Seismic interferometry is the process of cross-correlating seismic traces recorded at different locations at the Earth's surface with the aim of retrieving information about the subsurface.The field of interferometry changes our opinion about parts of seismic data that is usually discarded due to being considered as noise. Some examples of such noises are seismic codas (the multiply scattered parts of seismic waveforms) and background noise (whatever is recorded when no identifiable active source is emitting, and which is superimposed on all recorded data). In this study the efficiency of interferometry is shown trough some examples.

Coherency attribute is one of the proper tools in interpretation of structural discontinuities and stratigraphy features in 3-D seismic data. Coherency measurements in three dimensions discuss trace-to-trace similarity and therefore represent interpretable changes in these cases. The similar traces are mapped with high coherence coefficients while anomalies and discontinuities have low coherence coefficients. Coherency attribute shows evaluation criterion of lateral changes in the seismic response, caused by variation in structure, stratigraphy, lithology, porosity and the presence of hydrocarbon. Output of this attribute is a coherence cube which illustrates structural discontinuities and stratigraphy features with higher resolution. In this paper, the application of two conventional coherency attributes based on eigenstructure and CROSSCORRELATION for detection of faults in 3-D synthetic seismic data and actual seismic data is presented.Considering the experimental results, this method has an appropriate response to low SNR for 3-D synthetic models and 3-D actual data. In addition, the comparison of eigenstructure -based coherency attribute method with CROSSCORRELATION-based coherency attribute method indicates the former has higher resolution for detection faults than the latter.

Computer graphics offer various gadgets to enhance the reconstruction of high-order statistics that are not correctly addressed by the two-point statistics approaches. Almost all the newly developed multiple-point geostatistics (MPS) algorithms, to some extent, adapt these techniques to increase the simulation accuracy and efficiency. In this work, a scrutiny comparison between our recently developed MPS algorithm, the CROSSCORRELATION-wavelet simulation (CCWSIM), and a well-known MPS algorithm, FILTERSIM, is performed. The main motivation to benchmark these two algorithms is that both exploit some digital image processing filters for feature extraction. Indeed, both algorithms compute the similarity (or dissimilarity) between data events in simulation grid and training image in the feature space. In order to compare the accuracy of the algorithms, some statistics such as facies proportion, variogram, and connectivity function are computed. The results obtained reveal an excellent agreement of the CCWSIM realizations with the training image rather than FILTERSIM. Furthermore, on average, the required simulation runtime for CCWSIM is at least 10 times less than that for FILTERSIM.

Food security and access to healthy food and its’ quality is the primary and most basic of human needs. Food Security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needsand food preference for an active and healthy life. The main purpose of the study was survey of the food insecurity situation and access to food in urban households of the Alborz Province. The research method was descriptive and CROSSCORRELATION that was conducted by a survey. The population consists of all urban households in Alborz Province (N=652,466) and 185 households were chosen by the stratified random sampling method. The main instrument was a questionnaire where its’ validity confirmed by panel of experts in agricultural extension and education department at Tarbiat Modares and Tehran universities. Consistency and reliability of Research tools were assiumed by Cronbach’s alpha coefficient (0.74). The food security situation results show that 64.4 percent of households were in food safety category and 17.2 percent was in low levels of food insecurity, 10.3 percent were in medium food insecurity level and 8 percent were severely in food insecurity level. Also the results showed that there is significant differences at the 0.01 level in food access Between urban households in Alborz Province, This mean that, Nazarabad city households were in highly level than the other households.

