IRANIAN JOURNAL OF GEOPHYSICS
Year:2012 | Volume:6 | Issue:1|Papers Count:12

Geological events and the curiosity of human mind to comprehend these phenomena compel the researchers to investigate their structures and tectonic evolution. Some key parameters to better understand these subjects are Moho Depth (the boundary between crust and mantle) and also the Lithosphere-Asthenosphere Boundary (LAB).There are methods available which can give us some knowledge about these key parameters such as seismic, magnetotelluric, volcanologic and etc. Each one has advantages and disadvantages. In the seismological method, a period of about six months is needed to be sure that a reasonable quantity and quality of events has been detected to record enough data during the research. The high expense of instruments and lack of access roads in high topography has limited this method to adequately capture the data researchers seek. These problems also exist in the seismic base method. The seismic method is generally expensive. Moreover, this method is nearly blind in lithospheric depth such as LAB.We tried to introduce another method that used potential field data. Our data were topography and geoid undulation mainly observed by satellites. The method for this study utilizes some basic concepts such as local isostasy as wel as some basic physical and mathematical rules, relations and equations. Our topography data were from the newly released topography database for all over the world, ETOPO1. The spatial resolution of the data were 1 Arc-minute. The geoid undulation was calculated by a spherical harmonic up to order 2159 and degree 2190 from Earth Gravitational Model’s 2008 (EGM2008). To avoid the effects of anomalies deeper than LAB, wavelengths greater than 4000 km were removed.There were some advantages to this method such as: the higher speed of the calculation so that the examination of a big region was possibile at a fraction of the cost for other methods. Modeling was done on a very substantial area in the Northern part of the Iranian plateau that included the Northern part of Central Iran, the Alborz Mountains and the South Caspian Basin.The results showed the evidence of thickening of the crust up to~55 km underneath the Alborz Mountains. However, many previous researchers concluded no roots there. The other outcome of utilizing this method was thinning the crust and lithosphere underneath the Northern part of Central Iran (36-38 km for the crust and 140 km for the LAB) relative to the surrounding area. Our data reflected a solid correlation with some previous work and geological evidence. Subduction of the south Caspian basin (probable oceanic crust) underneath the Eurasia plate is completely visible; however, this activity was not recognized in Kopet-Dagh.

Being familiar with the modes of motion and coordinate changes of the Earth surface points as a function of time is very important and essential in different types of geodetic applications. The puepose of this research is the time-dependent modeling of displacement and coordinate changes of the Earth’s crust surface points due to the plate tectonic motions and earthquakes in the region of Iranian plateau. The provided model could be used to predict the coordinate changes of surface crust points or to predict the geodetic observations (distance and angle) from one arbitrary epoch to another. This model receives the coordinates in various ITRFs or WGS84 reference frames and after the computations are made, the results could be provided in any reference frame.The Bursa-Wolf seven-parameter conformal model was used to transform three dimensional Cartesian co-ordinates between WGS84 and ITRF2000. In the absence of a crustal motion, the equations for transforming positional coordinates from one ITRF to another are rather familiar to the surveying community, i.e. it is a seven-parameter transformation. In the presence of a crustal motion, the transformation equations can be generalized to allow one frame to move relative to the other. Thus, each of the seven defining parameters becomes a function of time. Therefore, in modeling, fourteen transformation parameters were used for ITRF2000 reference frame transformation to the previous and later reference frames.Okada (1985) analytical model was used to model sudden coseismic and interseismic motions due to earthquakes. In previous works (Pearson, 2010 and Meade, 2005) the block model was used for secular and interseismic deformation modeling, but in this research, we used Okada (1985) analytical modeling for this purpose since (1) Modeling the present-day velocity field determined with GPS networks incorporates geological constraints on the geometry of the main structures and on the long-term deformations; (2) Regions between the major faults are not rigid and so the modeling allows for internal deformations. Finally, we have a tectonic model for Arabia-Eurasia oblique collision zone in Iran that is more realistic than the rigid block model. This model shows that about 30% of GPS velocity field components are produced by faults inside Iran, 60% by Arabian plate and 10% by Anatolian plate.Continuous use of GPS data and local network observations is recommended to get a more precise model for secular and interseimic motions. Also using more precise geometric faulting parameters due to earthquakes obtained by inverse problem solution based on GPS or InSAR observations is recommended to get more precise outputs. Postseimic motions were not modeled in this research since this effect is a function of time and its amplitude is just considerable for large earthquakes, beside that the amount of this effect is reduced with time. Anyway, the postseismic deformation modeling due to intense earthquakes with a large focal depth using Wang (2006) model is recommended. In this research, just the effects of secular, interseismic and coseismic motions were included in the model. To complete the model, it is recommended to consider the effects of the crustal motions associated with land subsidence, volcanic activity, postglacial rebound etc.

