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Investigation of historical and instrumental seismicity and fault kinematics of major faults are used to deduce the stress state in the northeast of Greater Tehran. In the present study, we have identified the Mw 5.1 earthquake and its related aftershocks in northeast Tehran that occurred on May 7, 2020. In this regard, after combining the waveforms of seismograms recorded in the seismic stations of Tehran and neighbouring provinces, the location, magnitude, and exact time of occurrence of the main shock and its six aftershocks have been calculated. Then, using three methods, including waveform modelling, P wave polarity and the ratio of P and S wave amplitudes, the focal mechanism of the fault causing seismic events is estimated. Fault kinematic study and the epicenter of the seismic event and related aftershocks suggest that the Mosha fault could be responsible for the event. Furthermore, the regional tectonic stress field has been calculated by focal mechanism inversion. Comparisons between stress field orientations and stress ratio provide new information on the local stress field. The variation of the stress ratio in the lower and upper crust is considerably high, demonstrating an inhomogeneity of deformation related to the Mosha fault. [ABSTRACT FROM AUTHOR]

To investigate the crustal structure of the northeast of Lut block and Eastern Iranian Ranges, we deployed 31 seismic stations along a 230 km profile for a period of approximately nine months. Using the migration of P-wave receiver functions, we analyzed changes in the Moho depth along this profile. Our results indicate that the boundary between the crust and upper mantle is nearly flat, with a depth of approximately 40 km. Furthermore, we found that the thickness of the crust is relatively high beneath the Lut-Afghan Block boundaries and Bagheran Mountain, where receiver functions suggest the presence of more complex structures including inclined interfaces. Our findings suggest that the collision of Lut-Afghan blocks has not significantly increased the crustal thickness in the northeast Iran compared to the Zagros collision zone in the southwest of Iran. Rather, we propose that the observed shortening in this region is primarily due to the interaction of strike-slip motions, which can accommodate shortening through mechanisms such as rotation and wedge escaping. [ABSTRACT FROM AUTHOR]

Increasing our understanding of the earth's layering characteristics at an engineering scale is crucial for the optimal design of tall buildings, important industrial facilities, and lifelines infrastructures. The most important characteristics that can be measured by the seismic refraction method are the speed of longitudinal and transverse seismic waves. In addition, determining the thickness of layers up to depth of 150 m is another capability of this method. In this research, the classical refraction seismic method has been compared with methods based on artificial intelligence techniques with emphasis on two types of fully connected and convolution techniques. The results of this research show that by replacing the neural network that fits the characteristics of the subsurface layers instead of using classical inversion methods, the accuracy of classical inversion methods can be achieved in much less time. Fully connected and convolutional neural networks are highly capable for identifying geological structures whose measurement data is contaminated with noise, with acceptable accuracy without pre-processing. Therefore, the proposed method, in addition to the ability to detect the arrival time of seismic phases in noisy signals and the time-consuming process of manual processing, is likely to be useful for identifying complex geological formations. [ABSTRACT FROM AUTHOR]