IRANIAN JOURNAL OF GEOPHYSICS
Year:2009 | Volume:3 | Issue:2|Papers Count:7

Summary:Reliable determination of earthquake moment magnitude is a fundamental problem of seismic hazard assessment. Short period data does not generally extend to sufficiently low frequencies to allow for the reliable calculation of moment tensor. In the Tehran region, the available seismic data are mostly short-period records. To resolve this difficulty, following Motazedian and Atkinson (2005), the current study uses M1 magnitude, which closely follows moment magnitude for small to moderate events. In addition, using this special magnitude scale aids in overcoming the difficulty with the different magnitude scales in the catalogue of the Tehran region. The M1 magnitude scale is obtained from the spectral amplitude at 1 Hz. The Eigene-frequency of short period seismometers is always close to 1 Hz, thusM1 can be determined using data from short-period seismometers. Assuming a Brune point-source model, M1 magnitude is equal to the moment magnitude.To calculate M1 magnitude for a given event, the Fourier power spectrum of the S-window of each observed transverse component waveform is calculated. It is then corrected for geometrical and intrinsic attenuation using the attenuation relationship of Mottaghi (2007), developed for the Tehran region, and the applied to a Butterworth filter centered on 1 Hz to calculate spectrum amplitude at 1 Hz, A0(1). Next, using a trial and error method, M1 is calculated by fitting the A0(1) to the corresponding synthetic Brune spectrum amplitudes. The fit is done over a frequency band of 0.4 to 3 Hz while the band is divided to equal logarithmic bins. A shear velocity of 3.73 km/s has been assumed to calculate the synthetic Brune spectrum, along with an average density of g/cm3 and a stress drop of 10 MPa. The fit is strongly sensitive to 0.1 magnitude unit fluctuations. Magnitude of an event is defined by the average of the calculated values of M1 over all stations that recorded the event.Since 1995, the seismicity of the Tehran region has been monitored by Iran Telemetered Seismograph Network (ITSN), a series of small regional subnetworks operated by the Institute of Geophysics, University of Tehran. A review of the operation of the Tehran subnetwork of the ITSN has been provided by Ghods and Sobouti (2005).The data used in this study are1804 records of 179 earthquakes having magnitudes larger than 3 and occurring in the period of 1996-2004. To ensure reasonable location accuracy and lower sensitivity on radiation pattern, the selected events have an azimuthal gap lower than 250 degree. The events were selected to provide relatively homogenous ray coverage inside the study area [Figure 1]. The selected records have hypocentral distribution in the range of 10 to 440 km with rather dense coverage of hypocentral distance of 350 km (Figure 2). The result of this study is a catalogue of 179 events with magnitude M1 greater than 3 which occurred in the Tehran region. Variations of M1 residues versus epicentral distance show insignificant dependency with distance [Figure 5], implying that the attenuation relationship used in this study (Motaghi, 2007) is appropriate for the Tehran region. A comparison of calculated M1 catalog with the corresponding ML catalog (Figure 4) shows a systematic difference between these two scales following a relationship of M1=0.76 ML+0.92. The fit is computed using Least Absolute Residual algorithm and has a RMS of 0.111.

Summary:The interaction of the solar wind with the Earth’s magnetic field gives rise to a number of important phenomena. These include reconnection between the solar wind magnetic field and geomagnetic field lines, reconnection in the magnetotail, plasma convection in the magnetosphere/ionosphere, generation of a field-aligned current system, and energetic particle injection. A wide range of physical process is involved in the solar windmagnetosphere system, and consequently a wide array of methods has been used to study them, ranging from detailed studies of selected phenomena with the assumption of a specific field geometry or boundary condition, to fully three-dimensional simulation with a Magneto-hydrodynamics (MHD) assumption.Almost fifty years ago, was proven that the magnetic field lines could be broken and then reconnected with other magnetic lines, a process that is called magnetic reconnection. After that time, the concept of magnetic reconnection found important applications in space studies to provide the safety conditions for satellites and spacecrafts for the first time in 1961 this concept was applied to the geomagnetosphere and used to express the basic model for magnetospheric convection that happens on magnetopause and magnetotail during magnetic reconnection. Also in 1964, the first quantitative model of magnetic reconnection regarding solar flares was developed and introduced in a model that could describe the fast rate of energy release in solar ejections. The conventional definition of magnetic reconnection indicates that the phenomena occurs on the electric field parallel with the X-Line, however, for many reasons there has been a problem in modeling Magnetohydrodynamic simulations: It is possible that determining the X-Line accurately in irregular magnetopause is very difficult or impossible due to the lack of an unknown real resistivity model, which prevents the establishment of a reliable nonconvectional electric field.The simulation of MHD was performed in a Geocentric Solar Ecliptic coordinate system and with magneto- hydrodynamics equations in steady state form to quantify interaction between solar wind and magnetosphere. Simulation space included an area with dimensions of 90´60´60 times of earth radius (Re), for which the spatial and time steps for modeling were chosen as 0.5 Re and 0.937 s, respectively. Cells which construct the model for simulation were 326000 in number. A magnetic dipole in the origin and electrostatic equations for ionosphere were considered.To investigate the effect of resistivity on the place and time of magnetic reconnection, simulation was executed with consideration of different resistivity values. The results of the performed simulations showed that the place and time of magnetic reconnection depend largely on the variations of resistivity selected for the modeling, especially at the level of 0.0001 Ohm-m. Figures plotted from the simulation results formed with resistivities of less than 0.00001 and greater than 0.01 Ohm-m depicted the shape of the magnetosphere such that interaction between solar wind and the magnetosphere field appear not to have occurred or created any reconnectionGenerally speaking, this study applied the global MHD simulation to describe the magnetic reconnection. It has been proposed that the global convection related to magnetic reconnection can be determined by a calculation the energy, mass, or magnetic field transfer on open– closed field line boundaries based on different resistivities models.This study also examined the different values of specific resistivity for running simulations and determined the optimum model under known initial conditions.

