Institute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Time-Frequency based single-frequency seismic attributeTime-Frequency based single-frequency seismic attribute27139FAJournal Article19700101Spectral decomposition is a powerful tool for analysis of seismic data. Fourier transform determines the frequency contents of a signal. But for analysis of non-stationary signals, 1-D transform to frequency domain is not sufficient. In early years, transforming of seismic traces into time and frequency domain was done via windowed Fourier transform, called a Short Time Fourier Transform (STFT). In this method the resolution of the results in time-frequency domain was controlled by the width of the selected window. Continuous Wavelet transform (CWT) was a remedy to solve this problem by using the scaleable wavelets. The scale in CWT can be related to the frequency bandwidth of the wavelet. By converting the scale to frequency one can get the time-frequency map which is comparable to the time-frequency map obtained from STFT.
In this study, we applied CWT on a seismic section and extracted a single frequency seismic section from the resultant cube. The extracted sections were used as a seismic attribute to detect low frequency shadow of the hydrocarbon reserves over the study area as well as to analyze the existing thin layers on a synthetic seismic section.
Time-Frequency Analysis Methods, (a) STFT: The instantaneous frequency has often been considered as a way to introduce frequency dependence on time. If the signal is not narrow-band, however, the instantaneous frequency averages different spectral component in time. To become accurate in time, we therefore need a two dimensional time-frequency representation of the signal composed of spectral characteristics depending on time. By assuming that the signal is stationary when seen through a window of limited extent and moving the window along the signal in time, the Fourier transform of the windowed signals yields the Short Time Fourier Transform which is a two dimensional function in time-frequency domain.
(b) CWT: STFT requires a fixed time support. In practice, seismic data are non-stationary and using the STFT may not produce very reliable time-frequency map. Fixed window length and hence, fixed time-frequency resolution is a fundamental difficulty with the STFT for analyzing a non-stationary signal.
Continuous wavelet transform 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 honoring Heisenberg’s uncertainty principle (Mallat, 1999; Daubechies, 1992). In this study we used Morlet wavelet that provides an easy interpretation from scale to frequency (Torrence and Compo, 1998).
Single Frequency Seismic Section, Application 1: In this study we used a seismic section over one of the reservoirs in the South-West of Iran, where the reservoir interval is in sandstone in the Asmari Formation. Productivity is proven, and especially on its middle and upper part contains hydrocarbons.
Mapping of a seismic trace into the time-frequency domain produces a two dimensional data set by adding a frequency axis. In a similar way a two dimensional seismic section will generate a 3D data cube in which the third axis is frequency up to the Nyquist frequency. Sections of single frequency extracted from the cube are called single frequency seismic section (SFS). Comparison of different SFSs can be utilized to detect low frequency shadows caused by the presence of the hydrocarbon reservoirs. This method can potentially be utilized as a tool for direct hydrocarbon detection (Zabihi, 2006). We compared the single frequency seismic section of the STFT and CWT and discussed the differences between the results of the two methods. In a single frequency seismic section at frequency 15 Hz we found a low frequency anomaly below the reservoir which is a known phenomena. This anomaly disappeared at higher frequency single frequency seismic sections.
Application 2: Another application is thin layer analysis in a time-frequency domain. The idea is that tuning thickness is dependent on the dominant freSpectral decomposition is a powerful tool for analysis of seismic data. Fourier transform determines the frequency contents of a signal. But for analysis of non-stationary signals, 1-D transform to frequency domain is not sufficient. In early years, transforming of seismic traces into time and frequency domain was done via windowed Fourier transform, called a Short Time Fourier Transform (STFT). In this method the resolution of the results in time-frequency domain was controlled by the width of the selected window. Continuous Wavelet transform (CWT) was a remedy to solve this problem by using the scaleable wavelets. The scale in CWT can be related to the frequency bandwidth of the wavelet. By converting the scale to frequency one can get the time-frequency map which is comparable to the time-frequency map obtained from STFT.
In this study, we applied CWT on a seismic section and extracted a single frequency seismic section from the resultant cube. The extracted sections were used as a seismic attribute to detect low frequency shadow of the hydrocarbon reserves over the study area as well as to analyze the existing thin layers on a synthetic seismic section.
Time-Frequency Analysis Methods, (a) STFT: The instantaneous frequency has often been considered as a way to introduce frequency dependence on time. If the signal is not narrow-band, however, the instantaneous frequency averages different spectral component in time. To become accurate in time, we therefore need a two dimensional time-frequency representation of the signal composed of spectral characteristics depending on time. By assuming that the signal is stationary when seen through a window of limited extent and moving the window along the signal in time, the Fourier transform of the windowed signals yields the Short Time Fourier Transform which is a two dimensional function in time-frequency domain.
(b) CWT: STFT requires a fixed time support. In practice, seismic data are non-stationary and using the STFT may not produce very reliable time-frequency map. Fixed window length and hence, fixed time-frequency resolution is a fundamental difficulty with the STFT for analyzing a non-stationary signal.
