The initial model, Jacobian matrix, Frechet derivatives, and digital look-up tables are essential components in most time-domain electromagnetic (TEM) Data modeling methods in layered earth. Computations of these components in the modeling process are time-consuming as their determinations need to use iterative operations. Zohdy introduced an alternative method for the rapid inversion of direct current (DC) resistivity Data obtained using the Wenner and Schlumberger electrode configurations. The methodology allows for the inclusion of a flexible number of layers, which are chosen in conjunction with the initial model based on the Data-derived knowledge, and avoids large and iterative calculations of Jacobian matrix and Frechet derivatives. This paper presents an endeavor to enhance the outcomes of TEM Data modeling or inversion utilizing the Zohdy's technique through the elimination of the look-up tables. In this study, various synthetic models of stratified geological formations are examined using the the above-mentioned methodology. The modeling findings demonstrate robustness even when 5% and 10% noise levels are entered into the Dataset. Hence, this approach can be considered as a worthy method for TEM Data inversion accompanying some levels of noise in the Data.

Seismic waves are nonstationary signals because their frequency contents are changed when they propagate through layers of different elastic properties. Hydrocarbon reservoirs and fault zone are two major sources of attenuating high frequency components of a seismic wave. Dropping of higher frequency components of a seismic wave while passing through a hydrocarbon reservoir appears as low frequency shadow zone on a seismic section.In this study, we used instantaneous spectral attributes of real seismic Data in time-frequency domain in order to trace the existence of hydrocarbon reservoir and fault zone in the study area. For this purpose, we used short time Fourier transform and continuous wavelet transform. In order to reduce the run time, the instantaneous frequency was calculated directly from scalograms.

Due to the remarkable advantages of wavelet transformation, this technique is now very common in gravity analysis. In this research the Green’s function occurring in the Poisson potential field theory is used to construct non-orthogonal, non-compact, continuous wavelets. This kind of wavelet is directly corresponded to upward continuation procedure. Simple geometrical forward models such as Sphere, Vertical and Horizontal Cylinder, Thin sheet and Vertical sheet are applied as forward models. First, analytical wavelet transform of the models is calculated, and then the amplitude and the location of the maximum of the product is applied as a new mathematical model (forward model). The new models have a mathematical relation with the source parameters such as depth and shape of anomaly. However, because of being normal the forward models do not have any relation with the physical parameter of density contrast. In order to examine the accuracy, precision, behavior and application of the offered method, the synthetic Data for both noisy and noise-free Data, has been applied. Subsequently, considering the applicability and expansion of the method for applied goals, some suitable real Datasets have been used. For the purpose of gathering Data and testing the algorithm, two sources of Data were accessible: Institute of Geophysics University of Tehran and the National Iranian Oil Company. Formal permission was granted by both institutions. The outcome of this process was compared with the result of other established classical methods. The parameter of depth estimated by both methods is very close (about 400m difference for the depth of about 3.5km). After careful assessment, it became evident that results obtained from these comparisons are beneficial and useful. Real Data are separated into regional and local signals using discrete wavelet analysis. The maximum points of wavelet transforms (worn diagrams) are also applied to interpret the depth of the anomaly compared to adjacent anomalies.The result obtained by inverting the Data using the parameter of amplitude has less standard deviation compared to the location of the MSE, and is believed to be more accurate. It is observed that adding noise causes higher standard deviation; however after adding 20 percent noise in synthetic Data, less than 6% error occurred in the parameter of depth (still yields good results) which shows remarkable stability against noise.

The neoclassical growth model is tested by use of panel Data procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel Data method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

One of the main problems in geophysics Data analysis is the presence of noise. This problem in seismic survey is more obvious than in the other branches. In this study the random noise suppression is presented by a filter which is called eigenimage filter and operates on stacked 3D seismic Data in frequency domain. Our tools in suppression of random noises were SVD and Lanczos. The Lanczos method works much faster than SVD, specially when we have a sparse matrix. The special feature of the Lanczos method is its high performance. The F-xy filter has its own abilities such as fantastic signal preservation and well noise suppression, and can be used even before stacking. In this study presented performance of the filter on staked synthetic seismic Data.

Generally, the measured secondary field Data is inverted into resistivity using two principal models; the homogeneous half-space model and the layered half-space model. While the homogeneous half-space inversion uses single frequency Data, the inversion is done individually for each of the frequencies used, the multi-layer 1D inversion is able to take the Data of all frequencies available into account. The resulting parameter of the half-space inversion is the apparent resistivity which is the inverse of the apparent conductivity. It's possible that using the fast method to calculate the apparent resistivity, if the distance between the HEM sensor and the top of the half-space is known. Unfortunately, the dependency of the secondary field on the half-space resistivity is highly non-linear. Thus, the inversion is not straightforward and the apparent resistivities have to be derived by the use of look-up tables, curve fitting or iterative inversion procedures (Fraser, 1978; Siemon, 1997; Siemon, 2001).The usual technique for inversion of airborne electromagnetic Data frequency domain (HEM) Data is a 1D single site inversion, because of the 2D and 3D inversion of HEM Data wants very powerful computer hardware. Some inversion method for electromagnetic Data inversion suggested. Usually this method updated for ground electromagnetic methods. One of the methods employed in the inversion of airborne electromagnetic Data frequency domain (HEM), Levenberg-Marquardt method inversion (MLI) is looking for smoothing fitted to the Data in the inversion algorithm; this inversion method based on least squares criteria, seeking a modelby minimizing the residuals of an objective function. Marquardt’s inversion only pursuits the largest fitting of simulation Data to original measurements, and has the characteristics of simple algorithm and fast calculation. In this procedure usually HEM Data smoothed and then used in the inversion procedure, but any variation in Data change results. For stability of inversion procedure, it is suggested that stitched-together 1-D models along the profile that each sounding inverted by constrained neighbor sounding and each layer of each sounding inverted by depth constrained neighbor layers. In addition used smoothing constrained in inversion procedure instead of smoothing a Data like Marquardt–Levenberg inversion.In this paper, Starting model determined for apparent resistivity with Mundry technique and for centroied depth with Weidelt technique. To using this method, the auto inversion cod written in MATLAB software environment that inputs are real and imaginary part of Data with sensor altitude and output is inverted model with misfit. In the following this algorithm tested on standard synthetic Data, the model chosen for the generation of synthetic Data represents a layered earth structure having an inhomogeneous top layer in order to study the influence of shallow resistivity variations on the appearance of deep horizontal conductors in one-dimensional inversion results. The inversion of synthetic Data results shown this technique for inversion HEM Data improved the results and is much more accurate than Marquardt–Levenberg inversion. Finally the inversion algorithm used to invert a set of real DIGHEM field Data from Mirgah Naqshineh area in Saqqez of Kurdistan and interpretation of results according to geology information of area.

Space-time adaptive processing is a useful technique for clutter mitigation in airborne radars. space-time adaptive processing algorithms usually require estimation of interference covariance matrix with limited training Data and high complexity in processing. To overcome these problems, in this paper, deterministic clutter subspace and the direct Data domain method are introduced. Two methods are presented. In the first proposed method, filter coefficients are calculated by estimating airborne radar parameters and also calculation of interference covariance matrix. The sensitivity of estimated parameters is also computed. By extending clutter subspace, the suggested method could get more robust against parameter estimation. In the other proposed method, the direct Data domain is employed for calculating the coefficients vector by reducing the size of filters. Finally, the performance of the suggested algorithms is improved by utilizing other methods such as using more taps and deterministic clutter subspace.

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.