The study of the physical properties (geophysical methods) of rocks associated with its mechanical properties has recently received lots of attention. Recent studies show that geophysical methods especially the seismic and geoelectric methods are able to estimate the mechanical parameters and recognize their spatial variations, including anisotropy. Meanwhile, electrical and seismic methods are the most used one.Electrical measurement is one of the non-invasive geophysical methods commonly used by engineers working in various fields such as mining, geotechnical, civil, underground engineering as well as oil and gas mineral explorations. This method can be applied both in laboratory and in the field. Numerous scientists have focused on the relation between resistivity and porosity. However, there is a very limited study on the relation between the electrical resistivity and the rock properties apart from porosity.In this paper, changes in the electrical conductivity of rocks during a uniaxial compression test were investigated in laboratory. The uniaxial compressive strength, elastic modulus, and density values of the samples were determined in laboratory. We installed special electrodes on seven nearly saturated core samples in order to measure the resistivity. Core samples had a 52-mm diameter and a 110-mm length. Two-electrode as well as four-electrode arrays were both used in resistivity monitoring in laboratory. Using a four-electrode array minimized the undesirable electrode polarization effects. In the four-electrode array, we used two non-polarizing Ag/AgCl electrodes mounted on the core sample. Our laboratory observations showed that there was not any electrode polarization effect. When we used a two-electrode array, the resistivity changes were less than 5 percent compared to a four-electrode array. In our laboratory investigation, we used different sedimentary core samples including sandstone, fossilioferous limestone and travertine. Maximum resistivity observed for the travertine core sample was less than 12 kohm. During the uniaxial compressive test, deformation measurements were made and the stress–strain curves were plotted. Tangent Young’s modulus values were obtained from stress–strain curves at a stress level equal to 50% of the ultimate uniaxial compressive strength.Sandstone core samples showed a resistivity increase in the whole strain range. On the contrary, the fossiliferous limestone samples (thin section showed that the sample was composed of tiny calcium fossils in a fine aggregate of micrite cementation) showed a resistivity decrease in the whole strain range. Travertine and limestone showed an intermediate behavior (resistivity increased in the lower strain and it decreased in the higher range). In other words, the onset of new crack formation occurs well inside the quasi-linear part of the stress-strain curve. The quasi-linear portion of the stress-strain curve was the result of a competition between closure of one population of cracks, and the growth of new propagation of the existing cracks.Resistivity behavior during a uniaxial compression load is closely related to the pores in the lower strain ranges and then to the new induced fractures in higher strains. Our results showed that the electrical resistivity may be a representative measure of the rock properties. Additionally, the effect of certain minerals on the rock’s resistivity must be taken into account. The results indicated that the rock structure had an important effect on the resistivity behavior during a mechanical loading.