Continuous wavelet transform 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 honoring Heisenberg’s uncertainty principle (Mallat, 1999; Daubechies, 1992). In this study we used Morlet wavelet that provides an easy interpretation from scale to frequency (Torrence and Compo, 1998).
Single Frequency Seismic Section, Application 1: In this study we used a seismic section over one of the reservoirs in the South-West of Iran, where the reservoir interval is in sandstone in the Asmari Formation. Productivity is proven, and especially on its middle and upper part contains hydrocarbons.
Mapping of a seismic trace into the time-frequency domain produces a two dimensional data set by adding a frequency axis. In a similar way a two dimensional seismic section will generate a 3D data cube in which the third axis is frequency up to the Nyquist frequency. Sections of single frequency extracted from the cube are called single frequency seismic section (SFS). Comparison of different SFSs can be utilized to detect low frequency shadows caused by the presence of the hydrocarbon reservoirs. This method can potentially be utilized as a tool for direct hydrocarbon detection (Zabihi, 2006). We compared the single frequency seismic section of the STFT and CWT and discussed the differences between the results of the two methods. In a single frequency seismic section at frequency 15 Hz we found a low frequency anomaly below the reservoir which is a known phenomena. This anomaly disappeared at higher frequency single frequency seismic sections.
Application 2: Another application is thin layer analysis in a time-frequency domain. The idea is that tuning thickness is dependent on the dominant frehttps://jesphys.ut.ac.ir/article_27139_a0eadd892a8e3afb901572cf0d87b0e8.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722A review of MT data processingA review of MT data processing27140FAJournal Article19700101Magnetotelluric (MT) method is an important passive surface geophysical method which uses the Earth’s natural electromagnetic fields to investigate the electrical resistivity structure of the subsurface. The depth of investigation of MT is much higher than that of other electromagnetic (EM) methods (Vozoff, 1991). For a general conductivity distribution in the Earth, the horizontal electric field components are related to horizontal magnetic field components by the Impedance Tensor (Z):
(1)
Which shows vertical and horizontal variations of subsurface conductivity. Apparent resistivity ( ) and phase ( ) are the desired quantities which are calculated from Impedance matrix by the following expressions:
(2)
and are free space permeability and angular frequency. DET is determinant data. Time series are measured in frequency interval of 0.001-1000 and frequency spectra are used to estimate the Impedance Tensor as a function of frequency.The Impedance Tensor determinant (or effective impedance), , is as below (Berdichevsky and Dmitriev, 1976):
(3)
The advantage of using the determinant data is that it provides a useful average of the impedance for all current directions. Furthermore, no mode identifications (transverse electric, TE mode: current in parallel with the strike; or transverse magnetic, TM-mode: current perpendicular to the strike) are required, static shift corrections are not made, and the dimensionality of the data is not considered, since the effective impedance is believed to represent an average that provides robust 1D and 2D models.
MT data are as time series and information of the subsurface structure cannot be given. Then, MT data must be processed to be prepared for the inversion and interpretation. In the following sections, a review of MT processing steps consisting of time series analysis and also steps called manual processing is presented.
The analysis of the time series: The main problem in the processing of the MT data is that the field observations (raw data) are as time series (figure 1) but the basic theories are in the frequency domain. In this section, an explanation is given to how a single spectral matrix is computed from a specific time series section for all target frequency lines.
Transformation into frequency domain: The time series of each channel (2 electric field components and 3 magnetic field components) are transformed to the Fourier series by FFT (Fast Fourier Transform). Depending on the sampling rate in the specific
band and the adjusted section length, different resolution and frequency ranges are achieved.
The trend elimination: Before applying the Fourier transform, the raw data is processed with trend elimination. The trend elimination removes a possible systematic deviation from the x-axes. The mean value is set to zero and a straight line in the data (trend) which differs from the x-axis is removed. Figure 2 illustrates the trend elimination procedure.
The window function (windowing): After the trend elimination, the multiplication of the time series with a window function (windowing) follows. This is necessary in order to suppress side effects (discontinuities are generated at the edges) at the FFT and to obtain an optimum sharp mapping of the frequency spectra.
The fast Fourier transform (FFT): After the time series has been treated with the trend elimination and the windowing the fast Fourier transform (FFT) is applied to the data. The given time series has now been transformed into the frequency domain. All the further steps of computation are done with spectral data.
