manner, we used two different methods for cross correlation of potential field data. In the first method, cross correlation of regional field and total field at various heights were used while in the second one we used cross correlation of upward continued field at consecutive heights. The optimum height in both methods was the same. We applied the second method on the real magnetic data from Tabas region in which the optimum height of upward continuation was obtained 60 m in which seemed to suppress the local anomalies greatly.

Summary Geothermal energy is a clean, renewable and economic energy resource, therefore its exploration is very important. In Iran there are many hot springs and there are so much evidences in many areas that show geothermal resources. So far, many exploration operations have been carried out to discover this resources. Considering the evidences of geothermal resources in Zanjan Province, it is tried in this study to recognize and prioritize the areas having geothermal resource potential using several information layers such as geophysical layers obtained from airborne magnetic data; Curie depth, thermal gradient and thermal flow maps, which have been weighted using Fuzzy AHP method. Then, these layers were fused by index overlay method and the regions with geothermal potential were obtained. These regions were then prioritized and defined as the best regions that have geothermal potential by using Fuzzy AHP/Dempster-Shafer (DS) method. As a result, regions 11, 16 and 13, with scores values of 0. 702, 0. 621 and 0. 592 were recognized as the best regions for having geothermal potential. Final results have shown that overlay and fusion data by means of DS method is acceptable and reliable, once the weights of the layers are obtained using Fuzzy AHP scheme. Using existing evidences such as hot springs and other information related to the geothermal resources, we validated the obtained results and concluded that the results were sufficiently accurate. Introduction In spite of the occurrence many evidences that indicate several rich regions of the geothermal resource in Iran, very few exploration studies have been carried out so far specially in Zanjan province that have a lot of hot springs and other relevant evidences. Geothermal heat flux is one of the main parameters to be investigated in geothermal exploration programs but few direct heat flux measurements are available in Iran. We can get reliable and better result by using all exploration criteria related to the geothermal resources such as lithological, remote sensing, structural and geophysical information layers, if we use a suitable method to overlaying and fusion these layers. In this study we firstly use index overlay and fuzzy methods for overlaying these data and the DS-fuzzy AHP technique is then used for data fusion and prioritizing of the results. Methodology and Approaches In the first step, relevant digital information layers were prepared. They comprised of lithology layer, alteration layer obtained from ASTER data, structural layer acquired from lithology, ASTER and airborne magnetic data. Then, Curie depth, thermal gradient and thermal flow maps were obtained using a set of high resolution airborne magnetic data. All of these layers were then integrated by fuzzy logic and index overlay methods to determine the promise regions of geothermal resources. Finally, the favorable geothermal areas, obtained in the previous stage, were prioritized using DS-FAHP fusion method. Results and Conclusions By examining and evaluating the results and considering the existing evidences such as hot water springs, it is found that the data fusion DS-fuzzy AHP methods can provide an acceptable and reliable results. Moreover, the prioritization of the results using these data fusion methods indicates that high potential geothermal regions are related to all the evidences of geothermal resources. The obtained results also show that by applying fuzzy AHP for weighting exploration layers, one can consider uncertainty in decision making, and then, by using DS method for data fusion more reliable results can be acquired.