Tropospheric ozone is one of the main causes of respiratory problems and it hurts vegetations. In this research, statistical models based on a wide variety of regressionmodels are presented in order to evaluate the surface ozone concentrations in hourly and daily scales in Isfahan using meteorological variables and pollutant gases as predictors. Although none of meteorological variables and pollutant gas levels has the ability to interpret the measured ozone variations in Isfahan, the results have shown there is a significant correlation between them and the ozone variations. Calculating a nonlinear bivariate model can show the general ozone fluctuations, but because of irregular fluctuations in hourly data, it can not be a proper predictor. Most of the models assigned the biggest influence to the air temperature and humidity in surface ozone production and declared that the mean surface pressure do not have an important role in the point analysis. Also increasing the oxide compositions of nitrogen increases the ozone production. In a daily scale, carbon monoxide and temperature have presented the best interpretation for the ozone concentration.The aim of this research was to present a consistent evaluation of the surface ozone using statistical methods. At beginning, the society of ozone samples, pollutant gases and corresponding meteorological data was assessed and the correlation between the ozone level and each of them or a group of them was tested, step by step. Most of data did not obey a normal curve, so in different stages, some operations were necessary to make the data closer to the normal situation. In this paper, the data from meteorological and pollution observations were used as predictors. The station was located in 32.62N, 51.66E with the elevation of 1550 m.a-The data consisted of: a- The data from the pollution stations: surface ozone, CO, SO2, NO, NOx, NO2 b-Meteorological data: air temperature, relative humidity, wind speed, solar radiation, air pressure.Reviewing the time series of the ozone data (24 hours) showed that there was a daily sinusoidal cycle in the ozone concentration and a sinusoidal model can easily calculate the ozone amount as a function of the hours in a day. Although a sinusoidal curve was well fitted to the daily curve of the ozone concentration, random fluctuations in the daily average were seen. These irregularities caused difficulties in presenting a single proper model to show the daily cycle of the ozone concentrations.In the next stage, an equation was gained by modulation of the daily and hourly equations to show the ensemble daily and hourly cycle of the ozone concentrations.Analysis of the results of regression models shows that between the three equation, best equation be gained from step wise method. Then, by using a backward method, 13 equation be gained. All of these equations show that the daily scale can not justify the surface ozone variations. This can be because of the act of other unknown variables or because of the nonlinear nature of the correlations between ozone levels and the predictors. However the data were preprocessed to get closer to a normal distribution. For this purpose, both logarithmic and squared forms of the data were also used eventhough they could not make a considerable change in order to transform the data to normal distributions. All of them were used beside the natural data to form more regression models.It should be noted that the nature of these kinds of data, that needs complicated process to be created, makes the correlations coefficient less strong. The resulted equations in this paper showed that the current operations could not normalize the distributions of the data. The existence of a nonlinear correlation between the ozone levels and the studied variables can be a reason for the weakness of these models. In the previous studies, the highest determination coefficient was 0.36 (Alexandrof, 2005). In this paper, the best equation nearly showed the same amounts (r=0.304). In the backwards method, a higher coefficient was gained (r=0.592) but because of the lengthand size of the equation, it is not usable. Although the regression models and the principal component analysis showed that they had a strong ability to interpretat the surface ozone fluctuations and predict its concentration, the number of their independent variables prevented them from being useful enough from an application viewpoint.

Because of the non-stationary property of seismic signals, time-frequency transforms have widely used in seismic data processing and interpretations. Spectral decomposition can reveal the characteristics that are not easily observed in the time representation or the frequency representation alone. Conventional spectral decompositions such as short-time Fourier transform (STFT) and Wigner–Ville distribution (WVD) have some restrictions, such as Heisenberg uncertainty principle and cross terms. In this paper, we used the deconvolution of a short-time Fourier transform spectrogram to overcome the mentioned restrictions.The resolution of a time–frequency representation using STFT is strongly dependent on the length of the window function. A short window length will result in a good time resolution but a poor frequency resolution; a long window length will result in a poor time resolution but a good frequency resolution.No window function is used to calculate the WVD of signals. Therefore, WVD has a high resolution in time and frequency simultaneously. However, the existence of the cross–terms has limited the application of this distribution (Boashash, 2003). Auger et al. (1996) introduced the smoothed pseudo WVD (SPWVD) to eliminate these cross-terms. Smoothing the WVD leads to a tradeoff between the time–frequency resolution and cross-term elimination.When the smoothing function is the WVD of the window function used in STFT, the SPWVD will become the STFT spectrogram (Qiang and Wen-kai, 2010). There are no cross-terms in the STFT spectrogram, and yet it has a low resolution both in time and frequency. Therefore, a 2D deconvolution operator can be used to generate a high timefrequency representation of the signal with no cross-terms. To perform the 2D deconvolution, we used the iterative Lucy–Richardson algorithm. The resulted spectrogram after 2D deconvolution is nominated as deconvolutive STFT or DSTFT.The efficiency of this method is evaluated by applying on both synthetic and real seismic data. The results of synthetic example show that the deconvolutive short-time Fourier transform spectrogram (DSTFT) has the fine resolution as the Wigner–Ville distribution (WVD) has but with no cross-terms.Castagna et al. (2003) used the spectral decomposition to detect the low-frequency shadows associated with hydrocarbons. In fact, these shadows are often related to an additional energy occurring at low frequencies, rather than the preferential attenuation of higher frequencies. One possible explanation is that these are locally converted shear waves that have traveled mostly as P-waves and thus arrive slightly after the true primary event.We used a deconvolutive short-time Fourier transform spectrogram to illuminate the low-frequency shadow corresponding to a gas reservoir at one of the gas fields in the South West of Iran. The results of the real data example show that the DSTFT analysis has a much better resolution than the conventional spectral decomposition and can potentially be used to detect hydrocarbons from a gas reservoir directly using low-frequency shadows.