Summary:One important goal in the interpretation of magnetic data is to determine the type and location of the magnetic source. This has recently become particularly important due to the acquisition of large volumes of magnetic data both in environmental and geologicalapplications. Interpretation of the magnetic data involves determining the parameters that characterize the source of the observed anomaly. In this regard, depth to the top of the source is the most important parameter. To this end, there are generally two different approaches, namely the manual and automatic methods. Manual methods, as implied in the name need simple tools such as rulers, calculators and are commonly used in processing 2-D datasets or profiles. Additionally, these methods can be performed in the field, allowing the user to distinguish the noises from signals without recourse to the computer (due to their simplicity). However, because of the large amount of magnetic data that are being collected in the field of geology, using of more rapid and powerful methods are necessary. In contrast to manual methods, automatic methods have the ability to perform in both 2-D and 3-D datasets more rapidly and with at least as much precision.A large number of automatic methods exist for interpreting magnetic data. These methods can be applied to profile data (Hartman, 1971; Naudy, 1971; Nabighian, 1972; Jian, 1976; Thompson, 1982; Atchuta Rao et al.). There are numerous methods that work on grided data, including 3-D Euler deconvolution (Reid et al., 1990), the 3-D analytic signal (Roest et al., 1992) and the enhanced analytic signal technique (Hsu et al., 1996). The results of these methods are usually displayed by plotting a symbol superimposed in the magnetic map in the source location. Consequently, the source boundary (horizontal location) must be evaluated by common edge detection methods, namely, zero crossing of the second-order vertical derivative or maximum value of the analytic signal prior to running a depth estimation method. In general, all automatic methods use derivatives of the magnetic data which is computed either in the space domain by the finite difference method or in the frequency domain by the Fast Fourier (FF) technique. Then, an appropriate equation is developed for depth estimation starting from a simple geometry model, such as sphere, dyke, or horizontal cylinder. The depth to top of the body measure is assessed by solving this equation either in the space or frequency domain. The proposed method extends the theory of the complex analytic signal by computing three complex attributes including instantaneous amplitude of the analytic signal, instantaneous phase and instantaneous frequency. It must be noted that the instantaneous concept is applied in the analysis of the temporal series (time dependent dataset) and because the magnetic data are spatial, analogous to temporal, we use the term local instead of instantaneous. These three quantities are obtained as shown below:|A|=Ö[¶M/¶x]2+[¶M/¶z]2,q=tan-1(¶M/¶z/¶M/¶x),f=1/2p¶/¶xtan-1[¶M/¶z/¶M/¶x]where, A, q and f are amplitude, phase and frequency, respectively. Phase variation of potential field data can be used as an interpretation method. This idea appears in edge detection with tilt angle or phase angle. The advantages using of this quantity include its independence of body magnetization direction and its ease of computation. In this paper variations of this quantity, termed local frequency, are used for source parameter estimation, such as body depth and susceptibility. This method has been applied on the synthetic magnetic data from a vertical cylinder in both noiseless and noisy data. The presence of the noise causes the estimated depth to differ from the actual body depth; therefore, in practice, the noise should be removed by the upward continuation technique. This method was also applied on real magnetic data from Anomaly No.2 in the Gol-Gohar mining area. In order to remove the superficial noise, the magnetic anomaly wascontinued to 12.5m elevation. Using this method, it was found that causative body depth varies from 40 to 120 meters in different locations, which has broad correlation with explorative drilling results.

