Geophysics
Afsaneh Nasrabadi; Fateme Azimi; MohammadReza Sepahvand
Abstract
Crustal velocity structure and Moho discontinuity depth have investigated beneath 7 the broadband seismic stations, AFRZ, TKDS, TPRV, TNSJ, ANAR, KRSH of the Iranian Seismological Center (ISC) and YZKH of Iranian National Seismic Network (INSN) located in the center of Iran by joint inversion of receiver ...
Read More
Crustal velocity structure and Moho discontinuity depth have investigated beneath 7 the broadband seismic stations, AFRZ, TKDS, TPRV, TNSJ, ANAR, KRSH of the Iranian Seismological Center (ISC) and YZKH of Iranian National Seismic Network (INSN) located in the center of Iran by joint inversion of receiver functions and Rayleigh waves group velocity dispersion. Three years (2012 to 2014) teleseismic waveforms (with epicentral distance 25o-90o) for computation receiver functions by iterative approach in time domain have been processed. The Rayleigh waves group velocity dispersion curves were incorporated into our joint inversion scheme from an independent surface wave tomography study. Receiver function is response of local structure of ground (located beneath the three–component broadband seismic station) to teleseismic P-wave, that is sensitive to seismic discontinuities. Since there is very little absolute-velocity information contained in the receiver function, its inversion for shear-wave velocity structure is non-unique (velocity-depth trade-off). On the other hand, dispersion curves are sensitive to the average velocity structure of the upper layers rather than to seismic discontinuities. So the non-uniqueness problem can be solved by combining receiver function inversion with surface-wave dispersion. Results from joint inversion in center of Iran indicates that Moho discontinuity depth depth beneath AFRZ, TKDS and TPRV stations is 40 Km, beneath TKDS 42 Km, beneath ANAR is 38 Km and beneath KRSH and YZKH stations are 44 Km. It was shown that the joint inversion method can cause ±2 kilometers of error. The average Moho depth is about 42±2 kilometers beneath center of Iran.
Mohammad Tatar; M. Tatar; A. Kaviani
Abstract
Crustal structure of the Iranian plateau which is located between two convergent Arabian and Eurasian plates is studied. Teleseismic earthquakes recorded by broad band stations of Iranian National Seismic Network (INSN) are used to compute the receiver functions for each station. Rayleigh wave ...
Read More
Crustal structure of the Iranian plateau which is located between two convergent Arabian and Eurasian plates is studied. Teleseismic earthquakes recorded by broad band stations of Iranian National Seismic Network (INSN) are used to compute the receiver functions for each station. Rayleigh wave phase velocity dispersion curves were estimated employing two-station methods for all possible station pair of the above mentioned seismic network. A combined inversion of Rayleigh wave phase velocities and body wave receiver functions increases the uniqueness of the solution over separate inversions and also facilitates explicit parameterization of layer thickness in the model space. Our result indicates the crustal thickness differs from a minimum of 40 ±2 km in southeast of Iran, (ZHSF) to a maximum of 56 ±2km beneath the Sanandaj-Sirjan zone (SNGE). We observe a crustal thickness of 47 ±2km beneath the central Zagros (GHIR) to 52 ±2km below the eastern most of Zagros (BNDS), then to 47 ±2km beneath the northwestern part of the Zagros (SHGR). Crust of the Central Iran (KRBR) has a thickness of 48 ±2 km while the average Moho depth in southern parts of the Central Alborz (DAMV and THKV stations) is 54±2km. Our analysis shows a thinning of the crust to 43 ±2 km beneath the northwest of Iran (MAKO) and western part of the Caspian basin (GRMI).
A. Moradzadeh; F. Tahmasbi; M. Fateh
Abstract
The magnetotelluric (MT) method is a natural source electromagnetic geophysical technique, which is used mainly in petroleum, mineral and geothermal exploration. As in this method, the quantity of the measured data is bulky and have a complex structure, their modeling, ...
Read More
The magnetotelluric (MT) method is a natural source electromagnetic geophysical technique, which is used mainly in petroleum, mineral and geothermal exploration. As in this method, the quantity of the measured data is bulky and have a complex structure, their modeling, compared with the modeling of the other electrical data, is a very complex task or even impossible in some instances.
The main objective of this paper is to use the ability of the artificial neural networks (ANN) to find a solution for two-dimensional (2D) joint TE (transverse electric) and TM (transverse magnetic) modes inverse modeling of MT data. To achieve the goal, a multilayer perceptron (MLP) network with back propagation (BP) learning algorithm is used. In order to learn the designed network, many synthetic 2D models with the same category, have been created and their responses have been calculated for each polarization mode by forward modeling. Synthetic data include apparent resistivity and impedance phase in 9 stations and 11 frequencies in two polarization modes. After a comprehensive study, a perceptron with 3 layers and architecture of 396-9-9 has been designed and used to model the data.
This study show that the designed network is capable enough to produce an acceptable 2D underground model so that the correspondence mean relative modeling error is 3.9% and 6.9 % respectively for noise free data and 5 percent randomly added noisy data. This indicates that if ANN is designed and trained properly, then it would be capable enough to perform 2D inverse modeling of MT data. It has also shown that once the designed network has been trained properly it is able to perform the inverse modeling precisely in a short time. At the end, the performance of the designed network has been evaluated by a set of field MT data and its results has been compared with those produced by a common smooth rapid relaxation inversion (RRI) method. The comparison indicates that the results of these two different procedures are in close agreement.