The calibration of spectra: When registrating the time series, the data is affected by the transfer function of the measurement instruments. In order to eliminate this influence, the data must be calibrated. The spectra are multiplied by the reMagnetotelluric (MT) method is an important passive surface geophysical method which uses the Earth’s natural electromagnetic fields to investigate the electrical resistivity structure of the subsurface. The depth of investigation of MT is much higher than that of other electromagnetic (EM) methods (Vozoff, 1991). For a general conductivity distribution in the Earth, the horizontal electric field components are related to horizontal magnetic field components by the Impedance Tensor (Z):
(1)
Which shows vertical and horizontal variations of subsurface conductivity. Apparent resistivity ( ) and phase ( ) are the desired quantities which are calculated from Impedance matrix by the following expressions:
(2)
and are free space permeability and angular frequency. DET is determinant data. Time series are measured in frequency interval of 0.001-1000 and frequency spectra are used to estimate the Impedance Tensor as a function of frequency.The Impedance Tensor determinant (or effective impedance), , is as below (Berdichevsky and Dmitriev, 1976):
(3)
The advantage of using the determinant data is that it provides a useful average of the impedance for all current directions. Furthermore, no mode identifications (transverse electric, TE mode: current in parallel with the strike; or transverse magnetic, TM-mode: current perpendicular to the strike) are required, static shift corrections are not made, and the dimensionality of the data is not considered, since the effective impedance is believed to represent an average that provides robust 1D and 2D models.
MT data are as time series and information of the subsurface structure cannot be given. Then, MT data must be processed to be prepared for the inversion and interpretation. In the following sections, a review of MT processing steps consisting of time series analysis and also steps called manual processing is presented.
The analysis of the time series: The main problem in the processing of the MT data is that the field observations (raw data) are as time series (figure 1) but the basic theories are in the frequency domain. In this section, an explanation is given to how a single spectral matrix is computed from a specific time series section for all target frequency lines.
Transformation into frequency domain: The time series of each channel (2 electric field components and 3 magnetic field components) are transformed to the Fourier series by FFT (Fast Fourier Transform). Depending on the sampling rate in the specific
band and the adjusted section length, different resolution and frequency ranges are achieved.
The trend elimination: Before applying the Fourier transform, the raw data is processed with trend elimination. The trend elimination removes a possible systematic deviation from the x-axes. The mean value is set to zero and a straight line in the data (trend) which differs from the x-axis is removed. Figure 2 illustrates the trend elimination procedure.
The window function (windowing): After the trend elimination, the multiplication of the time series with a window function (windowing) follows. This is necessary in order to suppress side effects (discontinuities are generated at the edges) at the FFT and to obtain an optimum sharp mapping of the frequency spectra.
The fast Fourier transform (FFT): After the time series has been treated with the trend elimination and the windowing the fast Fourier transform (FFT) is applied to the data. The given time series has now been transformed into the frequency domain. All the further steps of computation are done with spectral data.
The calibration of spectra: When registrating the time series, the data is affected by the transfer function of the measurement instruments. In order to eliminate this influence, the data must be calibrated. The spectra are multiplied by the rehttps://jesphys.ut.ac.ir/article_27140_4e680f92197f325f2a204942e0be3504.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Paleostress analysis of horizontal Plio-Quaternary deposits in the NW of ZanjanPaleostress analysis of horizontal Plio-Quaternary deposits in the NW of Zanjan27132FAJournal Article19700101https://jesphys.ut.ac.ir/article_27132_db8469cdfd0503af6459305adf02f6f8.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Aeromagnetic data processing of Basiran Area using cubic splinesAeromagnetic data processing of Basiran Area using cubic splines27133FAJournal Article19700101https://jesphys.ut.ac.ir/article_27133_7654b85f0259871433574363385369b4.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722The effect of important parameters on simulation based on stochastic finite fault modelingThe effect of important parameters on simulation based on stochastic finite fault modeling27134FAJournal Article19700101Contrary to the long period ground motions that can be predicted and estimated, high frequency ground motions have random character and behave stochastically. Stochastic modeling methods are usually used for the modeling of high frequency ground motions where there are not strong ground motion records. Stochastic methods are of two kinds: in the first one, the seismic source is considered as a point source and in the second, modeling is based on the finite fault. Seismic source is considered as a rectangular fault plane divided by some subfaults in its longitudinal and traversal directions.
Stochastic modeling method, using the seismic point source was presented by Boore in 1983. In the Boore stochastic method, the high frequency motions of earthquakes can be presented as the Gaussian noise with limited frequency band having the mean source spectrum w2. In this method a shape function is applied to a time series of white noise with zero mean, bringing it out of the stationary status, then the Fourier transform is applied to the noise and the amplitude spectrum of this time series is substituted by a desired spectrum and the phase spectrum remains unchanged. Transformation back to the time domain results in a time series whose amplitude spectrum is exactly in accordance with a specified spectrum.
In the simulation based on finite fault modeling, each subfault is considered as a point source, using the source model presented by Brune with a corner frequency and a constant stress drop. The target accelerogram is obtained by summation of accelerograms generated by each subfaults and by considering their corresponding delay times. This simulation method is used broadly in the assessment of strong ground motions. The first ground motion simulation program, FINISM, was finite fault modeling, based on the Boore stochastic method. The new version of the FINISM program, which is called EXSIM, has been used in this study.