Summary:AN-EUL is a new automatic method for the simultaneous approximation of depth, geometry and location of magnetic sources. The principle advantage of this method is its combining both the analytic signal and the Euler Deconvolution methods. It is by substituting the appropriate derivatives of Euler homogeneous equations for the expression of the analytic signal of the potential field in the main equations of this method that the advantage is gained. In this method, the determination of the source location is based on the position of the maximum value of the analytic signal amplitude. For 3D sources, the maximum value of the amplitude of analytic signals (AAS) is not always located directly over the body, and the shape of AAS is dependent on the magnetization direction. Therefore, there are some errors in the determination of location based on the maximum value of AAS for these types of sources. Consequently, the calculation of the depth and structural indices, also have errors when using the maximum value of AAS. By using the reduction to the pole and pseudo gravity transform of the magnetic anomaly, the approximation of the source location and, consequently, the calculation of the depth and structural indices of the source gain high accuracy. In this paper, in order to compare results, the AN-EUL method has been applied to magnetic data, reduced to the pole data and pseudo gravity data due to the magnetic sphere. The results show that the calculations have greater accuracy for reduced to the pole data and pseudo gravity data than for magnetic data. Finally, this method is applied to a series of real magnetic data. By using the reduction to the pole and pseudo gravity filters, the reduced to the pole and pseudo gravity anomalies are calculated. Subsequently, the ANEUL method is applied to these data, and the results are compared with each other. All of the steps in this paper are performed by using a written code in MATLAB.

Summary In the present paper, the microphysical processes of aerosol particles are investigated using the new coupled system of the aerosol HAM model and the Weather Research and Forecasting (WRF) modeling system. The WRF-HAM model uses a new “pseudomodal” approach for the representation of the aerosol size distribution. The aerosol population in this model is represented by the superposition of seven modes, assuming a lognormal distribution within each mode. The seven modes are grouped into four geometrical size classes, ranging from the nucleation, Aitken, and accumulation modes to coarse modes. Besides size, two categories of particles are distinguished, internally mixed and water soluble particles (four modes), and externally mixed and insoluble particles (three modes). This classification allows prediction of the hygroscopic properties of initially insoluble aerosol compounds, which controls their atmospheric lifetimes and also their interactions with clouds.The WRF-HAM model includes the various microphysical processes of condensation of sulfuric acid, nucleation and new particle formation, coagulation of aerosol particles, and the thermodynamical equilibrium of aerosols with the water vapor. Gravitational sedimentation and dry deposition are considered the main removal processes for the aerosol particles in this coupled WRF-HAM model. The model also contains the in-cloud scavenging of aerosol particles by precipitation within convective cumulus clouds. The main global aerosol compounds including sulfate, black carbon, particulate organic carbon (POM), sea salt and mineral dust have been considered in this study. The emission fluxes of different aerosol compounds are prescribed based on the Emission Inventory for the Aerosol Model Inter-comparison Experiment B, AEROCOM representative for the year 2000.The model simulations were conducted for a 24-hour period from 22 to 23 February 2006 in a domain with 30 km horizontal grid spacing, encompassing south-western Asia, North Africa and some parts of Europe. The model simulation results were investigated for different microphysical processes of condensation, nucleation and coagulation. For better understanding of the treatment of different microphysical processes in this study, all aerosol particles were assumed to have been emitted into the atmosphere only during the first time step.The model simulation results show that the sulfuric acid concentration consumed by new particle formation in the nucleation mode is higher than that by condensation on aerosol particles. In addition, the secondary sulfate aerosol in nucleation mode formed at high altitudes in the cloudy regions of the domain. It has also been concluded that aerosol particles in both insoluble and mixed modes grow by condensation of sulfuric acid. Moreover, it is shown that the condensation of gaseous sulfate on aerosol particles causes the transfer of particles in insoluble modes to the corresponding mixed modes. Thus, the mass of different aerosol compounds in insoluble modes decreases after condensation of sulfuric acid over their surfaces, while that of the aerosol particles in mixed modes increases significantly due to both condensation of gaseous sulfate and transfer of condensed particles in insoluble modes. The coagulation process of aerosol particles was further observed to modify the aerosol mass within the modes and transfers the coagulating particles to the coarser modes. The aerosol mass modification by coagulation is smaller than that by the condensation process.