Summary The source parameters of Bam (2003) earthquake such as the unjustifiability of the released seismic moment in this fault with magnitude relation, the mismatch for post-seismic fault and InSAR faults, not convincing tectonic regime with reported faults and seeing two extreme peaks on accelerometers need to be investigated. As there are ambiguities and sometimes inconsistencies in the reports about these source parameters, we have decided to estimate the source parameters of the causative faults of Bam earthquake using joint inversion of InSAR and strong ground motion data. Static displacement obtained from strong ground motion data has a complementary role for areas covered by vegetation. Our results show two faults that are responsible for breakdowns in Bam city. One of these faults has transferred stress to another one that reaches to surface. The second fault is a right-lateral strike-slip fault with strike 358° , dip 84° and rake 178° to the east that has maximum moment release with 2. 49 m slip. The first fault is laid beneath the second fault with strike 172° and dip 45° to west. The stress transfer pattern of the first fault on the second one is comparable with slip distribution on the second fault. Using forward modeling of strong ground motion data and fixing parameters obtained from joint inversion of InSAR and strong ground motion data, the epicenter of the earthquake is 58. 27° and 29. 05° . Thus, in this study, the exact fault parameters for the Bam earthquake have been determined using joint inversion of InSAR and strong ground motion data, and it is tried to show that the tectonic of this region is related to this fault. Introduction The study of recognition of causative faults of earthquakes and prediction of the physics of these earthquakes is a favorite topic of earth science researchers. The parameters of an earthquake causative fault could demonstrate attributes of tectonic plates and provide critical information about the earthquake hazard assessment. Methodology and Approaches In the first step, we need to determine the fault displacement obtained from InSAR and strong ground motion data. This displacement could be measured using the observations of radar satellites via the synthetic aaperture radar interferometry method. This method can be used to map the deformations created on the ground by earthquakes (Okada, 1985). Using SARS cape software, the displacement is extracted from interferograms that are obtained from InSAR data. To obtain the displacement from strong ground motion data, the BASCO code is used to extract static displacement from acceleration data. To find the parameters of causative fault as analytical models, the joint inversion processing is aimed such that to minimize the difference between the observed and predicted data in a least-squares sense. The non-linear inversion is based on the Levenberg-Marquardt (Marquardt, 1963) minimization algorithm. It has multiple restarts to reasonably guarantee the convergence of the cost Function to the global minimum. Finally, the synthetic accelerations are created with finite fault model to demonstrate the best fit between the observed and synthetic accelerograms at stations. Results and Conclusions The results of this study show that two faults are responsible for breakdowns in Bam city. One of these faults has transferred stress to another one that reaches to surface. The second fault is a right-lateral strike-slip fault with strike 358° , dip 84° and rake 178° to the east that has maximum moment release with 2. 49 m slip. The first fault is laid beneath the second fault with strike 172° and dip 45° to west. The stress transfer pattern of the first fault on the second one is comparable with slip distribution on the second fault. Using forward modeling of strong ground motion data and fixing parameters obtained from joint inversion of InSAR and strong ground motion data, the estimated epicenter of the earthquake is determined to be 58. 27° and 29. 05° .

Summary The Gorshov and Coworkers research on finding beryllium by the rapid release of radionuclide neutrons in 1930 is the first study on soil using gamma-ray activation. In this study, using MCNP code, simulations were performed on soil and sedimentary rocks, and the flux of gamma rays resulting from the destruction of the positron ejected from the material was calculated, and then, using this quantity, some information about the material has been achieved. Using these results in these simulations, an optimal location for the source and detector was obtained to identify oil reservoirs, in which oil, gas and water reservoirs can be identified and separated from each other. In this optimum case, the contrast values for the water, oil and gas reservoirs varied from zero to 53. 60, 63. 40 and 73. 89%, respectively, which were due to changes in the percentage of oil, water and gas in sedimentary rocks at the energy 511 keV. The advantage of using this method is that due to the minimal amount of energy required for carbon interactions of 18. 7 MeV, there is no need to investigate existing sources in nature, since the maximum energy emitted by these sources is less than 18. 7 MeV. Therefore, the sources in nature do not cause any disruption in these measurements. Introduction In the field of nuclear physics, the use of gamma rays is an important and practical method. This method is used in various industries such as oil and gas production, mining and quarrying, environmental monitoring and so on. The technology of using gamma rays is based on the interaction of the beams with materials. The three main phenomena are the interaction of gamma rays with photoelectric material, Compton scattering, and pair-production phenomena. Due to its very high sensitivity, activation analysis has become an important and applicable tool in various fields ranging from science and engineering to industry, mineral exploration, medicine, etc. One of the biggest advantages of analyzing by this activation method is that it can detect most isotopes with very high sensitivity. In this research, different simulations have been carried out on sedimentary rocks in order to find a suitable method for exploration of oil reservoirs. These simulations are performed in three different states and by comparing the results obtained in each case, the best results are selected to explore the oil reservoirs. Methodology and Approaches The Monte Carlo method is a class of computational algorithms that rely on random iterative sampling to calculate their results. To use this method, a structured input file must be provided, including problem information such as geometry, material type, source, output type, and so on. The code, based on this method, solves the problem using input file information and the crosssection library and generates the results in an output file. In the simulations by this code, a cubic block of about 2 meters is considered to be a type of sedimentary rocks, oil, gas, water and a combination of these rocks with water, gas or oil. The simulated cavity has a depth of 180 cm and a radius of 7. 62 cm. A 2-inch NaI (Tl) detector is inserted into the specified hole. Results and Conclusions According to the obtained results, it is possible to detect and identify the oil reservoirs and to separate the oil reservoirs from the gas and water reservoirs. The advantage of using this method is that due to the minimal amount of energy required for carbon interactions of 18. 7 MeV, there is no need to investigate existing sources in nature, since the maximum energy emitted by these sources is less than 18. 7 MeV. Thus, the sources in nature do not cause any disruption in the measurements.