Three parameters, quality factor, kappa and stress drop which have effects on the high frequency amplitudes, are important parameters in stochastic modeling which are considered in this study. Moreover the effects of shear wave velocity and density on the simulation accelerations are studied as well. In these simulations to distinguish the effect of a specific parameter, the amount of that parameter is changed, while other parameters are kept constant. It is found that the main effect of quality factor is in the high frequencies and variation of this parameter has no significant effect on the spectral accelerations in low frequencies. With increasing quality factor, the spectral accelerations as well as peak ground acceleration (PGA) which correspond to the spectral accelerations in high frequencies, will increase. This increase is greater in high frequencies and smaller in the low ones. The spectral acceleration and PGA reduce when kappa increases but the acceleration reduction is higher in high frequencies. It has been seen that the spectral accelerations increase with the increase in stress drop. Of course, this increment is small in low frequencies, but considerable in high frequencies. Rupture velocity is usually assumed as 80% of shear wave velocity. As shear wave velocity increases, the rupture velocity increases as well and consequently the fault will fracture more rapidly. Therefore, the delay time of subfault pulses reaching the observation point will reduce. This shows that if shear wave velocity increases, the duration of simulated accelerograms reduces slightly.
Finally the density effect on the simulation results is investigated in this study and it is concluded that if the density increases by ? then PGA, spectral accelerations and Fourier amplitude of simulated accelerogram will increases by 1/??Contrary to the long period ground motions that can be predicted and estimated, high frequency ground motions have random character and behave stochastically. Stochastic modeling methods are usually used for the modeling of high frequency ground motions where there are not strong ground motion records. Stochastic methods are of two kinds: in the first one, the seismic source is considered as a point source and in the second, modeling is based on the finite fault. Seismic source is considered as a rectangular fault plane divided by some subfaults in its longitudinal and traversal directions.
Stochastic modeling method, using the seismic point source was presented by Boore in 1983. In the Boore stochastic method, the high frequency motions of earthquakes can be presented as the Gaussian noise with limited frequency band having the mean source spectrum w2. In this method a shape function is applied to a time series of white noise with zero mean, bringing it out of the stationary status, then the Fourier transform is applied to the noise and the amplitude spectrum of this time series is substituted by a desired spectrum and the phase spectrum remains unchanged. Transformation back to the time domain results in a time series whose amplitude spectrum is exactly in accordance with a specified spectrum.
In the simulation based on finite fault modeling, each subfault is considered as a point source, using the source model presented by Brune with a corner frequency and a constant stress drop. The target accelerogram is obtained by summation of accelerograms generated by each subfaults and by considering their corresponding delay times. This simulation method is used broadly in the assessment of strong ground motions. The first ground motion simulation program, FINISM, was finite fault modeling, based on the Boore stochastic method. The new version of the FINISM program, which is called EXSIM, has been used in this study.
Three parameters, quality factor, kappa and stress drop which have effects on the high frequency amplitudes, are important parameters in stochastic modeling which are considered in this study. Moreover the effects of shear wave velocity and density on the simulation accelerations are studied as well. In these simulations to distinguish the effect of a specific parameter, the amount of that parameter is changed, while other parameters are kept constant. It is found that the main effect of quality factor is in the high frequencies and variation of this parameter has no significant effect on the spectral accelerations in low frequencies. With increasing quality factor, the spectral accelerations as well as peak ground acceleration (PGA) which correspond to the spectral accelerations in high frequencies, will increase. This increase is greater in high frequencies and smaller in the low ones. The spectral acceleration and PGA reduce when kappa increases but the acceleration reduction is higher in high frequencies. It has been seen that the spectral accelerations increase with the increase in stress drop. Of course, this increment is small in low frequencies, but considerable in high frequencies. Rupture velocity is usually assumed as 80% of shear wave velocity. As shear wave velocity increases, the rupture velocity increases as well and consequently the fault will fracture more rapidly. Therefore, the delay time of subfault pulses reaching the observation point will reduce. This shows that if shear wave velocity increases, the duration of simulated accelerograms reduces slightly.
Finally the density effect on the simulation results is investigated in this study and it is concluded that if the density increases by ? then PGA, spectral accelerations and Fourier amplitude of simulated accelerogram will increases by 1/??https://jesphys.ut.ac.ir/article_27134_800949f68eb8406cc31158ad3e276658.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Identification of the Sahneh buried fault using magnetic and VLF MethodsIdentification of the Sahneh buried fault using magnetic and VLF Methods27135FAJournal Article19700101https://jesphys.ut.ac.ir/article_27135_d218cf73186f2200a9a685313d0b86ad.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Sea level mean monthly variations in the Persian Gulf, Oman Sea and the North of the Arabian Sea, in 1994Sea level mean monthly variations in the Persian Gulf, Oman Sea and the North of the Arabian Sea, in 199427136FAJournal Article19700101Sea level variations in the Persian Gulf, the Oman Sea and the north of the Arabian Sea have been investigated. For this purpose, 365 daily satellite images provided by the TOPEX/Poseidon (T/P) and Jason-1 satellites were processed. Programming in Matlab software environment was also carried out for some parts of image processing.
