IRANIAN JOURNAL OF GEOPHYSICS
Year:2011 | Volume:5 | Issue:4|Papers Count:12

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Wavelet analysis is a major development in the methods of data analysis in the last twenty years, in both research and applications. With concern over current climate changes and their attribution, the analysis of natural climate variability on relatively long timescales has attracted much attention in recent years. While the short instrumental record provides only a tentative estimate of multi-decadal variability, in a long paleo-climatologic series the multi-decadal oscillation appears as a statistically significant mode of climatic variability with a heterogeneous spatial pattern (Datsenko et al., 2001). A powerful method for analyzing the localized intermittent oscillations is the wavelet transform, which is known as one of the best-suited tools for tracing a given oscillation through a time series (Holschneider, 1995). The application of wavelet analysis in analyzing time-based data, particularly those with non-stationary characteristics, has been found to be very successful.The wavelet transform of time series is a convolution with the local base functions or wavelets, which can be stretched and translated with a flexible resolution in both frequency and time. The wavelet transform decomposes a series into time-frequency space, enabling the identification of both the dominant modes of variability and the manner in which those modes vary with time. One of the wavelets which has both real and imaginary parts is the Morlet wavelet. This wavelet is the most commonly used complex wavelet in climate studies.As with its Fourier counterpart, there is an inverse wavelet transform that allows the original signal to be recovered from its wavelet transform by integrating all scales and locations, a and b. If we limit the integration over a range of a scale rather than all of scale a, we can perform a basic filtering of the original signal.In this study, time-frequency spectral decomposition has been conducted to investigate the precipitation variability in a western region of Iran, including Kermanshah, Sanandaj, Hamadan, and Khoram Abad, and to compare these stations with each other over a 43-year period from 1966 to 2009. The wavelet transform spectra were computed for the monthly total precipitation of each record. Results show that all stations had annual return periods with a confidence interval above 90 percent, and that in some years it became strong and in some years becomes weak, showing as well as the occurrence of wet and drought period in these regions. Moreover, at all stations there are some inter-annual components and a similar 128- to 256-month-long returning period with a high statistical confidence level. Precipitation behavior in various frequency bands showed that the local and large scale behaviors of the stations are very similar to each other, although in some scales the difference is significant. Additionally, the trend of variability in the 32-64 frequency band, unlike other bands, shows increased variability of precipitation.

This paper studies the dynamics of the Mediterranean storm track from the view point of Rossby wave activity and its flux. The evolution of wave activity is related to wave transience and nonconservative effects. In this study, the formulation introduced by Esler and Haynes (1999) has been applyed to compute wave activity. The data used for this study are from December 2004 through February 2005. In order to investigate the flux of the wave activity into and out of the Mediterranean region, first the wave activity and its flux were calculated for each grid point of a grid covering the middle latitudes of the North Atlantic Ocean and the Mediterranean Sea. Next, a two-dimentional rectangular domain at 300 hPa surface as well as a three-dimentional rectangular cubic domain extending from 600 to 200 hPa surfaces was selected on the Mediterranean region, and wave activity and its flux were calculated for their different boundaries. These computations were done at 6-hr intervals for each month (December, January and February) of the winter as well as the periods of the two case studies: case 1 (23/12/2004-01/01/2005) with clear propagation of the wave packets to the western Mediterranean from the North Atlantic storm track, and case 2 (07/01/2005-13/01/2005) with zonal propagation of the wave packets along the central latitude of the North Atlantic storm track. In addition, to investigate more accurately contributions of the different parts of the domains to wave activity, each of the domains was divided into three smaller subdomains located on the western, central and eastern Mediterranean. Given the importance of the northern boundary in the wave activity flux into the Mediterranean region, the flux of this boundary was studied in more detail. The results indicate that1. Entrance of the wave activity was observed in the western and northern boundaries of the two- and three-dimentional domains at all the time scales (monthly, seasonally and during the periods of the case studies), while the eastern and southern boundaries showed the exit of wave activity from the Mediterranean region.2. In all of the cases, except Case Study 1, due to the dominance of the total output flux, the Mediterranean region acted as a source of wave activity. This result might be regarded, at least partly, as the reason for the existence of various cyclogenesis centers in the Mediterranean region.3. In Case Study 1, due to the dominance of the total input flux, the Mediterranean region acted as a sink of wave activity, suggesting high energy transfer from the Atlantic storm track to the Mediterranean region.4. The wave activity fluxes associated with the subdomains over the western, central and eastern Mediterranean show that the input flux from the eastern boundary of the subdomains west of the Mediterranean was greater than those from the eastern boundaries of the other subdomains. This finding might be one of the possible reasons for the existence of the main cyclogenesis centers in the western part of the Mediterranean. In this case, the western subdomain acted as a source of the wave activity, whereas the central and eastern subdomains played the opposite role.5. In December 2004, it seemed the wave activity flux in the western Mediterranean was almost independent of the Atlantic storm track, and most of the influx came from the eastern part of the Mediterranean northern boundary. In January 2005, and to a lesser extent in February, the western Mediterranean was affected by the Atlantic storm track and the major influx belonged to the western part of the Mediterranean region.