Summary:Potential field data (gravity and magnetic data) are usually analyzed by employing linear transformations, the spectral method, inversion techniques and analytic signal methods. Nowadays, there are different methods of modeling the gravity data; but each has limitations. One of the limitations of these methods is the assumption of a simple shape for buried structures whereas the actual shape could be entirely different. This study uses cubic units (3D model) to solve this limitation because affords the ability to make any shape for unknown underground structures by arranging these cubics.In this paper, a new method called Forced Neural Networks (FNN) to find the density variation of buried deposits or underground structures in different depth sections by assuming the cubic model is described. The aim of the geological modeling is to determine the shape and location of underground structures in 3-D sections. Here, one neuron network and back propagation algorithm are applied to discover the density difference. In this method, weights of the neurons are assigned as density for each cubic and the activation function has a linear property such that the outputs are the same as the inputs. After using the back propagation, densities for each cubic are updated and the output of the neurons gives the gravity anomaly. Hence, the density differences are found. However, the results of this system are insufficient because non-uniqueness and horizontal locations are constrained; therefore, the value of density difference is set to zero if its value is very close to zero according to the density difference which is obtained from geological features of the region. Otherwise these values are set to the density difference of the geological region after back propagation.Using a forced neural network, after sufficient epoch is applied, fixed values are assigned to the output of the neuron according to the density difference, and this process is continued until the mean square error of the output becomes sufficiently small. The method is used for both noise-free and noise-corrupted synthetic data and, after obtaining satisfactory results for three synthetic data models, this method was used for modeling of the real data.The Dehloran Bitumen map in Iran was chosen as a real data application. The area under consideration is located in the Zagros tectonic zone, west of Iran where we are looking for Bitumen. Layers of Medium-bedded limestone with intermediate marllimestone are the dominant formations in the area and the hydrocarbon zone is one of the most important characteristics of the area. A program was written using the Anomaly modeling method. The final result of this method shows that the deposit begins from the low depth to approximately less than 40 meters. This modeling yeilded satisfactory results for the drilling in the region. The results of the drillings show that the lowest depth of the deposit varies from 7 to 10 meters. This method can easily be applied for gravity, microgravity and magnetic data especially for porphyry deposits.

Summary:Land surface parameterization schemes estimate the exchanges of momentum, mass and energy between land surface and the atmosphere. Runoff is one of the important components of the land surface water balance. Parameterization of runoff is difficult because of its dependence on rainfall, soil moisture and topography which vary greatly across time and space. A coarse-resolution land surface scheme cannot explicitly model the complexities of runoff generation in the model grid square. Instead, it aims to represent the major processes via sub-grid parameterizations. A popular solution involves the use of probability distribution functions to represent sub-grid variability.In this paper, the river discharge in three sub-basins of the Karoon river catchment (Farsiat, Harmaleh and Soosan) simulated by the OSU land surface scheme is compared to that simulated by the coupled OSU-SIMTOP (OSU-SIM) model. The OSU land surface scheme parameterizes runoff based on the probability distribution function (pdf) of the soil infiltration, while the coupled OSU-SIM uses the pdf of the topographic characteristics to model runoff. The two models were run off-line using the atmospheric forcing derived from the Weather Research and Forecasting (WRF) model for the 2- month period of 1 December 2005 to 31 January 2006. The simulations were conducted with a 5´5 km grid spacing over a domain having 106´115 grid points along altitude and longitude, respectively, and centered at 50oE and 32oN. The models were calibrated during December 2005, and the simulation results for January 2006 were used to intercompare the models and evaluate their simulations against the observed river discharge at the Farsiat, Harmaleh and Soosan hydrometric stations. The results show that, compared to OSU, the OSU-SIM model had higher efficiency, a smaller mean absolute error (MAE) and lower bias in simulating river discharge in all of the three sub-basins. The higher correlation coefficient between the simulated and observed river discharge and closer to 1 normalized standard deviation of the simulated runoff suggest the superiority of OSUSIM to OSU in all of the three sub-basins for the evaluation period. The lower skill of the OSU in predicting runoff may be attributed to errors of the WRF model in the rainfall prediction, error in the rainfall-runoff relationship of the OSU, or inaccuracy in the surface parameters, especially those related to the pdf of the soil infiltration, used for the simulations. The comparison between the observed river discharge and that simulated by the OSU model shows the error in the initial conditions, especially those initial conditions of surface water and ground water storage, could also be another source of the error in the simulated discharge. Results also suggest that the performance of OSU-SIM is sensitive to the horizontal resolution of the model. Using low-resolution digital elevation data for calculating topographic index decreases the efficiency of the model and increases the mean absolute error of the OSU-SIM land surface scheme.