Summary Multiple reflections are coherent seismic noises whose presence, especially in marine data, lower data quality. In this research "dual-tree rational dilatation wavelet transform" Or DT-RADWT is used to attenuate multiple reflection noise from seismic data. The advantage of this transform to the dyadic discrete wavelet transform, is its fractional sampling, which allows for higher timefrequency resolution. The proposed algorithm in this research is wavelet domain noise analysis or WDNA, in which DT-RADWT and split Bergman iteration algorithm are used. WDNA is a data-based algorithm. The split Bergman iterative algorithm is designed to quickly obtain the optimal solution. Radon transform is a common method to attenuate multiple reflections, and it is used to obtain the initial pattern of multiple reflections. The purpose of WDNA is to improve Radon transform output and to better maintain primary reflections. The presence of high levels of random noise reduces the quality process of noise reduction, but WDNA is designed to overcome the adverse effect of random noise. The WDNA results in multiple reflection attenuation have been tested by synthetic and marine data, and their results have been compared with Radon and WDGA outputs. The results show good improvement in seismic data quality using WDNA algorithm in comparison with Radon transform. Introduction The reflection waves, which is reflected between the subsurface or free surface reflectors more than once before being received on the receivers, are called multiple reflections. Multiple reflections, often destructively interact with the primary reflections and reduce the quality of the seismic image. An inverse filter based on predictive deconvolution using the periodic feature is used to attenuate multiple reflections in the water. Multiple and primary reflections show different moveout and travel-times, This property is the basis of the theory of many multiple attenuation techniques such as CMP stacking, F-K filter, and Radon transform. Radon transform was first introduced by Johann Radon (1917) and for the first time, parabolic Radon conversion was used as a multiple attenuation technique by Hampson (1986). Since then, the Radon transform became one of the most widely used tools to suppress multiple noises. Goudarzi and Riahi (2013) presented WDGA method based on the data type, as an efficient way of attenuating various seismic noises. However, this approach, if there is a high level of random noise in the data, cannot well separate the coherent noise from the reflections. Here we try to introduce a new method to solve this problem. Methodology and Approaches The proposed method in this research is called wavelet domain noise analysis (WDNA) algorithm. Similar to WDGA, this method is based on data, but because of the use of the split Bergman iteration is less sensitive to random noise. It also reduces the time to reach an optimal solution and it has better convergence. These features enable better detection of the desired noise and better signal separation from the noise. The goal of this research is to apply the benefits of Radon transform, and at the same time, to use the DT-RADWT wavelet transform capabilities to provide high resolution. We take advantage of the split Bergman iterative algorithm to build a full multiple reflection model from initial multiple models (achieved from Radon filter). Finally, in the DT-RADWT domine, full model of multiple reflections would be subtracted from the input data, and thus, the filtered data would be obtained. Results and Conclusions In this research, the WDNA algorithm has been introduced and its application in attenuating multiple reflections from seismic data has been investigated. The WDNA algorithm is based on the data and requires an initial noise model that is obtained from Radon transform (or any other suitable filter) to attenuate multiple reflections and in the dual-tree wavelet transform domain, it is used to produce a complete noise model with the Bergman iteration algorithm. Subtracting the full noise model from seismic input data yields almost no multiple reflection noise and the initial reflections are well maintained. The use of the DT-RADWT wavelet transform increases the frequency resolution and split Bergman algorithm helps to achieve a fast convergent solution that also causes insensitivity with random noise in the attenuation process of multiple reflections. The results of applying the WDNA method on synthetic and real data have resulted in better outputs than Radon and WDGA.