The results of this research reveal that water level fluctuations are effectively observed only in the southern parts of the Persian Gulf; the southern region of Bahrain and Qatar, sometimes in the central and northern parts of the Persian Gulf; along the Bushehr Coasts, near the Arvandrood and Karoon rivers mouths in Abadan, and in the central parts of the northern Arabian Sea including; the eastern part of Muscat and Soor and also near the Chabahar coast of the Oman Sea. In other regions, water level fluctuations were very small and approximately about the average water level in the oceans in 1994.
These regions are important not only from the viewpoint of water level fluctuations, but also for other oceanographic and climatic parameters. Therefore, the characteristics of the water level fluctuations in the Persian Gulf, Oman Sea and the north of the Arabian Sea can be studied better by classifying and breaking these regions into the following main areas:
1. Bushehr coast to Arvandrood river mouth:
Southward currents resulting from northwesterly and westerly winds cause the water level to decrease on the Bushehr coast and thus to increase in the centeral region of the Persian Gulf.
2. Arvandrood river mouth and Kuwait and Saudi Arabia coasts:
Obtained results for this region indicate that water level in this area is relatively low and freshwater river runoff has a small impact on it, too.
3. Southern coasts of Bahrain and Qatar:
The large evaporation over the Persian Gulf and the shallowness of the water depth results in a high saline water (to a maximum value of about 50 psu) in this area. The dense water formed leaves this area through the deep part of the Strait of Hormuz and results in a decreasing water level in January, April, July, September and November. In February, March, May and October, thermal mixing causes density currents initiated from this area to weaken, a situation which leads to an increase in the water level along the southern coasts of Bahrain and Qatar during these months.
4. Central area of the Persian Gulf:
This region is separated from the North by a temperature and salinity front. Inflow from the Hormuz Strait in conjunction with down welling and areas of intense evaporation (> 40psu) create a cyclonic gyre in the center of the Persian Gulf. This gyre causes the water level to increase in this area in January, April, July, September and November.
5. The north of the Arabian Sea:
The northward flowing Somali current and the Oman coastal current are the most important and powerful currents in the Arabian Sea. The water level near 23°N is usually lower than the mean water level in the ocean.
6. Southern coasts of the Oman Sea:
The outflow from the Persian Gulf mainly goes toward this area. The water level has a weak fluctuation and is usually around zero in this area.
7. The line connecting Muscat to Chabahar and eastern coast of Chabahar:
The upwelling usually occurs in this area and the water level is usually greater than the mean water level in the ocean.
8. Rasal Hadd Area:
At the easternmost point of Oman (Rasal Hadd), interaction between the northward flowing Somali Current and Oman Coastal Current leads to forming the Rasal Hadd jet (also termed the Rasal Hadd front).
Moreover, a study of water level variations in 1994 results in distinguishing 3 different temporal patterns as; January pattern, October pattern and Calm pattern.
The results reveal that both of the maximum and minimum water levels observed in the south-east of Qatar were +57.5 cm and -47.5 cm, respectively. Water level fluctuations were intensified in October and January and were wSea level variations in the Persian Gulf, the Oman Sea and the north of the Arabian Sea have been investigated. For this purpose, 365 daily satellite images provided by the TOPEX/Poseidon (T/P) and Jason-1 satellites were processed. Programming in Matlab software environment was also carried out for some parts of image processing.
The results of this research reveal that water level fluctuations are effectively observed only in the southern parts of the Persian Gulf; the southern region of Bahrain and Qatar, sometimes in the central and northern parts of the Persian Gulf; along the Bushehr Coasts, near the Arvandrood and Karoon rivers mouths in Abadan, and in the central parts of the northern Arabian Sea including; the eastern part of Muscat and Soor and also near the Chabahar coast of the Oman Sea. In other regions, water level fluctuations were very small and approximately about the average water level in the oceans in 1994.
These regions are important not only from the viewpoint of water level fluctuations, but also for other oceanographic and climatic parameters. Therefore, the characteristics of the water level fluctuations in the Persian Gulf, Oman Sea and the north of the Arabian Sea can be studied better by classifying and breaking these regions into the following main areas:
1. Bushehr coast to Arvandrood river mouth:
Southward currents resulting from northwesterly and westerly winds cause the water level to decrease on the Bushehr coast and thus to increase in the centeral region of the Persian Gulf.
2. Arvandrood river mouth and Kuwait and Saudi Arabia coasts:
Obtained results for this region indicate that water level in this area is relatively low and freshwater river runoff has a small impact on it, too.