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The Gravity Recovery and Climate Experiment (GRACE) twin-satellite gravimetry mission has been monitoring time-varying changes of the Earth’s gravitational field on a near-global scale since 2002. GRACE has been producing monthly time series of Earth gravity models up to a degree and order of 120. Its major scientific objective is to obtain detailed information on global water storage changes via the recovery of gravity changes.Filtering or smoothing of GRACE data is necessary to reduce the contribution of noisy short wavelength components of the geopotential models and, as a consequence, to obtain reliable estimates of time-varying gravity signals. Errors of GRACE data increase rapidly with the spherical harmonic degree and manifest themselves in maps of surface mass variability as long, linear features, generally oriented in north to south stripes.The averaging operators or filters implemented on the GRACE data can be divided into two main categories: deterministic or stochastic. Deterministic filters are based on properly choosing an optimal averaging radius which leads to an optimal tradeoff between noise reduction and spatial resolution. In contrast, stochastic operators, or the so-called optimal filters, rely on the principal that external knowledge of the problem (such as desired signal structure and solution error estimates) can be used to set up the filter.This study uses Gaussian averaging and Wiener optimal filters as examples of deterministic and stochastic operators, respectively. The Gaussian filter weighting coefficients can be computed by Jekeli’s recursion formula. Wiener optimal filtering is designed based on the minimum sum of squares of differences between the desired and corresponding filtered signals. It uses the power spectra information of the desired gravitational signal and the observation noise which is inferred from the averaged GRACE degree power spectrum. It was found that the power of signal decreases withincreasing harmonic degree with approximately 10a|i-20, where a=0.4 and b=1.3 for i<21 and a=0.5 and b=1.5 for i<14 are estimated by a least squares adjustment of GRACE data. The degree power of the noise increases in the logarithmic scale, linearly with the increasing l.We show that the Wiener optimal filter is a low-pass filter; that is, in general, it functions similarly to a Gaussian filter.Moreover, these two filter coefficients have been applied to 55 monthly GRACE gravity models for the estimation of the monthly anomalies of total water storage over Iran. The results were compared with the output of the Global Land Data Assimilation System (GLDAS) hydrological model (snow cover plus soil moisture variations) and groundwater variations from borehole pizometer data for the estimation of monthly total water storage variations over Iran. It is shown that Wiener optimal filtering outcomes are nearly identical to those of Gaussian averaging. However, designing the optimal Wiener filter based on the observation is the main advantage of the Wiener filter over the Gaussian one.

Different factors such as the total ozone, cloudiness and aerosol particles influence the surface UV-B from solar radiation. UV-B is particularly dangerous to life on the surface of the earth. In this paper, the effects of total ozone on UV-B radiation, obtained by the spectrophotometer Brewer (type MK IV) system and measurements of cloud cover over the Esfahan area using meteorological records for July 2003 to June 2004, have been considered. The results show that daily integrated UV-B radiation for this period varied between 0 to 6000 J/m2, its maximum occurred during June and July, and its minimum occurred during December and January. The maximum day-to-day variations of UV-B occurred in May and April. Also, the annual mean of integrated UV-B was approximately 3212 J.m-2.d-1(Joules per square meter per day).The results also show that UV radiation can reach critical levels for Esfahan, sufficient to have negative health consequences on humans, especially in June and July. It may be necessary, therefore, for the national weather bureau to issue warnings in this time of the year.The correlations between UV-B and total ozone and cloudiness also show that substantial cloud cover (generally present between December and April) is more important in harmful levels of UV-B radiation than is total ozone. When cloud cover is insignificant, ozone is more effective in reducing UV-B radiation.The cloud factor (C) is found to be about 0.25 in this area. High cloud cover (e. g. (7-8) Octas) can reduce UV-B by 70%. Maximum cloudiness occurs during January and the secondary peak occurs in April (mid-spring). The minimum of cloudiness occurs in the period including July, August and September. Additionally, the results show that clear sky conditions usually have a cloud factor of less than 0.3, while the high cloud cover condition measures between 0.6 and 1.0. These values are in agreement with the values of other research results.Thick cloud cover ((7-8) Octas) can substantially reduce UV-B radiation. Usually, there are negative correlations between cloud cover or total ozone and UV-B radiation. The best defined correlations during cloudy months and clear sky periods were extracted from the analyses and are as follows:Ln (UV-B)=-1.0294 C+7.8910Ln(UV-B)=-2.4156 Ln (O3)+22.235All correlation coefficients of linear regressions for the cloud factor versus UV-B is within the 99% confidence level, and for the total ozone and UV-B it is within 95% confidence level. These relationships can be used to determine UV-B radiation in this area, for technical use. The relationship between the logarithm of daily integrated UV-B radiation and the logarithm of total daily ozone for clear sky conditions shows that a (the Radiation Amplification Factor, RAF) has a value of about 2.4.

