Crustal Thickness of Iran Inferred from Converted Waves

Taghizadeh-Farahmand, Fataneh; Afsari, Narges; Sodoudi, F.

doi:10.1007/s00024-014-0901-0

Cited by 40 publications

(32 citation statements)

References 39 publications

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“…Moho depth variations calculated by crustal gravity modeling (e.g., Jimenez‐Munt et al, ; Figure a) and seismic receiver functions surveys (Sodoudi et al, ; Paul et al, ; Radjaee et al, ; Motaghi et al, ; Taghizade_Farahmand et al, ; Teknik et al, ; Figure b) show a significant crustal thickening beneath the Zagros Mountains. Since there is no volcanic activity within the Zagros mountain range (e.g., Agard et al, ), we expect a deeper Curie isotherm for the Zagros mountain range.…”

Section: Resultsmentioning

confidence: 99%

“…The comparison of the DBMS (Figure 11) with the Moho depth ( Figure 13) shows a weak correlation between the two maps and the DBMS is generally shallower than the Moho depth except in the Makran accretionary wedge. Moho depth variations calculated by crustal gravity modeling (e.g., Jimenez-Munt et al, 2012; Figure 13a) and seismic receiver functions surveys (Sodoudi et al, 2009;Paul et al, 2010;Radjaee et al, 2010;Motaghi et al, 2015;Taghizade_Farahmand et al, 2015;Teknik et al, 2019; Figure 13b) show a significant crustal thickening beneath the Zagros Mountains. Since there is no volcanic activity within the Zagros mountain range (e.g., Agard et al, 2011), we expect a deeper Curie isotherm for the Zagros mountain range.…”

Section: Journal Of Geophysical Research: Solid Earthmentioning

confidence: 99%

“…The oceanic plate cooling model of Stein and Stein (1992) suggests a depth of~40 km for Curie isotherm of the oceanic lithosphere of age 80-100 Ma, closely in agreement with the calculated DBMS in the Makran accretionary wedge. (Sodoudi et al, 2009;Paul et al, 2010;Radjaee et al, 2010;Motaghi et al, 2015;Taghizade_Farahmand et al, 2015;Teknik et al, 2019) based on receiver function analysis. Inverted triangles are the location of broadband seismic stations across Iran.…”

Section: 1029/2019jb018119mentioning

confidence: 99%

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Estimation of Depth to Bottom of Magnetic Sources Using Spectral Methods: Application on Iran's Aeromagnetic Data

2020

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The effect of window size while estimating the depth to bottom of magnetic sources (DBMS) is examined on the synthetic 2‐D magnetic field data. The data are generated for 3‐D fractal magnetization models assuming different combinations of depth to the top and bottom of magnetic sources. The depths are estimated assuming an uncorrelated and correlated random distribution of sources. The depths to the top estimated for fractal distribution of sources are found within 70% of the true values for all window sizes, whereas the values for the uncorrelated random distributions are far from the real depths. The DBMS for the correlated random distribution of sources are estimated within 70–80% of the true values for the window sizes of 5 times or more of the targeted depths. The modified centroid method is found to provide better estimations as compared to scaling‐spectral peak modeling. The modified centroid method is applied to the aeromagnetic data of Iran to estimate the DBMS using a window size of 200 × 200 km2. The shallow DBMS between 12 and 20 km are obtained for the Quaternary Sabalan and Sahand volcanoes to the NW of Iran, the central and NE of central Iran, the central Zagros, NW of Tabas block, and the south of the Lut Block. The deepest DBMS of the order of 40 km is found in the Makran. The rest of Iranian Plateau is characterized by DBMS of 20 to 30 km. The shallow DBMS are found in correlation with the depth of the Ophiolites.

