JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS
Year:2019 | Volume:5 | Issue:1 |Papers Count:12

Summary The residual phase estimation and correction of a post-stack seismic section is important and necessary. The remaining non-stationary phase detection of data without the use of well logs information can be made using statistical methods. Kurtosis maximization approach by constant-phase rotation is the most popular post-stack statistical methods that can reveal the non-stationary phase of seismic data. Kurtosis criterion is a fourth-order statistics that preserves the wavelet phase information and plays an important role in seismic data interpretation. In this paper, we change the problem of regularized kurtosis maximization for non-stationary phase estimation that is presented by van der Baan and Fomel (2009) to reduce significantly the computational volume while maintaining the quality of our results. The proposed approach due to lower computational volume, and also, the fewer number of free parameters is more efficient than similar regularization approaches. Therefore, for large-scale data analysis, it is easier to use. The effectiveness of the proposed technique for identifying and correcting the non-stationary residual phase of the seismic signals are shown on both synthetic and field data...

Summary Specific geological and economic features of the Zagros orogeny region have made this region an important subject to study. Therefore, in this research, we explore the structure of the crust and lithosphere of this region by forward modeling of gravity and geoid data The survey area is located in the Zagros mountain range comprising of all major structural domains from Persian Gulf to central Iran. According to our results, crustal thickness is minimum beneath Persian Gulf increasing toward northeast and reaches to its maximum of 66 km underneath main Zagros thrust. Passing through the Sanandaj-Sirjan zone, crustal thickness decreases again below the central Iran. The depth to the boundary between lithosphere and asthenosphere changes from ~220 km beneath Arabian plate to 100 km beneath central Iran...

Summary Ground penetrating radar (GPR) method is a non-destructive method for detecting shallow subsurface objectives based on the transmission of electromagnetic waves into the earth and the recording of received reflections from the sent waves. Noise recording of GPR data is inevitable. Noise attenuation is one of the most important and most significant steps in GPR processing. GPR data contains a variety of noise types. Random noise during data acquisition affects the quality of the data. Several noise reduction techniques have been proposed in various papers. In this paper, two methods of total variation (TV) and group sparsity total variation (GSTV) are used to reduce noise from GPR data in rational dilation wavelet transform (RADWT) and dual-tree rational dilation wavelet transform (DTRADWT) spaces. The TV method is an inversion method for noise attenuation of data. The developed type of TV is called GSTV. TV and GSTV methods are very effective in noise attenuation. However, due to the presence of random noise in all frequencies of the data, it is difficult to reduce noise in the data...

Summary The East Azarbaijan geothermal area is located in northwest of Iran, which hosts several hot springs. It is situated mostly around the Sabalan and Sahand mountains. The Sabalan and Sahand geothermal area is now under investigation for the geothermal electric power generation. It is characterized by high thermal gradient and high heat flow. In this study, our aim is to determine the fractal parameter and top and bottom depths of the magnetic sources. A modified spectral analysis technique named “ de-fractal spectral depth method” is developed and used to estimate the top and bottom depths of the magnetized layer. A mathematical relationship is used between the observed power spectrum (due to fractal magnetization) and an equivalent random magnetization power spectrum. The de-fractal approach removes the effect of fractal magnetization from the observed power spectrum, and estimates the parameters of the depth to top and depth to bottom of the magnetized layer using iterative forward modelling of the power spectrum. This approach is applied to the aeromagnetic data of the East Azarbaijan Province. The obtained results indicated variable magnetic bottom depths ranging from 9. 8 km to about 16. 8 km. In addition, the fractal parameter was found to vary from 1. 6 to 3 within the study area...

Summary The spatial distribution of petrophysical parameters in reservoirs is one of the most important factors for reservoir characterization. A Flow unit is a continuous part of a specific volume of the reservoir in which its geological and petrophysical properties are alike. Therefore, accurate prediction of the flow units in a reservoir is a great task to reliably achieve petrophysical description of the reservoir. Normally, there is no simple relationship between porosity and permeability in reservoirs, thus, a proper method to evaluate the permeability-porosity relationship in hydrocarbon reservoirs is to determine hydraulic flow units. To present the permeability-porosity relationship in this paper, first, the hydraulic flow units in three wells of the hydrocarbon field under study were determined using the methods of flow zone indicator (FZI) and electrofacies, and then, the permeability-porosity relationship in each of the hydraulic flow units was investigated. The results indicate the same number of hydraulic flow units obtained by both methods. However, higher correlation coefficient is obtained for the permeability-porosity plot using FZI method compared to electrofacies method. Moreover, hydraulic flow units can be determined using electrofacies method from well logs even if porosity and permeability data are not available...

