فصلنامه فیزیک زمین و فضا
سال چهل و چهارم شماره 4 (زمستان 1397)

The rise of satellite altimetry is a revolution in the ocean sciences. Due to its global coverage and its high resolution, altimetry classically outperforms in situ water level measurement. Ekman and geostrophic currents are large parts of the ocean’s current, playing a vital role in global climate variations. According to the classic oceanography, Ekman and geostrophic currents can be calculated through the pressure gradient force as well as the friction force assuming that the water’s density is constant. Investigation of Ekman and geostrophic currents existence along with the determination of their velocities can profoundly affect the various events of oceanography and different interactive processes between the atmosphere and the ocean. Additionally, the measurement of sea currents can be useful in determination of contamination transport, seawater exchange, fisheries, oil transfer, immigration of aquatic animals and several marine activities (e.g. military, telecommunication, fishing and research activities) and also has different effects on the regional climate. In the current study, local and climatic conditions, Ekman and geostrophic currents and their velocities have been investigated based on the solution of Ekman and geostrophic equilibrium equations in the region of the Persian Gulf and the Oman Sea. To this end, using data of Saral and Jeason-2 altimetry satellites and surface wind data measured by ASCAT satellite, velocities values of v and u as well as the value and the direction of Ekman and geostrophic currents were extracted in forms of monthly data. The results were compared with obtained measurements by AVISO and NOAA for the region of the Persian Gulf and the Oman Sea, and based on the obtained results of this study, the difference in the value of these currents is about 1 cm/s.

Transforming geocentric Cartesian coordinates (X, Y, Z) to geodetic curvilinear coordinates (φ, λ, h) on a biaxial ellipsoid is one of the problems used in satellite positioning, coordinates conversion between reference systems, astronomy and geodetic calculations. For this purpose, various methods including Closed-form, Vector method and Fixed-point method have been developed. In this paper, a new paradigm for calculation of initial values is presented. According to the new initial values, two state of the art iterative methods are modified to calculate the geodetic height and the geodetic latitude accurately and without iteration. The results show that for those points with height values between -10 to 1,000,000 km (30-fold more than the altitude of GPS satellites), the maximum error of the calculated height and geodetic latitude is less than 1.5×10-8 m and 1×10-14 rad (error lower than 0.001 mm in horizontal), respectively.

In this paper a fast method for large-scale sparse inversion of magnetic data is considered. The L1-norm stabilizer is used to generate models with sharp and distinct interfaces. To deal with the non-linearity introduced by the L1-norm, a model-space iteratively reweighted least squares algorithm is used. The original model matrix is factorized using the Golub-Kahan bidiagonalization that projects the problem onto a Krylov subspace with a significantly reduced dimension. The model matrix of the projected system inherits the ill-conditioning of the original matrix, but the spectrum of the projected system accurately captures only a portion of the full spectrum. Equipped with the singular value decomposition of the projected system matrix, the solution of the projected problem is expressed using a filtered singular value expansion. This expansion depends on a regularization parameter which is determined using the method of Generalized Cross Validation (GCV), but here it is used for the truncated spectrum. This new technique, Truncated GCV (TGCV), is more effective compared with the standard GCV method. Numerical results using a synthetic example and real data demonstrate the efficiency of the presented algorithm.

Time-frequency filtering is an acceptable technique for attenuating noise in 2-D (time-space) and 3-D (time-space-space) reflection seismic data. The common approach for this purpose is transforming each seismic signal from 1-D time domain to a 2-D time-frequency domain and then denoising the signal by a designed filter and finally transforming back the filtered signal to original time domain. The technique is efficient for ground roll and also random noise attenuation. However, if we deal with a large data set and a great number of contaminated signals with ground roll noise, a much move consuming time will be required. In this paper, time-frequency filtering is formulated and carried out by a different approach. The data is transformed from original time-space domain into several single-frequency time-space domains, and the filters to reduce noise is designed in the new domains. The transform is easily and completely invertible. The employed time frequency analysis method is a high-resolution version of S-transform. Application to synthetic and real shot gathers confirms the good performance and efficiency of the method for attenuating ground roll noise and random noise.

