The availability of a large amount of the data recorded by the Iranian Seismic Telemetry Network (ISTN) has motivated this study to develop relations for the routine determination of ML scale for Central Alborz region of northern Iran. The ML is commonly used in engineering because it is determined within the frequency range (0.5-3 sec) of interest in most of such applications. For any comprehensive seismic hazard analysis, one needs a calibrated magnitude relationship as well as an earthquake catalog for the study region. It is a well-known fact that the regional geology has a great influence on magnitude relations. Therefore, for each seismic region a specific magnitude relation has to be developed. The ML scale is based on the arithmetic mean of horizontal components of the synthesized Wood–Anderson seismograms. We used both nonparametric and parametric methods for inversion. We used a large dataset of 3886 events including 62031 waveforms which recorded by Tehran, Semnan and Sari seismic networks during 02/03/1997 to 13/03/2011. These seismic networks comprise of 19 three-component stations. We calculated the associated synthesized Wood-Anderson seismogram for each SS-1 waveform which records the velocity. Based on Richter’s method, we used amplitudes which are arithmetic means of those of horizontal components.Richter’s ML formula first developed for southern California and Savage and Anderson introduced a nonparametric least-squares inversion method which has been used by others. In this method, the amplitudes recorded at arbitrary distances are linearly interpolated to yield values for the attenuation curve at some fixed distances. In this study, we used both methods.The resulting equations are -logA0=0.9819log (r/100)+0.0028 (r-100)+3.0 and-logA0=1.076log (r)+0.0029 (r)+0.5580 from parametric and non-parametric methods, respectively. Where r is hypocentral in kilometer and A0 is amplitude in millimeter. The two methods yielded very similar results. Unlike the parametric method, the nonparametric one does not impose any a priori assumption of the shape of the attenuation curve on the data and has the potential to detect hinges in the attenuation curve that are caused by structural boundaries such as Moho or geological variations affects on the attenuation curve. Thus the result obtained by nonparametric method was chosen as the final result.Bakun and Joyner (1984) give the following formula for the Q/f ratio: taking an average S-wave crustal velocity of VS=3.3 km/sec, the k value obtained by the non-parametric method, 0.0029, would imply a Q/f ratio of 150 in Central Alborz, Iran.

We useWe use ML shear wave velocity to derive a high resolution 2D ML shear wave velocity map for the Iranian Plateau. The ML amplitudes and arrival times are routinely measured for the calculation of local magnitude. ML shear wave velocity is very sensitive to the lateral change of crustal thickness and switches between the velocity of Lg and Sn waves. An Lg wave will die out as soon as encounter a sudden crustal change in favor of formation of mantle Sn wave. The collected data base is consisted of 56152 ML velocity belong to 2943 precisely relocated events happened during 1996 to 2012. The arrival time of ML amplitudes were read from waveforms of permanent and temporary networks in Iran. Using the arrival time of an ML amplitude and its ray length, we calculate average shear wave velocity for each ray. The selected events are consisted of 63 clusters with epicentral location uncertainty of 5 km or less. The cluster approach adopted in this work allows us to easily calculate empirical velocity error for each summary ray connecting a given observing station to the corresponding cluster. This also reduces drastically the number of initial 56152 rays to just 3107 summary rays and thus significantly reduces the required computation time for the seismic tomography. Except for the Makran region, the summary rays provide a good coverage for most of Iran. Using a constrained direct damped weighted least square inversion scheme, we inverted the ML velocity for a 2D ML shear wave velocity map of Iran along with its cluster and station correction terms. In our tomography, we constrained the velocity of each cell based on the azimuthal coverage of the hitting rays. The input average velocity for each ML ray was also weighted based on its empirical reading spread. The computed ML shear velocity varies mostly between 2.9 and 3.6 km/s, so suggesting that the majority of the rays are indeed Lg rays. The map shows a general similarity with previous maps of Pn velocity indicating that ML shear wave velocity is strongly affected by lateral changes of crustal thickness and upper mantle velocity. Our results show that Caspian Basin, and Zagros regions are Lg blocking regions. We speculate that the blockage of Lg wave in Zagros is related to strong lateral crustal thickness changes caused by the orogenic processes. We also noted that the shear wave velocity border between the Zagros and Central Iran is considerably deviating from Zagros suture line indicating a partial underthrusting of the cold Arabian plate beneath the Central Iran. The Lg blockage in South Caspian basin is either related to its postulated oceanic type crust and/or strong lateral change in its crustal thickness. East of the Caspian Sea shows high velocities likewise its interior, implying the low plain is underlane by either an oceanic type crust or a transitional crust with large lateral variations of crustal thickness. The ML velocity map also shows a velocity in the range of Lg velocity for the Lut block and thus implying a continental nature for the unknown Lut block. Alborz, most of the Central Iran and especially the northwestern Iran show rather low Lg velocities suggesting a warm continental crust.

In this manuscript we suggest a fast adaptive distributed method for maximum likelihood approximation (MLA) in multiple view object localization problem. For this purpose, we use "up to scale" property of projective geometry and by defining coefficients for convergence criterion, we increase the convergence speed of the consensus algorithm. We try to present a mathematical model for the problem. We use two types of error function. The proposed method uses maximum likelihood for obtaining its best parameters. Our approach utilizes "up to scale" property in projective geometry to reach the consensus quickly. The difference between nodes' values and meanwhile consensus values are evaluated by two error functions. To estimate consensus value in the second error function, we used local weighted average of each node. At the last of the paper, we prove our claims by experimental results.