Introduction: Commitment of staff is intangible asset that can highly provide better services. Since providing healthcare services is only possible through efficient human resources, this study evaluated the association between person-organization fit and organizational commitment of staff in university hospitals of Tehran University of Medical Sciences.Methods: This was a descriptive and cross-sectional study which was conducted in 2011. All the staff employed in university hospitals and medical centers have been chosen. Firoozgar, Baharloo, Shariati and Hazrate Rasoul Hospitals were randomly selected as the study samples. Sample size (n=180) distributed between the hospitals using the class-ratio method. Data were collected using person-organization fit (Scrogines) and organizational commitment (Myer and Allen) questionnaires, and their validity and reliability were evaluated. Data analysis was done through the Spearman-Pearson correlation test, Cronbach's alpha and Intra cross-correlation tests.Results: According to the study, the level of person-organization fit between staff was moderate (4.48). In the dimensions of organizational commitment, normative commitment (4.68) had the highest mean and continuous commitment (4.42) had the lowest mean. In general, organizational commitment among the staff has been in high levels. In addition, there was a statistical significant association between person-fit and affective commitment, continuance commitment, normative commitment and organizational commitment.Conclusion: Considering the important role of healthcare organizations and human resources for providing services and achieving conclusive goals, it is necessary to prove the association between person organization fit and organizational commitment and policymaker must give more attention to the person organization fit in attraction, selection and adjustability of staff.

Summary Various studies have shown that the cross-correlation (Wapenaar, 2004), cross-convolution (Slob and Wapenaar, 2007) and de-convolution (Wapenaar et al., 2008) can provide empirical Green’s functions (EGFs) between receiver pairs. These approaches, which are attributed to seismic interferometry, assume that one of the receiver acts as a source, whereas the other one is instated as a virtual receiver. The resulted EGFs allowed many studies to be applied in different regions even though (including) areas with low seismicity.The main assumption of interferometry approach is based on completely diffuse signals which are generated by a closed surface of sources (Schuster et al.2004). In other word, the distribution of sources and theirs energy in a medium are usually uniform. This condition ensured that inter-station EGF is extracted accurately. In general, the sources (left and right of the receivers) located on or near lines which is passed through both receivers are in the stationary region, and the sources above and below are in non-stationary regions. Also, Snieder (2004) indicated that the Fresnel zone of receivers surrounded all the sources which are located in stationary region. In this study, we referred to these sources as stationary sources. Consequently, all sources outside the Fresnel zone were referred to as non-stationary sources. It is generally accepted that the stationary sources play a major role/contribution to retrieve the inter-station EGF. Stationary sources and their energies are characterized by coherency and small wavenumber. In contrast, non-stationary sources and their energies are characterized by incoherency, larger wavenumber. We used this difference in order to separate stationary and nonstationary sources.In the Earth, distribution of noise sources and theirs energy are strongly non-uniform, which contravenes the theoretical interferometry requirements (Stehly et al.2006). In other words, cross correlations from non-stationary sources in stacking procedure do not cancel completely if the source coverage is incomplete. Consequently, incomplete source coverage leads to retrieve unreliable inter-station EGF.In this study, we used 144 sources on circle environment (r=40 km) surrounded by two receivers which are located/installed in A (-4, 0) and B (4, 0) as shown in Figure 1. Moreover, synthetic time series were generated using Mexican-hat source time function (see left panel of Figure 2). All recorded waveform signals of these sources in station A and B are shown in middle and right panel of Figure 2, respectively. After the preprocessing procedure and CROSSCORRELATION performances, we constructed a cross-correlogram matrix, which is called CC, using a collection of cross-correlation function signals (see left panel of Figure 3). The dimensions of this matrix include time (number of point in signal data set, npts) and source counter/numerator. In brief, inter-station EGF is retrieved using stacking the crosscorrelogram matrix signals along the source counter dimension. We followed the analysis and preprocessing of the cross-correlogram before stacking outlined in Poliannikov and Willis (2011). Thus, we decompose cross-correlogram matrix using singular value decomposition (SVD) technique to separate the stationary and non-stationary energies. This idea illustrates/explains that the cross-correlogram matrix could be calculated by its eigenvalues and eigenvectors. Poliannikov and Willis (2011) indicated that the large eigenvalues (singular values) are associated with events which is located in Fresnel zone. Afterward, we constructed lower-rank approximations of the cross-correlogram matrix using two larger eigenvalues, which is called CC2, and then stack CC2 along the source counter dimension to retrieve interstation EGF (see Figures 4, 5 and 6).