3. Southern coasts of Bahrain and Qatar:
The large evaporation over the Persian Gulf and the shallowness of the water depth results in a high saline water (to a maximum value of about 50 psu) in this area. The dense water formed leaves this area through the deep part of the Strait of Hormuz and results in a decreasing water level in January, April, July, September and November. In February, March, May and October, thermal mixing causes density currents initiated from this area to weaken, a situation which leads to an increase in the water level along the southern coasts of Bahrain and Qatar during these months.
4. Central area of the Persian Gulf:
This region is separated from the North by a temperature and salinity front. Inflow from the Hormuz Strait in conjunction with down welling and areas of intense evaporation (> 40psu) create a cyclonic gyre in the center of the Persian Gulf. This gyre causes the water level to increase in this area in January, April, July, September and November.
5. The north of the Arabian Sea:
The northward flowing Somali current and the Oman coastal current are the most important and powerful currents in the Arabian Sea. The water level near 23°N is usually lower than the mean water level in the ocean.
6. Southern coasts of the Oman Sea:
The outflow from the Persian Gulf mainly goes toward this area. The water level has a weak fluctuation and is usually around zero in this area.
7. The line connecting Muscat to Chabahar and eastern coast of Chabahar:
The upwelling usually occurs in this area and the water level is usually greater than the mean water level in the ocean.
8. Rasal Hadd Area:
At the easternmost point of Oman (Rasal Hadd), interaction between the northward flowing Somali Current and Oman Coastal Current leads to forming the Rasal Hadd jet (also termed the Rasal Hadd front).
Moreover, a study of water level variations in 1994 results in distinguishing 3 different temporal patterns as; January pattern, October pattern and Calm pattern.
The results reveal that both of the maximum and minimum water levels observed in the south-east of Qatar were +57.5 cm and -47.5 cm, respectively. Water level fluctuations were intensified in October and January and were whttps://jesphys.ut.ac.ir/article_27136_0d3b9a441492e895e724ca277e323591.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Determining sediment thickness in the Oman Sea using free air anomaly through satellite altimetry observationDetermining sediment thickness in the Oman Sea using free air anomaly through satellite altimetry observation27137FAJournal Article19700101Changing altimetry information from satellites on the surface of seas and oceans, into anomaly gravity for example free air anomaly, is a fundamentally new method presented by researches. The determination of the free air gravity anomaly over the Earth’s marine regions has led to a major improvement in our understanding of plate tectonic processes relating to oceanic ridges, the formation of marine sediment. In recent years satellite altimetry has emerged as a powerful reconnaissance tool for exploration of sedimentary basins on continental margins as well as in deep-water regions. With the advent of more altimetric missions, with increasing accuracies and varying orbital configurations, it has become possible to generate large-scale altimeter-derived residual geoid and gravity anomaly maps over the oceans. Satellite altimetry is one of the most accurate and unique techniques ever known. Applying this technique, we are able to dominate much surface observation compared with parallel techniques. Measuring the topography of the sea surface and possible changes during time interval, the due altimeters provide us with useful information about gravity field, form and structure of the seabed, heat conditions, salinity and oceanic currents.
Through satellite altimetry observations and known coordinates on Mean Sea Level (MSL), we can measure the differential gravity potential between reference Ellipsoid and mean sea level by reversing Bruns formula. Obtaining the potential of geoid, we can estimate the ellipsoidal potential. Then through Abel-Poisson's integral in certain conditions, we can transfer the obtained potential to the sea surface and have access to gravity acceleration within intended places. Then having the gravity acceleration we can compute the free-air gravity anomalies. The case study evaluated in the Oman Sea contains the following stages:
1. Computation of Mean Sea level (MSL) from satellite altimetry observations.
2. Determining the Sea Surface Topography (SST) obtained via oceanographic studies.
3. Conversion of the MSL level to geoidal undulations by difference SST and MSL.
3. Converting the geoidal undulations into potential value at the surface of the reference ellipsoid using inverse Bruns formula.
4. Removal of the effect of ellipsoidal harmonic expansion to 360 degree and order computational point.
5. Upward continuation of the incremental gravity potential obtained from the removal steps to gravity intensity at the point of interest by using gradient ellipsoidal Abel-Poisson integral.