1-Introduction For the first time in the modern history of seismology, Richter, (1935) invented a local magnitude scale (ML) for earthquakes for region of California. This scale is appropriate for estimation of magnitude of a wide range of earthquakes. Few investigations were performed about ML scale for earthquakes in Iran in different areas and using different databases. Shoja-Taheri, et al. (2007), Askari et al. (2009) and Nemati et al. (2014) are the examples from ML estimation in Iran. Shoja-Taheri, et al. (2007) used the strong motion data of NE Iran, Askari et al. (2009) used amplitude of short period seismograms in north of Iran and Nemati et al. (2014) used amplitude of waveforms of a local network data in eastern Alborz to calculate a local scale for magnitude of earthquakes in Iran. 2-Methodology ML of the earthquakes is calculated using averaging between the maximum amplitude of shear waves in the horizontal components of the waveforms of the earthquakes. Equation of ML is a logarithmic and parametric relationship. ML equation parameters should be determined for each seismic area. Estimation of a local magnitude scale is necessary for precisely estimation of magnitude of earthquakes in every seismic area. For calculation of ML for the earthquake of a specific area, it is better that the parameters should be estimated using the earthquake data of the same region. In this research the ML equation is determined for entire Iran using Iranian earthquakes, for the first time. Some researchers divide Iran into distinct seismotectonic regions, in which they calculate ML for each area, separately. In this paper ML is calibrated for entire Iran, because ray path of an earthquake in a specific region may cross the neighboring provinces. The chosen earthquakes for ML determination should be the most precisely located earthquakes, because the epicentral distance is so important in the processing. For this purpose, 1409 synthetic Wood-Anderson amplitude of 229 earthquakes occurred in 24 – 42 ° N and 43 – 65 ° E with magnitude of 3. 5-5. 4, azimuthal gap less than 180° and RMS less than 0. 5 were used. These earthquakes were recorded by seismological network of International Institute of Earthquake Engineering and Seismology of Iran between 2004 and 2016. The processing was done using Matlab software and arranging a big matrix composed of the epicentral distances, amplitudes and their functions. The amplitudes have been read using Seisan software on the horizontal components by automatic picking. In this stage, the amplitudes (velocity) are changed to the Wood-Anderson torsion seismograph scale (displacement). After reading, huge number of numbers (amplitudes, distances, earthquake coordinates, … ) form the mentioned matrix equation. 3-Results and discussion After the processing and inverting thematrix equation, using a parametric equation, in which geometrical spreading and inelastic attenuation were supposed, the attenuation equation for local magnitude in Iran were estimated: Inelastic attenuation is related to microscopic incompletions in mineralogical structure of the minerals, existence of water or the other fluids in porosities of the rocks, discontinuities in earthcrust, friction and transforming the wave momentum energy into temperature in wave path. In this relation, 100 km for epicentral distance of the earthquakes and the constant number (3. 0) for magnitude of earthquakes were put, because of Richter primary conditions for calibration of the ML equation in 1935. The station corrections (Sj) were obtained for all of the stations representing overestimation and underestimation of ML for correction the output magnitudes. The abovementioned numbers were put, because it is supposed that an earthquake with the magnitude of 3. 0 produces 1 mm amplitude in a seismograph, which is installed in a distance of 100 km. This equation suggests more attenuation for wave amplitude for distances more than 150 km in comparison to the previously estimation of Hutton and Boore, (1987) equation. 4-Conclusion 1) It could be concluded from this paper that wave amplitude attenuation decreases with distance in order of 1/r1. 0928 and geological site effects in Iran on magnitude estimation is in a broad range of 0. 7. 2) One of the preferences of this work (determination of ML for the entire Iran) in comparison to the other works (determination of ML for the seismotectonic provinces of Iran, individually) is that the ray path of a regional earthquake always cut more than two seismotectonic provinces. 3) This relation is useful for the governmental institutions in Iran that calculates the ML, like International Institute of Earthquake Engineering and Seismology of Iran. 4) Obtained station corrections were estimated between-0. 198-0. 44 magnitude unit.

Due to absence of an appropriate scale for estimation of ML for the earthquakes in eastern Alborz Range, we calculated 1113 synthetic Wood-Anderson peak amplitudes from waveforms of 215 earthquakes recorded by 23 stations at local hypocentral distances. The events were recorded by two local temporary seismological networks installed during 2007 and 2008 by the Geological Survey of Iran (GSI) and the stations of the permanent network of the Institute of Geophysics of University of Tehran (IGUT). Both temporary networks were installed for ML two discontinuous periods of nine months in the eastern- middle Alborz. In order to estimate an empirical attenuation curve for ML amplitudes, A, read from the stations at very short hypocentral distances, we fit a parametric relationship to the peak amplitude readings while considering geometrical spreading, intrinsic attenuation and stations corrections. We obtained the following empirical attenuation relationship:log Aij =1.986 log [rij/100]-0.00452 (Rij-100) -3+SjWhere R  is hypocentral distance in km between the jth station pair and ith earthquake and Sj is value of station correction for the jth station The realtionship clearly indicates a larger attenuation for shear waves in short hypocentral distances below 20 km. Our new ML relationship implies ML that using ML relationship derived for hypocentral distances larger than 50 km would overestimate ML magnitude of events recorded by our local networks by about half of unit magnitude. Thus we suggest that for local networks in other regions lacking any local ML relationship, ML relationship derived in this study to be used.