Summary In this study, processing and interpretation methods in remote sensing such as visual and spectral analysis have been performed on the ASTER (advanced spaceborne thermal emission and reflection radiometer) data from Zarshuran gold area, and as a result, the alteration zones in the area have been identified. Then, the results of two-dimensional and three-dimensional modeling of Induced Polarization (IP) and resistivity data, using geological information, alteration and mineralization from the core drilling have been interpreted. Eventually, optimal locations for exploratory drilling have been proposed..

summary Downward continuation of potential field data plays an important role in interpretation of gravity and magnetic data. For its inherent instability, many methods have been presented to downward continue stably and precisely. The Tikhonov regularization approach is one of the most robust. It is based on a lowpass filter derivation in the Fourier spectral domain, by means of a minimization problem solution. In this manuscript, we propose an improved regularization operator for downward continuation of potential field data. First, we simply define a special wavenumber named the cutoff wavenumber to divide the potential field spectrum into the signal part and the noise part based on the radially averaged power spectrum of potential field data. Next, we use the conventional downward continuation operator to downward continue the signal and the Tikhonov regularization operator to suppress the noise. Moreover, the parameters of the improved operator are defined by the cutoff wavenumber which has an obvious physical significance. For computing the α parameter, it is necessary that the C-norm of the potential field must be calculated. The improved operator can not only eliminate the influence of the high-wavenumber noise but also avoid the attenuation of the signal. Experiments through synthetic gravity and real gravity data from Kohe Namak region, Ghom province, Iran show that the downward continuation precision of the proposed operator is higher than the Tikhonov regularization operator...

Summary Edge detection of causative bodies is crucial in interpretation of potential field data. Among the various edge detection methods, eigenvalue methods are majorly used to discriminate the gravity anomalies. The present investigation is, however, about to employ the eigenvalue methods to detect the magnetic anomalies. To this end, the largest eigenvalue method was applied to the total intensity of the magnetic data in Astmal area besides the vertical derivative, tilt angle and analytical signal methods in order to interpret the magnetic anomalies. In aids of the mentioned methods, the geomagnetic target map was prepared and integrated with the geological information. Results indicated that the mineralizing fluids infiltrate the faults and fractures of the area. Also, dykes might play important roles in formation of the Astmal magnetite deposit...

Summary Global and regional geomagnetic field models give the components of the geomagnetic field as functions of place and time. Most of these models utilize polynomials or Fourier series to map the input variables to the geomagnetic field values. The only temporal variation in these models is the long term secular variation. However, there is an increasing need amongst certain users for the models that can provide shorter term temporal variations, such as the geomagnetic daily variation. In this research, we have constructed an empirical model of the quiet daily geomagnetic field variation based on functional fitting...

Summary 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...

Summary In this paper, the task is to return from a set of multiplicities from a model to obtain an approximation of that model using sparse approximation. The term ‘ approximation’ indicate the sufficiency of an interpretation that is close enough to the true mode, i. e. reality. In geosciences, the multiplicities are provided by multiple-point statistical (MPS) methods. Realistic modeling of the earth interior demands for more sophisticated geostatistical methods based on true available images, i. e. the training images. Among the available MPS methods, the DisPat algorithm is a distance-based MPS method, which generates appealing realizations for stationary and non-stationary training images by classifying the patterns based on distance functions using kernel methods. Advances in non-stationary image modeling is an advantage of the DisPat method. Realizations generated by the MPS methods form the training set for the sparse approximation. The sparse approximation is comprising of two steps, sparse coding and dictionary update, which are alternately used to optimize the trained dictionary. Model selection algorithms like LARS are used for sparse coding. LARS optimizes the regression model sequentially by choosing a proper number of variables and adding the best variable to the active set in each iteration. The ILS-DLA dictionary learning algorithm addresses the internal structure of the dictionary by considering the overlapping or non-overlapping blocks and the inversion task according to the internal structure of the trained dictionary. The ILS-DLA is fast in the sense that it inverts smaller blocks constructing the trained dictionary rather than inverting the entire dictionary. The trained dictionary is sequentially updated by alternating between sparse coding and dictionary training steps. According to the experiments, the compressed sparsity-based image model is superior to 90% of the generated realizations by 90% probability...

Summary Two-dimensional (2-D) surface wave tomography is one of the most practical approach to reveal the lateral variation of phase/group velocity. Hence, various methods have been introduced to attain the more accurate images. In this paper, we compare two different tomography methods (Tikhonov regularization method and Yanovskaya-Ditmar method) and try to show their advantages and drawbacks resulted from their basic assumptions. We first investigate the lateral resolution capability of the methods using the synthetic checkerboard test in cases of noise free and noise level of 3%. Synthetic models are considered with different grid spacings of 0. 5˚ ×0. 5˚ , 1˚ ×1˚ and 2˚ ×2˚ . Then, they were applied on real data containing phase velocity obtained from the teleseismic Rayleigh waves recorded at broad band stations located in Iran at the period of 30 s. A comparison of the results obtained from the Tikhonov regularization and Yanoskaya-Ditmar methods shows that the former method has a better lateral resolution than the latter method...