Measurement and estimation of natural variables in space and time domains involve considerable uncertainties. Precipitation is an important hydrologic variable with a significant spatial uncertainty. Different methods have been developed to estimate areal average values of precipitation, such as Thiessen, Inverse Distance Weighting, Adaptive Inverse Distance Weighting, and Kriging. Although these methods are simple, they suffer from lack of incorporating spatial dependencies and uncertainties of the available data.The purpose of the current research is to investigate the uncertainties of spatial precipitation over NamakLake watershed in central Iran. For this purpose, both classical and modern spatial exploration analysis methods including Ordinary Kriging (OK) - Bayesian Maximum Entropy (BME) (for univariate cases) and BME-Cokriging (for multivariate cases) methods have been used.BME is a method of spatial and spatiotemporal geostatistics which can rigorously and efficiently incorporate uncertain features of available data. BME is the method which can provide maps with lower uncertainties and more reliable measurements with the contribution of strong mathematical computations and combination with different knowledge bases and uncertainty sources. Generally, BME processing consists of three stages: (1) The prior stage, which uses general knowledge including statistical moments (mean, variogram, and covariance) and scientific or experimental laws or theories, (2) the meta stage, which takes site-specific knowledge including hard and soft data. Hard data are measured values which are considered accurate with low error and high uncertainty and soft data are data with high error and low uncertainty can have different shapes such as interval and probabilistic, and (3) the posterior stage, which is the integration of the prior and meta stages and aims to maximize posterior probability distribution function (PDF). In this stage, the prior PDF is updated with specific data. The prior and posterior PDFs are related based on Bayes’ theorem. Based on our purpose of estimation, different conditions such as mean and mode (known as BMEmean and BMEmode) of the posterior PDF can be obtained. The BMEmean minimizes the mean square error, and the BMEmode is the most probable realization.Kriging is one of the optimum classical geostatistical methods which can estimate unsampled stations with the contribution of sampled measurements. Kriging is a special case of BME. Under some assumptions (considering mean and covariance as general knowledge and hard/ soft data as site-specific knowledge), kriged and BME predictions become the same. Kriging is used in this study as a base for comparison.One hundred and five rain gauge stations are located in and around the study area, out of which 44 have full records of observations for the period of 1977 through 2008. The records of these stations are considered as the hard dataset. The remaining stations have some missing data and therefore observations in these stations are classified as the soft dataset.The stages of spatial modeling in this paper are as follows: (1) The primary processing of raw data, which includes investigating statistical missing data; the hard and soft data are distinguished in this stage. (2) The determination of variograms; the primary fitting of experimental variograms was done using GS+software based on the maximum correlation coefficient and then these parameters are optimized by the Iterated Non-linear Weighted Least Squares (INWLS) method for univariate cases and Iterated Least Squares (ILS) method for multivariate cases. (3) The application of the optimum theoretical variograms obtained through the Kriging, Cokriging and BME methods, and, finally, (4) the estimation of precipitation.The cross validation technique was used to evaluate the results of these two methods. The results of this study have shown that BME estimates were less biased and more accurate than those of the classical OK.