Aeromagnetic surveys play an important role in the exploration of natural resources of economic interest, as well as in regional geologic mapping. Magnetic anomalies caused by the lateral variations of magnetization in the earth’s crust often are characterized by smooth regional gradients with isolated features. The main goal of magnetic prospecting is to infer both the geometry and the magnetization of the geologic structure that causes the observed magnetic anomalies. However, akin to other potential-field methods, interpretation of magnetic field anomalies is non-unique because more than one distribution of magnetization (i.e., magnetic dipole moment per unit volume) and source geometry can explain the same observed magnetic anomaly. One important goal in the interpretation of magnetic data is to determine the geometry and the location of the magnetic source. This has recently become particularly important because of the large volumes of magnetic data that are being collected for environmental and geological applications. To this end, a variety of semiautomatic methods based on the use of derivatives of the magnetic field have been developed to determine magnetic source parameters such as locations of boundaries and depths. As faster computers and commercial software have become widely available, these techniques are being used more extensively. Utilizing first-order derivatives of the magnetic field, Euler deconvolution was first applied on profile data and subsequently on gridded data. The method has come into wide use as an aid for interpreting magnetic data. The main advantage of the Euler method is that it can provide automatic estimates of the source location of the causative magnetic anomalies. However, it requires an assumption about the geometry of the body that is the actual source. In practice, assumption is achieved by specifying a structural index h to define the source geometry in generalized situations, setting a good strategy for discriminating, and selecting meaningful solutions. Recent extensions to the Euler method allow h to be estimated from the data, with the calculation of Hilbert transforms of the derivatives. The SPI method, which requires second-order derivatives of the field, uses a term known as the local wavenumber to provide a rapid estimate of the depth of buried magnetic bodies. The local wavenumber was defined as the spatial derivative of the local phase. The SPI method worked on gridded data, but assumed a contact model (h=0). Later extensions to the method enabled calculation of h, but these required third-order derivatives. The calculation of third-order derivatives from gridded data is problematic, so the use of profile data was advocated by Smith et al. (2005).In a more recent paper, a linearized least-squares method was applied to obtain information about the depth and nature of the buried sources from first-and second-order derivatives of the field (the analytic signal and its horizontal gradient). However, their approach requires knowledge of the horizontal position of the source, inferred from the peak of the analytic signal. Inappropriate sampling of the data and/or noise can make the selection of the horizontal position inaccurate. As a result, these inaccuracies lead to errors in the estimatation of both the depth and the nature of the sources.To overcome the limitations of the previous studies and to improve the process of estimating the source parameters using the analytic signal approach, an automatic method is presented to estimate horizontal location, depth, and the nature of 2D magnetic sources using derivatives of the analytic signal. Derivatives of the field of up to only the second order are used. First, a generalized equation is derived and solved using the least-squares method to provide source location parameters without any a priori information about the nature of the source. Then, using the estimated source location parameters, the nature of the source is obtained. To implement the method, the anomalies are first identified using the analytic signal peak. The method is then applied to a data window around the peak, where the signal-to-noise ratios of both the analytic signal derivative and the horizontal gradient of the analytic signal are relatively high. The determination of the number of data selected is based on the quality of the data and interference from nearby sources. The optimum number of selected data is small enough to see only a single anomaly and large enough to contain sufficient variations in the anomaly within the window. In this study, data for which the analytic signal values are greater than 10% of the peak value were used within each window.The presented method was applied successfully to synthetic magnetic data from 2D models with random noise as well as on a 3D synthetic Bishop model. In synthetic examples, we tested the feasibility of the proposed method; using theoretical anomalies of 2D magnetic models buried at different depths. These models were a horizontal cylinder with an infinite horizontal extent and a thin dike with infinite depth extent. The total-field anomaly values were calculated along a 100 km profile striking south–north at intervals of 1 km.Good results were obtained on a real magnetic dataset related to an ore field in Jalal-Abad, Iran, which has a broad correlation with drilling. In this regard, the results obtained by the proposed method were selected as start point in 2D modeling, and this shows a good fit with the measured profile.