This work used the prestack seismic data of Penobscot reservoir in Canada to identify gas-bearing layers using amplitude versus offset (AVO) processing. Prestack seismic data was used in ten different angles from 3 to 30 degrees to estimate the reflection coefficients of compressional velocity, shear velocity, density and acoustic impedance. Least square and Tikhonov regularization solutions were applied to invert the Aki & Richards equation. Model covariance matrix showed that Tikhonov regularization had less variance of estimation in comparison to least square solution. Therefore, Tikhonov method was used to calculate the seismic attributes in the studied cross-line. The results showed that reflection coefficient of shear velocity and density could accurately differentiate gas-bearing layers. Introduction The information driven from wells are capable of determining the reservoir characteristics more preciously and distinctly than seismic data, but such data show much lower resolution horizontally. Therefore, seismic data have gained attention due to their better lateral resolution and their ability to provide the structural information for geologists. The objective of seismic AVO (amplitude variation with offset) inversion is to achieve the physical and elastic properties of subsurface layers using the prestack gathers. Its theory is based on Zoeppritz equations, which provides exact descriptions of the AVO phenomena at elastic interfaces. However, it is too complicated to obtain stable seismic inversion results. Thus, the approximation proposed by Aki and Richards is widely used in inversions. Methodology and Approaches Aki and Richards approximation has three unknown seismic attributes (the reflection coefficients of compressional velocity, shear velocity and density), which should be derived from seismic data. This study applies the algebra technique to obtain the unknown parameters. A typical feature of inverse problems is that they are ill-posed, and a unique solution may not exist and small errors in the data may cause large variations in the estimation of inverse modeling parameters. One of the best known and most used regularization methods is Tikhonov regularization. This research work estimates the seismic attributes in the Penobscot region using Tikhonov regularization. Results and Conclusions Well L30 was drilled in Cross-line 1153 and gas-bearing layers were found in time range of 2000-2036 ms. In general, gas layers could decrease P-wave velocity and slightly increase S-wave velocity. Therefore, the variation of the P-wave and S-wave reflection with offset can be used as a direct hydrocarbon indicator. The reflection coefficient of shear velocity and density could detect the gas layers with high accuracy.

Summary The main goal of this study is to identify different phases of existing fluids in a carbonate reservoir using seismic reservoir characterization techniques. Seismic reservoir characterization helps to find more information about reservoir rock and fluid properties in hydrocarbon exploration. The most common methods of seismic reservoir characterization are post stack, prestack and AVO (amplitude versus offset) analysis techniques. Pre-stack seismic inversion techniques produce more reliable results than post-stack inversion approaches. Pre-stack inversion methods utilize CMP gathers for inversion of seismic data. These methods invert several terms including Vp, Vs and density simultaneously. In this study, simultaneous inversion and AVO inversion were applied on pre-stack seismic data to characterize fluid distribution in the reservoir. From the AVO inversion, the intercept and gradient values were obtained, and from simultaneous inversion, Lame parameters were extracted throughout the carbonate reservoir. The obtained results were then analyzed to identify lateral fluid variations in the reservoir. After analysis of the obtained results from AVO and simultaneous inversions, it was concluded that AVO inversion was not as applicable as LMR inversion in the carbonate reservoirs. Introduction Identification of rock and fluid properties of hydrocarbon reservoirs is one of the most important parts of reservoir characterization projects. Seismic inversion algorithms play crucial roles in quantitative reservoir interpretation jobs. Seismic inversion increases the resolution of seismic data by integration of low frequency and high frequency data from seismic and well data. The obtained models provide a higher quality data for interpretation and identification of lithology and fluid in the reservoir. AVO analysis is another aspect of quantitative seismic interpretation, which delivers an effective tool as a direct hydrocarbon indicator for detection of fluid content and analysis of reservoir quality. AVO analysis is very effective in clastic reservoirs. Simultaneous inversion is a generalized version of post-stack inversion to invert pre-stack CDP gathers (angle gathers) to calculate P-impedance, S-impedance, and density (Hampson, 2005). It is more stable than a post-stack inversion because pre-stack inversion in brine-saturated rocks incorporates P-wave velocity, S-wave velocity, and density that should be related linearly. Several authors (e. g. Gardner, 1974, and Castagna, 1985) have given this relation. Any deviation from the background trend can be interpreted as fluid-saturated rock. Simultaneous inversion algorithm solves the Fatti equation, which is the simplified version of the Zoeppritz equation. LMR inversion was proposed by Goodway (1997) as a lithology and fluid indicator using Lame parameters λ (incompressibility), μ (rigidity) and density (ρ ). μ ρ (MR) gives information regarding rock matrix, and λ ρ (LR) reflects the fluid information of the rock. Methodology and Approaches In this study, AVO analysis and LMR inversion methods were utilized to delineate the boundary of fluid content variations in the reservoir. The applied methodology is summarized as follows: 1. Data loading including well and seismic data 2. Quality control of loaded data and enhancing them by applying relevant corrections 3. Converting pre-stack seismic data into the angle domain by an appropriate velocity model 4. Wavelet extraction and well to seismic tie 5. AVO analysis 6. Initial mode building for inversion 7. Inversion analysis at well locations 8. Performing pre-stack simultaneous inversion on seismic data to obtain Vp, Vs and density for calculation of Lame parameters 9. Preparing LMR attributes 10. Interpretation of LMR attributes for identification of lateral fluid variations in the reservoir Results and Conclusions The results obtained from the analysis of different methods of quantitative interpretation of seismic data showed that in the studied carbonate reservoir, the AVO attributes are not capable of discriminating rock and reservoir fluid properties. However, this does not mean that seismic data are inadequate in identification of lateral variations of the reservoir properties, and utilization of more advanced and more suitable techniques in carbonate reservoirs solves this problem. Therefore, in this study, simultaneous inversion and LMR attributes were analyzed to delineate oil and gas distribution in the reservoir.