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The convergence between the Arabian and Eurasian Plates has resulted in the extension of the Alborz mountains belt in the north and the Zagros mountains belt in the west-southwest of Iran, and in the different deformation zones with various distributions of seismicity and local topography which make geological structure interpretations difficult for the Iranian plateau. Detecting Moho depth and crustal thickness could be of great help in understanding the dynamics of the predominate tectonics which is the main objective of this study. The P-receiver function technique was selected for this work because it is a popular method for estimating crustal thickness and detecting Moho depth variations under a seismic station. We computed receiver functions for 9 permanent broadband seismic stations of the International Institute of Earthquake Engineering and Seismology (IIEES), which are installed between the Damavand station and the Shoshtar station in the limited region between 32.10o-35.63oN and 48.801o-51.97oE. All stations were equipped with Güralp CMG3T seismometers. The teleseismic events in epicentral distances between 30o-90owith magnitudes larger than 5.5 (mb) and a clear P onset with high signal-to-noise ratio recorded between 2006 and 2010 were selected. We applied observed backazimuth and incident angles derived from the eigenvalues of the covariance matrix for calculating P -receiver functions. Seismograms were then rotated into the ray coordinate system (L, Q, T) such that the components were oriented in the directions of the P-, SV- and SH-waves, respectively. By deconvolving the P-waveforms on the L-component from the corresponding Q-and T-components, the source and path effects were removed. We obtained approximately 110 P receiver functions for the study region. We increased the signal-to-noise ratio by stacking after the moveout correction for a reference slowness of 6.4 s/deg, which corresponds to an epicentral distance of 67o. PRFs for all stations were calculated and the distribution of the P to S piercing points at 40 Km plotted, which is the expected depth of Moho. To improve the spatial resolution, PRFs of all stations were stacked in bins of 0.04o.Due to the different deformation zones that exist along the profile, our results reveal the significant variations of the Moho depths beneath the Iranian plateau. The depth of the crustal discontinuities as well as the Moho was estimated by calculating the time difference in the arrival of the converted Ps phase relative to the direct P wave. For depth estimation, we used the IASP91 reference model.The estimated Moho depth beneath the Shoshtar station in the Zagros Fold and Thrust Belt (ZFTB) is estimated to be 50.5 km, which increases to a depth of about 67 km in the Sanandaj-Sirjan metamorphic zone (SSZ). Furthermore, the Moho depth decreases to approximately 42 km beneath the GHVR station located in the Uroumieh Dokhtar metamorphic zone (UDMA). A local crustal thickening of approxmiately 67 km is observed beneath the DAMV station located near the Damavand volcano in the Alborz zone. The Zhu & Kanamori method was also employed to determine the crustal thickness (H) and Vp/Vs ratio by using the arrival times of the crustal multiples.

Rectangle array is widely used in resistivity and induced polarization (IP) studies. The purpose of this array is to restrict the wide areas especially in the exploration of sulfide minerals. On the contrary to the wide application of this array, less attention has been paid to the results of modelling and true estimates. The interpretations are normally qualitative.A 3D resistivity and IP model was developed for the geoelectric surveys with a rectangle array. We used the COMSOL environment to solve the DC-resistivity and Maxwell’s equations by the finite element method. Codes were programmed in Matlab language.A common geometry of the model space was used for both resistivity and IP modelling. In the rectangle array, two current electrodes were located in a large distance and different potentials were measured on the profiles parallel to the current electrodes. Our model was formed by a homogeneous half space (a large block with dimensions 800×800×500m3, with a resistivity of 400 ohm.m). Two current electrodes with a 200-m distance were located on the surface. Non-polarizing electrodes were located in a 5-m distance. The two measuring electrodes were moved on the profiles (parallel to the current electrode direction). Nine parallel profiles were located symmetrically on each side of the current electrode direction. Each profile had a 40-m length. The distance between the profiles was 5 m. The electrode configuration could be changed in the model. IP and resistivity anomalies could be created from different blocked locations in the subsurface (into the half space). The blocks near the potential profiles had small dimensions. The block sizes increased as the depth increased. We calculated the geometrical factor for the half-space. Apparent resistivity for each dual potential electrode was calculated from different potentials measured during the code execution and its geometry factors.We compared the results from different anomalies by sensitivity Dpa/Dpi, where Dpa is the difference between the apparent resistivity of the anomaly and the homogeneous half-space (400 ohm.m) and Dpi is the difference between the resistivity value of the half-space and the anomaly in block number i.Frequency domain IP was calculated directly from Maxwell's equations. Block scheme of the model done in the modelling space resistivity were used here. There was a resistivity value for each subsurface block in the resistivity model while there were a resistivity and a dielectric value for each block in an IP model. Resistivity and dielectric values of each block are functions of the frequency. We used the Cole-Cole model in order to calculate the resistivity and dielectric values in each frequency. Four intrinsic Cole-Cole parameters (DC-resistivity, chargeability, time constant and frequency relaxation) were considered for each block. During the frequency changes, these parameters were constant. Finally, apparent resistivity and percentage frequency effect (PFE) maps were calculated in a frequency range of 0.1 to 12000 Hz.In this research, we studied the effect of size, depth and overburden thickness of the subsurface anomalies. The geoelectrical effects of vertical and horizontal anomalies were investigated. The impact of the potential electrode separation was also verified. The results showed that the qualitative interpretation using the apparent resistivity and appearent percentage frequency effect (PFE) maps was correct when anomaly had remarkable dimensions, a small depth and a high conductivity. The apparent-resistivity map reflected the effect of conductive and polarisable anomalies better than the PFE map.