The time lapse gravity method is a widely used technique to monitor the subsurface density changes in time and space. In hydrocarbon reservoirs, the density variations are due to different factors, such as: substitution of fluids with high density contrast, water influx, gas injection, and the variation in reservoir geomechanical behavior. Considering the monitoring of saturation changes in the reservoir that cannot be inferred directly by seismic survey, a forward modelling followed by a sensitivity study is performed to examine that in what conditions the saturation changes are detectable by means of 4D gravity method in the understudy reservoir. Then static and dynamic models of a giant multi-phase gas reservoir are constructed. Then, synthetic gravity data are generated after variation of production time intervals and the number of production and injection wells. In addition to detecting the gravity signal for shallower reservoirs with similar characteristics to our reservoir, a sensitivity analysis was conducted for variation in depth of the reservoir. As either the depth of the reservoir decreases or the number of the production wells and production time periods increases, the produced gravity signal is more prone to be detectable by means of modern offshore gravimeters. The gravity signal could be detected with the maximum magnitude range of 9 - in different scenarios as a consequence of gas-water substitution, which is consistent with water drive support from surrounding aquifers. Therefore, this method is applicable for providing complementary and even independent source of information about the saturation front changes in the under-study reservoir.

One of the methodologies employed in gravimetry exploration is eigenvector analysis of Gravity Gradient Tensor (GGT) which yields a solution including an estimation of a causative body’s Center of Mass (COM), dimensionality and strike direction. The eigenvectors of GGT give very rewarding clues about COM and strike direction. Additionally, the relationships between its components provide a quantity (I), representative of a geologic body dimensions. Although this procedure directly measures derivative components of gravity vector, it is costly and demands modern gradiometers. This study intends to obtain GGT from an ordinary gravity field measurement (gz). This Tensor is called Computed GGT (CGGT). In this procedure, some information about a geologic mass COM, strike and rough geometry, just after an ordinary gravimetry survey, is gained. Because of derivative calculations, the impacts of noise existing in the main measured gravity field (gz) could be destructive in CGGT solutions. Accordingly, to adjust them, a “moving twenty-five point averaging” method, and “upward continuation” are applied. The methodology is tested on various complex isolated and binary models in noisy conditions. It is also tested on real geologic example from a salt dome, USA, and all the results are highly acceptable.

Spatial aliasing is an unwanted side effect that produces artifacts during seismic data processing, imaging and interpolation. It is often caused by insufficient spatial sampling of seismic data and often happens in CMP (Common Mid-Point) gather. To tackle this artifact, several techniques have been developed in time-space domain as well as frequency domain such as frequency-wavenumber, frequency-space, and frequency-time. The main advantages of seismic interpolation in time-space domain over frequency domain are: a) frequency components of the initial signals are preserved, and b) the prior knowledge that a seismic event consists of many plane wave segments, can be used. Using the later advantage, a seismic event can be predicted by pursuing the continuity of seismic events in a trace-by-trace manner. This process, which has become popular in seismic data reconstruction and imaging within the past few years, is known as predictive painting. We use predictive painting to predict the wavefronts and two-way-travel time curves in regularly sampled CMP gathers followed by increasing the number of traces by cubic interpolation. Then, the amplitude of the interpolated trace is obtained by averaging the amplitudes of the neighbouring traces. Performance of the proposed method is demonstrated on several synthetic seismic data examples as well as a field data set.

In 98 km SW of Tres Picos in Mexico (15.022°N, 93.899°W, 47.40 km depth) a powerful earthquake of Mw=8.2 took place at 04:49:19 UTC (LT=UTC-05:00) on September 8, 2017. In this study, using three standard, classical and intelligent methods including median, Kalman filter, and Neural Network, respectively, the GPS Total Electron Content (TEC) measurements of three months were surveyed to detect the potential unusual variations around the time and location of Mexico earthquake. Every three implemented methods indicated a striking irregular variation of TEC at the earthquake time. However, on the earthquake day, the geomagnetic indices Dst and Ap have exceeded the allowed ranges and even reached maximum values during the studied time period. Besides, the solar index of F10.7 showed high activity around the earthquake day. Therefore, it is difficult to acknowledge the seismicity nature of the detected TEC unusual variations on earthquake day. Therefore, in this case, we encounter a mixed and complex behavior of ionosphere.

In this paper, a new method of ionospheric tomography is developed and evaluated based on the neural networks (NN). This new method is named ITNN. In this method, wavelet neural network (WNN) with particle swarm optimization (PSO) training algorithm is used to solve some of the ionospheric tomography problems. The results of ITNN method are compared with the residual minimization training neural network (RMTNN) and modified RMTNN (MRMTNN). In all three methods, empirical orthogonal functions (EOFs) are used as a vertical objective function. To apply the methods for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (in three days in 2007) are used. Besides, two GPS stations from international GNSS service (IGS) are used as test stations. The ionosonde data in Tehran (φ=35.73820, λ=51.38510) has been used for validating the reliability of the proposed methods. The minimum RMSE for RMTNN, MRMTNN, ITNN are 0.5312, 0.4743, 0.3465 (1011ele./m3) and the minimum bias are 0.4682, 0.3890, and 0.3368 (1011ele./m3) respectively. The results indicate the superiority of ITNN method over the other two methods.

Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database includes 7336 records situated in 11 features from daily brainstorm data within a twenty year period. The samples were selected based on a case study in Torbat-e Heydariyeh. 70% were chosen for learning and 30% were chosen for taking tests. From 7181 available data, 75% and 25% were used for training and evaluating, respectively. This research studied the performance of different neural networks in order to predict precipitation and then presented an algorithm for combining neural networks with linear and nonlinear methods. After modeling and comparing their results using neural networks, the root mean square error was recorded for each method. In the first modeling, the artificial neural network error was 0.05, in the second modeling, linear combination of neural networks error was 0.07, and in the third model, nonlinear combination neural networks error was 0.001. Reducing the error of forecasting precipitation has always been one of the goals of the researchers. This study, with the forecast of precipitation by neural networks, suggested that the use of a more robust method called a nonlinear combination neural network can lead to improve men is in for cast diagnostic accuracy.

One of the atmospheric hazards that seriously affect human life and health is the occurrence of thermal tensions and stress in the form of heat and cold waves. Iran is one of the areas of the planet that has climate variability due to its geographical characteristics; therefore, consequently, its different regions are not immune to heat and cold waves. On the other hand, Iran's climate variability is the factor causing the difference between thresholds of heat and cold wave occurrence for its different regions. Therefore, in this study, based on three different thresholds, spatial analysis of the frequency of occurrence of heat and cold waves has been done. Thus in this work, using average daily data from 1960 to 2014, PET (Physiological Equivalent Temperature) was used to monitor heat and cold waves of four stations in Iran. Results of this study showed that in the context of global warming, although significant differences in the frequency of cold waves cannot be seen, these changes are significant and increasing for the frequency of occurrence of heat waves of selected station.

A knowledge of cloud properties and spatiotemporal variations of clouds is especially crucial to understand the radiative forcing of climate. This research aims to study cloudiness in Iran using the most recent satellite data, powerful databases, and regional and seasonal analyses. In this study, three data series were used for the spatiotemporal variations of cloudiness in the country: A) Cloudiness data of 42 synoptic stations in the country during the statistical period from 1970 to 2005, B) Cloud Optical Thickness (COT) of Terra and Aqua MODIS sensors for 2003-2015, and C) Total Cloud Cover (TCC) of ECMWF Database, ERA-Interim version, for 1979-2015. The values obtained in the country were located via the kriging geostatistical method by RMSE. The results showed that the highest TCC occurs during the winter months. At this time of the year, the cloud cover is reduced from North to South and from West to East. Besides, COT showed that in the cold months of the year, the highest COT is observed in January and the lowest in March. The west and central Zagros highlands have the highest COT. Incorporating COT and TCC results showed that the two factors of height and approximation and access to moisture sources contribute significantly to the regional differences of cloudiness in Iran.

A good understanding of the diverse mechanisms in the atmosphere is required in modelling the climate. In this study, the diurnal and seasonal patterns of all-sky downward longwave radiation (DLR) at Ilorin (8o 32l N, 4o 34l E), Nigeria were investigated alongside relative humidity (RH) and temperature. The average diurnal pattern of DLR gives an arc that begins by increasing gradually with some inherent fluctuations from 01:00 hour to a maximum at 14:00 hour local time, and then gradually decreases to a minimum at 00:00 hour. However, sometimes erratic and double peak arc diurnal DLR patterns occur around the mid and the end of the year periods respectively. The seasonal, diurnal peak of temperature and the minimum of relative humidity (RH) occur approximately two hours after the peak of DLR. Besides, the seasonal trends of both DLR and RH match each other, except sometimes in June, which could be due to the midyear DLR erratic diurnal effect. Possibly, the mechanisms of the inter-tropical discontinuity (ITD) influence the particular diurnal patterns of DLR at the midyear and year-end periods. Moreover, monthly dispersion of DLR is high during known months of high atmospheric aerosols, and annual maximum temperature occurs after the Harmattan season. Hence, variations in DLR are influenced mostly by mineral dust in the atmosphere, mechanisms of ITD and changes in the sun-earth distance, which reflects on the different seasons in Ilorin.