Summary In this paper, we suggest a simple procedure for quality factor tomography inspired from local earthquake travel time tomography. Amplitude residuals (as input data) are calculated from difference between logarithm of observed amplitude and logarithm of predictive amplitude taken from ground motion equations. The averaged residuals on a station-by-station basis illustrate significant systematic variations in NW (northwest) Iran. Here, we assume that the variations are generated by lateral change of anelasticity (i. e. model parameter), created by change of rock properties. After formulating the relationship between data and model parameters, we carry out a two-dimensional (2-D) tomography for the NW of Iran as a case study using 2901 rays recorded by 35 seismic stations. The obtained tomogram shows anomalies that have been recognized before by travel time tomographies. Thus, we propose that the tomography of amplitude residuals is a proper method for resolving structural heterogeneities responsible for large amplitude variations across seismic regions. This may help to have more realistic seismic hazard assessments. Introduction Tabriz city with 1. 6 m population is located in the NW Iran. Estimation of empirical attenuation relations for the region is a key for realistic seismic hazard assessments. Recently, Motaghi et al. (2016) have employed microseismicity of the region to estimate an attenuation relationship for the NW Iran. Their regression has left significant amplitude residuals at different stations where neighboring stations have similar average residuals confirming important lateral structural variations ignored in the attenuation estimations. For instance, stations located around the NTF have systematic negative residuals consistent with location of a thick sedimentary basin in the region. Such systematic residual patterns have been reported before in Canada and North America by Atkinson (2004) or in Alborz Mountains, north Iran, by Motaghi and Ghods (2012). These observations have inspired us to conduct 2-D amplitude residual tomography similar to widely used local travel time tomography (e. g., Rawlinson and Sambridge, 2003). We assume that attenuation variations are only affected by anelastic coefficient variations generated by change of rock properties. Thus, we have discretized the study region and considered anelastic attenuation coefficient (inverse of quality factor) as an unknown parameter for the inversion procedure. Methodology and Approaches We formulized a linear relationship between the amplitude residual (as datum) and anelastic coefficient variation (as unknown parameter), and then, we analyzed seismograms recorded from 943 local earthquakes with magnitudes between 1. 6 and 5. 2, and azimuthal gap less than 250o. The data were gathered by 35 seismic stations located in the NW Iran to carry out the inversion. We used a weighted damped least squares approach (e. g. Menke, 1989) in which weight matrices for the data and model parameters were used. The weights for the data come from the signal to noise ratio calculated for each signal. The weights for the model parameters come from the number of rays per block. This helps to exclude model parameters associated with blocks not crossed by rays. An optimal damping value of 23 was selected from the trade-off curve between the total residuals and the weighted model variances. The inversion procedure estimated variations of intrinsic attenuation coefficient relative to the average value (0. 0012). We converted the obtained values into change of quality factor, Δ Q, which was more usual in the literature. Results and Conclusions Our tomogram for the NW Iran showed a lower quality anomaly for Neogene sedimentary basin around the North Tabriz Fault in contact with high quality Cretaceous volcanic and Cambrian metamorphic basement outcrops in the north of the region. Such clear consistency between our tomogram and lithology variations at the surface disappeared in the eastern part of the tomogram where deep (depth = 20– 45 km) events occurring in the oceanic-like crust of the South Caspian Basin are observed. Comparing our eastern anomalies with a teleseismic tomography across the same region, we found that low and high quality patches in eastern part of our tomogram are probably generated by thermal effects of Sabalan volcano and oceanic like crust of the South Caspian Basin, respectively. Good consistency between our results and previously reported features confirms that the amplitude residuals are proper data set to detect structural heterogeneities responsible for large amplitude variations in active seismic regions.