6. Restoring the removed effect at the fourth step at computational point of step 5.
In order to gain global and regional effects we applied geo-potential models. Such models have the advantage of providing us with a large covering area in a minimum of time, high speed and of certainly being economical. Future application of this research includes analyzing geological structures via interpreting gravity anomalies in any sea region.Through the due anomaly and the so-called three dimensional inverse gravity problem in space domain and frequency domain, one can determine the depth of the basement or the same sediment thickness. The methods proposed by Chakravarthi, Parker and Oldenburg apply absolution inverse problem in the space and frequency domain in the Oman Sea area, to be used for determination the sediment thickness. For determining sediment thickness via solving the inverse gravity problem in the space domain, the method of Chakravarthi, and Sundarajan is to be used. In this method density is interchanyeable with depth and to show this dependency we have made use of a parabolic function. In the basin sedimentary, gravity arising from a prism, is calculated. The method of interpretation begins by calculating the initial depth estimations of a sedimentary basin. Oldenburg deduced a method to compute the density contrast topography from the gravity anomaly reversely in a two-dimensional Cartesian coordinate systemChanging altimetry information from satellites on the surface of seas and oceans, into anomaly gravity for example free air anomaly, is a fundamentally new method presented by researches. The determination of the free air gravity anomaly over the Earth’s marine regions has led to a major improvement in our understanding of plate tectonic processes relating to oceanic ridges, the formation of marine sediment. In recent years satellite altimetry has emerged as a powerful reconnaissance tool for exploration of sedimentary basins on continental margins as well as in deep-water regions. With the advent of more altimetric missions, with increasing accuracies and varying orbital configurations, it has become possible to generate large-scale altimeter-derived residual geoid and gravity anomaly maps over the oceans. Satellite altimetry is one of the most accurate and unique techniques ever known. Applying this technique, we are able to dominate much surface observation compared with parallel techniques. Measuring the topography of the sea surface and possible changes during time interval, the due altimeters provide us with useful information about gravity field, form and structure of the seabed, heat conditions, salinity and oceanic currents.
Through satellite altimetry observations and known coordinates on Mean Sea Level (MSL), we can measure the differential gravity potential between reference Ellipsoid and mean sea level by reversing Bruns formula. Obtaining the potential of geoid, we can estimate the ellipsoidal potential. Then through Abel-Poisson's integral in certain conditions, we can transfer the obtained potential to the sea surface and have access to gravity acceleration within intended places. Then having the gravity acceleration we can compute the free-air gravity anomalies. The case study evaluated in the Oman Sea contains the following stages:
1. Computation of Mean Sea level (MSL) from satellite altimetry observations.
2. Determining the Sea Surface Topography (SST) obtained via oceanographic studies.
3. Conversion of the MSL level to geoidal undulations by difference SST and MSL.
3. Converting the geoidal undulations into potential value at the surface of the reference ellipsoid using inverse Bruns formula.
4. Removal of the effect of ellipsoidal harmonic expansion to 360 degree and order computational point.
5. Upward continuation of the incremental gravity potential obtained from the removal steps to gravity intensity at the point of interest by using gradient ellipsoidal Abel-Poisson integral.
6. Restoring the removed effect at the fourth step at computational point of step 5.
In order to gain global and regional effects we applied geo-potential models. Such models have the advantage of providing us with a large covering area in a minimum of time, high speed and of certainly being economical. Future application of this research includes analyzing geological structures via interpreting gravity anomalies in any sea region.Through the due anomaly and the so-called three dimensional inverse gravity problem in space domain and frequency domain, one can determine the depth of the basement or the same sediment thickness. The methods proposed by Chakravarthi, Parker and Oldenburg apply absolution inverse problem in the space and frequency domain in the Oman Sea area, to be used for determination the sediment thickness. For determining sediment thickness via solving the inverse gravity problem in the space domain, the method of Chakravarthi, and Sundarajan is to be used. In this method density is interchanyeable with depth and to show this dependency we have made use of a parabolic function. In the basin sedimentary, gravity arising from a prism, is calculated. The method of interpretation begins by calculating the initial depth estimations of a sedimentary basin. Oldenburg deduced a method to compute the density contrast topography from the gravity anomaly reversely in a two-dimensional Cartesian coordinate systemhttps://jesphys.ut.ac.ir/article_27137_2a12b43a6deb1d1f8fa74734d8aea908.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722The synoptic and dynamic study for maximum precipitation over the Khorasan regionThe synoptic and dynamic study for maximum precipitation over the Khorasan region27138FAJournal Article19700101In this paper, synoptic patterns associated with maximum precipitation over the Khorasan region, between 1985-2000 with the computation of some forcing functions are studied and classified into three types.
Type A: Consists of the anticyclones that move from the Scandinavian Peninsula and central-Europe in an east and south-east direction during cold seasons, with relatively high speed and cold cores. They first affect the Caspian Sea area and then the Khorasan region, and cause little of precipitation and very cold weather which lasts for 24 to 36 hours in the region. For this type the maximum amount of precipitation for 24 hours recorded was 16mm in Goochan city.
Type B: includes steering frontal cyclones from east of the Mediterranean Sea that after passing the central part of Iran, affect the Khorasan region, and if these systems combine with a low pressure system from the Red Sea, that is a form of inverted thermal trough, which developed due to the effects of forcing functions they produce more precipitation than type A, but with a mild temperature. For this type maximum amount of precipitation recorded for 24 hours was 16mm in a lower latitude compared to type A, in Ferdos city.