Spectral decomposition has provided a means for observing those features in seismic data that are not always clear in the time domain. There are several approaches that can be used to produce a spectral decomposition of a seismic trace. A case history of using the spectral decomposition and coherency to interpret incised valleys was shown by Peyton et al. (1998). Partyka et al. (1999) used a windowed spectral analysis to produce discrete-frequency energy cubes for applications in reservoir characterization. Continuous wavelet transform (CWT) was introduced by Morlet et al. (1982). In CWT, time frequency atoms are chosen in such a way that its time support changes for different frequencies according to Heisenberg’s uncertainty principle (Mallat, 1999; Daubechies, 1992). In early years, transforming the seismic traces into time and frequency domain was done via a windowed Fourier transform, called the Short Time Fourier Transform (STFT). In STFT, the time-frequency resolution is fixed over the entire time-frequency space by reselecting a window length. Therefore, the resolution in the seismic data analysis becomes dependent on a user-specified window length. This problem can be resolved by an S-transform. The window used in this method at any given moment is adapted to the frequency analysis. In this method, time and frequency resolution will change the window of the time–frequency to obtain a multi-resolution analysis. The CWT commonly used in the data compression decomposes the signal from the time domain to a time-scale domain using the orthogonal wavelets that vary in length and frequency by a factor of two. In contrast, the S-transform decomposes the signal from the time domain to a time-frequency domain using the non-orthogonal variable size Morlet wavelet (Mallat, 1999; Stockwell, 1996; Sinha, et al., 2005). While being computationally more intensive than the orthogonal wavelet transform, the nonorthogonal S-transform provides an added time and frequency resolution valuable for interpretation.Stockwell et al. (1996) interpreted the S-transform as a combination of CWT and STFT. The difference between the S-transform and STFT is that the Gaussian window is a function of time and frequency for the S-transform, while it is only a function of time for the Short Time Fourier Transform (STFT).Another method of spectral decomposition is Matching Pursuit Decomposition (MPD). MPD involves a cross-correlation of the wavelet dictionary against the seismic trace. The projection of the best correlating wavelet on the seismic trace is then subtracted from that trace. The wavelet dictionary is then cross-correlated against the residual, and again the best correlated wavelet projection is subtracted. The process is repeated iteratively until the energy left in the residual falls below some acceptable threshold (Castagna, 2006).MPD has been used recently in a seismic signal analysis (Castagna et al., 2003; Liu and Marfurt, 2005). Wang et al. (2007) has applied an inverse-Q filter to the seismic signals to show the existence of a low-frequency shadow. One of the applications of the spectral decomposition is to detect the geological structures with less thickness such as buried channels. Filled with porous rocks, and located in a non-porous environment, the channels will be a good place for hydrocarbon reserves. For this reason, from the past, detection of the boundary of these channels and lithologic features inside them has been essential in explorations of these reserves.In this paper, we investigated the application and efficiency of the S-transform and MPD methods in the time-frequency analysis of the seismic sections to delineate and detect a buried channel in Sarvak Formation in one of the oil fields located in the South West of Iran. The results from the MPD and S-transform were compared with an STFT applying to the single frequency seismic sections at frequencies 15 Hz and 25 Hz.

During the last decade, there has been an increasing interest in the use of attributes derived from 3-D seismic data to define reservoir properties, such as the presence and amount of porosity and fluid content. Therefore, it is worthwhile to continue the advances in the study and application of expert systems in the petroleum industry so that it is possible to use the attributes in reservoir characterization more effectively. The establishment of the existence of an intelligent formulation between two sets of data (inputs/outputs) has been the main topic of such studies. One such topic of great interest was the characterization of 3D seismic data with relation to lithology, rock type, fluid content, porosity, shear wave velocity, and other reservoir properties. Petrophysical parameters, such as water saturation and porosity, are very important data for hydrocarbon reservoir characterization. Hitherto, several researchers endeavored to predict them from seismic data using statistical methods and intelligent systems (Russell et al., 2002; Russell et al., 2003; Chopra and Marfurt, 2006). Correct recognition of porosity model and estimation of petrophysical parameters in reservoirs is a key issue in any oil project. The correct estimation of porosity as a petrophysical parameter can inform decisions that have high financial risk, such as drilling. By determining reservoir characterizations and assessing petrophisical parameters with a adequate accuracy during the first steps of studies, researchers would be able to produce optimum exploitation with a minimum number of wells.This paper focuses on the link between seismic attributes and reservoir properties such as lithology, porosity, and pore-fluid saturation. Typically, seismic attributes have been the only information obtainable from seismic data. Using statistical rock-physics, the type of seismic attributes that are direct functions (analytically defined) of the elastic properties can be probabilistically transformed, sample-by-sample and independently one of each other, into reservoir properties. In this paper, we combine the methods of geostatistics and multiattribute prediction for the integration of seismic and well-log data, and illustrate this new procedure with a case study. A number of new ideas are developed for the statistical determination of reservoir parameters using seismic attributes, combining the classical techniques of multivariate statistics and the more recent methods of neural network analysis. We first extract average porosity values at the zone of interest, and then compare these values to average seismic attributes over the same zone. The technique of cross-validation is subequently used to show which attributes are significant. We then apply the results of the training and cross-validation to data slices derived from both the seismic data cube and the inverted cube to produce an initial porosity map. Finally, we improve the fit between the well log values and the porosity map using co-kriging.The main purpose of this paper is to present a quantitative assessment of porosity as a petrophisical parameter in an offshore oil field in Iran using the newly proposed method of reservoirs parameter estimation. This paper shows that by using both seismic data and well logging data it is possible to obtain a more accurate model of porosity in a given reservoir. Specifically, the study determines the relationship between a set of seismic attributes and a reservoir parameter such as porosity at well locations, and then uses this relationship to compute reservoir parameters from sets of seismic attributes throughout a seismic volume. Therefore, a primary plan of porosity is available for the area of study. In the next step, by using geostatistics and, according to the initial plan, as a secondary variable in collocated cokriging, we can approach a more accurate plan to show the distribution of porosity. In effect, the proposed method combines geostatistics with multiattribute transforms. This technique uses multivariate statistics and neural networks to improve the secondary dataset used in the collocated cokriging technique.

The purpose of this paper was to study the dynamical and thermodynamical structures of the Siberian high pressure (SH) and some of the effective parameters in its development. The data used were from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis winter time data for a 60- year period (1948-2008). To identify the most significant feature points using the Siberian High Index (SHI) the 25 strongest cases were selected from the period of the study. The range of the fields investigated included the mean sea level pressure, the lower- and upper-tropospheric geopotential heights, wind, temperature, and the potential vorticity (PV), as well as the pressure field on the tropopause surface (PV=2 PVU) and the wave activity vector.The results showed that in the sea level pressure field, the Siberian high pressure has been strengthening around its climatological position at the developing stage until the peak time. After that the high pressure has started to extend and its central cell has been divided into two distinct cells with one moving southeastward into the Far East and consequently cold surge over there while the ridge of the other cell extends westerly toward Europe and the North East of Iran.The composite maps of the anomalies suggest that the vertical structures of the SH are different in the downstream and upstream portions of the surface high. A noticeable feature was that the downstream portion of the SH exhibited a thermal structure, while its upstream portion showed a dynamical structure. In addition, although the SH was generally recognized as an anticyclonic circulation in the lower troposphere, the vertical structure of the wind anomalies indicated that there were cyclonic and anticyclonic circulations in the upper troposphere, respectively, in the downstream and upstream parts of the central area (40-65oN, 80-120oE) of the SH.At the amplification stage of the SH, the appearance of negative pressure anomalies over the Mediterranean Sea implies that this stage can enhance favorable conditions for cyclogenesis over the Mediterranean Sea. This indicates that the SH could have some impacts on the meteorological fields outside its source area.The other finding was that the SH may have profound effects on the meteorological fields in the middle- and upper-troposphere. Examples include the occurrence of a tropopause folding in the downstream side of the SH and the formation of a blocking ridge, as a part of a quasi-stationary external Rossby wave train, in the upstream side when the surface high is amplified.The calculation of the horizontal component of the wave activity flux for the stationary Rossby wave revealed that the Rossby wave originated from the Euro-Atlantic sector and the blocking ridge was a component of this approaching wave. Also, during the development of the blocking ridge, the wave activity flux diverges from the negative height anomalies located at the upstream of the ridge and converges into the amplifying blocking ridge.By evaluating each term of the horizontal temperature advection based on a composite field for the 850 hPa level, it was found that the advection of the basic state temperature by wind anomalies had an important role in developing a surface cold high throughout the amplification stage of the Siberian high pressure.Finally, through a qualitative analysis, it was seen that the coupling between the negative PV anomalies at the surface due to the low-level cold anomalies and upper-level positive PV anomalies due to the tropopause folding lead to the amplification of the SH as well as the blocking ridge. The main conclusion was that the SH was not simply a local thermal system along with the restricted effects in the low-level troposphere.