Type C: includes cold core anticyclones that move from a high latitude (similar to type A) simultaneously with the steering cyclones (or trough) from the Mediterranean or the Red Sea (similar to type B) and theses two systems impact over Iran and produce a strong temperature and pressure gradient along the northern part of Iran, that makes a frontal zone over the Khorasan region and therefore heavy rain and snow is expected to fall over the region. For every type, the group which had considerable precipitation compared to other cases, their relative vorticity, absolute vorticity and advection are computed on 500hpa level. The maximum amount of precipitation in this period of study recorded for 24 hours was 40 mm in Mashhad city (of type C) at time of 1998.2.10. At this time relative vorticity is order of magnitude about , and its advection at 1998.2.9, is order of magnitude . So when a low pressure from a lower latitude, that has sufficient humidity and moves to the center of Iran , impact with a high pressure from upper latitude that has very cold air over Iran we will have high precipitation in the Khorasan region.In this paper, synoptic patterns associated with maximum precipitation over the Khorasan region, between 1985-2000 with the computation of some forcing functions are studied and classified into three types.
Type A: Consists of the anticyclones that move from the Scandinavian Peninsula and central-Europe in an east and south-east direction during cold seasons, with relatively high speed and cold cores. They first affect the Caspian Sea area and then the Khorasan region, and cause little of precipitation and very cold weather which lasts for 24 to 36 hours in the region. For this type the maximum amount of precipitation for 24 hours recorded was 16mm in Goochan city.
Type B: includes steering frontal cyclones from east of the Mediterranean Sea that after passing the central part of Iran, affect the Khorasan region, and if these systems combine with a low pressure system from the Red Sea, that is a form of inverted thermal trough, which developed due to the effects of forcing functions they produce more precipitation than type A, but with a mild temperature. For this type maximum amount of precipitation recorded for 24 hours was 16mm in a lower latitude compared to type A, in Ferdos city.
Type C: includes cold core anticyclones that move from a high latitude (similar to type A) simultaneously with the steering cyclones (or trough) from the Mediterranean or the Red Sea (similar to type B) and theses two systems impact over Iran and produce a strong temperature and pressure gradient along the northern part of Iran, that makes a frontal zone over the Khorasan region and therefore heavy rain and snow is expected to fall over the region. For every type, the group which had considerable precipitation compared to other cases, their relative vorticity, absolute vorticity and advection are computed on 500hpa level. The maximum amount of precipitation in this period of study recorded for 24 hours was 40 mm in Mashhad city (of type C) at time of 1998.2.10. At this time relative vorticity is order of magnitude about , and its advection at 1998.2.9, is order of magnitude . So when a low pressure from a lower latitude, that has sufficient humidity and moves to the center of Iran , impact with a high pressure from upper latitude that has very cold air over Iran we will have high precipitation in the Khorasan region.https://jesphys.ut.ac.ir/article_27138_21c25ddd9c548fe51d2bef2faceba5ba.pdfInstitute of Geophysics, University of TehranJournal of the Earth and Space Physics2538-371X34220080722Magnetic and ground penetrating Radar methods to detect shallow ancient underground cavities at Ghasr-e-Shirin town in the southwest of IranMagnetic and ground penetrating Radar methods to detect shallow ancient underground cavities at Ghasr-e-Shirin town in the southwest of Iran27141FAJournal Article19700101Ground penetrating radar (GPR) and magnetic methods were used at Ghasr-e-Shirin
town in Kermanshah province, southwest of Iran for detecting a series of large ancient
(ca 1500 years) underground cavities (Tagh). The area of study is a historical monument which is named as Emarat e Khosro. Radar and total intensity magnetic field measurements have been done on the eastern part of the complex, where some underground man-made cavities are located. Radar and magnetic profiles indicate these cavities easily. Radargrams, total magnetic map, first horizontal derivative and first vertical derivative of total magnetic field indicate the location of the anomalies, which conform with the real situation. A comparison of results of 50 MHZ and 100 MHZ radargrams shows that the latter distinguishes the anomalies better. This work is completely experimental to indicate the effect of underground cavities on radar and magnetic data. It is concluded that a combination of these methods is a proper tool for delineating underground cavities.Ground penetrating radar (GPR) and magnetic methods were used at Ghasr-e-Shirin
town in Kermanshah province, southwest of Iran for detecting a series of large ancient
(ca 1500 years) underground cavities (Tagh). The area of study is a historical monument which is named as Emarat e Khosro. Radar and total intensity magnetic field measurements have been done on the eastern part of the complex, where some underground man-made cavities are located. Radar and magnetic profiles indicate these cavities easily. Radargrams, total magnetic map, first horizontal derivative and first vertical derivative of total magnetic field indicate the location of the anomalies, which conform with the real situation. A comparison of results of 50 MHZ and 100 MHZ radargrams shows that the latter distinguishes the anomalies better. This work is completely experimental to indicate the effect of underground cavities on radar and magnetic data. It is concluded that a combination of these methods is a proper tool for delineating underground cavities.https://jesphys.ut.ac.ir/article_27141_ad623095a932b7f4cc00177edadcbdd8.pdf