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Section: Resultsmentioning

confidence: 99%

Section: Journal Of Geophysical Research: Solid Earthmentioning

confidence: 99%

Section: 1029/2019jb018119mentioning

confidence: 99%

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Estimation of Depth to Bottom of Magnetic Sources Using Spectral Methods: Application on Iran's Aeromagnetic Data

2020

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“…To validate the gravimetric Moho solutions, we compared them with existing regional seismic studies for Iran [Mangino and Priestley, 1998;Paul et al, 2006;Taghizadeh-Farahmand et al, 2010;Radjaee et al, 2010;Tatar and Nasrabadi, 2013;Taghizadeh-Farahmand et al, 2015;Motaghi et al, 2015;Abdollahi et al, 2018]. A compilation of these numerous seismic datasets has been prepared and checks for their consistency were performed.…”

Section: Comparing Gravimetric and Seismic Moho Estimatesmentioning

confidence: 99%

“…In the area under investigation in this paper, several studies based on seismic data have been performed for the determination of the regional Moho model. Most of these studies concentrated on a specific area, for instance profiles between Shiraz-Mashhad, Tehran-Mashhad and Mashhad-Tabriz [Asudeh, 1982], the southern part of the Caspian Sea [Mangino and Priestley, 1998], Tehran region [Hatzfeld et al, 2003], Mashhad [Doloei and Roberts, 2003;Javan Doloei, 2003], Central Alborz and the northern Iran [Sodoudi et al, 2009… Radjaee et al, 2010, central Zagros [Paul et al, 2006, Shad Manaman et al, 2011, Kopeh-Dagh [Nowrouzi et al, 2007], Naein [Nasrabadi et al, 2008], the northwest Iran [Taghizadeh-Farahmand et al, 2015], and Sanandaj-Sirjan zone [Sadidkhouy et al, 2012]. Furthermore, based on terrestrial gravity data in this area with a quite homogenously coverage, Dehghani and Makris [1984] combined gravimetric and seismic data to determination of the crustal structure of Iran.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of different gravimetric methods to Moho recovery in Iran

Ebadi

Barzaghi

Safari

et al. 2019

Annals of Geophysics

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The complexity of geological units in Iran because of several unique events like tectonics and orogenic activities in this region led to extensive investigations for Moho recovery by seismic methods therein. In this research, three gravimetric methods have been evaluated by some point-wise seismic data. We applied collocation method as an iterative process as well as modified forms of Sjöberg and Jeffrey's theory of isostasy for local Moho depth recovery. The gravity data has been generated by GOCO03S model reduced by topography/bathymetry, sediment and consolidated crust effects. Although the iteration process in collocation approach only slightly changed the estimated depths, this method led to a better agreement with seismic data rather than others. Differences between collocation, Jeffrey and Sjöberg's solutions with seismic studies are similar but Jeffrey and Sjöberg's methods displayed a systematic bias. The standard deviations of the residuals among seismic data and gravimetric solutions are around 6 km. Overall, the evaluation of these approaches indicated that Moho from gravimetric approaches reduced only slightly the standard deviation of seismic Moho estimates. Significant discrepancies with seismic data have been detected in Makran subduction zone, Oman Sea, Persian Gulf and Caspian Sea. The explanation of such inconsistency can be partially due to the poor quality of CRUST1.0 data in these areas, as this model has been used to correct the gravity values that were input in the inversion procedures.

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jrfapp: A Python Package for Joint Inversion of Apparent S-Wave Velocity and Receiver Function Time Series

Veisi,

Motaghi,

Schiffer

2024

Pure Appl. Geophys.

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Receiver function (RF) inversion is a well-established method to quantify a horizontally layered approximation of the S-wave velocity structure beneath a seismic station using deconvolved signals of P to S converted teleseismic earthquake waves. It is well-known that the RF inverse problem is highly non-unique and non-linear, and various tools exist that may overcome this problem. One of the most-commonly used methods is joint inversion with other seismological data that are sensitive to S-wave velocity, such as surface waves or S-waveforms. In this contribution, we present a joint inversion framework along with a Python package that implements the joint inversion of RF and the so-called apparent S-wave velocity (VS,app). We assess its performance and feasibility through several synthetic tests. Our implementation includes a pseudo-initial model estimation using two alternative methods, which helps address the inherent non-uniqueness of the joint inversion of RFs and VS,app. This implementation enhances the resolving power of the joint inversion, enabling estimation of S-wave velocities with resolution approaching that of deep controlled source seismic methods. As an illustration, we showcase a real-data example from a permanent station in the Makran subduction zone southeast of the Iranian Plateau. We compare our joint inversion results with several S-wave velocity models obtained through a deep seismic sounding profile and joint inversion of surface wave dispersion and RFs. This comparison shows that although we note a slightly lower sensitivity of our proposed method at greater depths (beyond 50 km), the method yields much better results for shallow structures compared to joint inversion of RFs and surface waves.

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Crustal Thickness of Iran Inferred from Converted Waves

Cited by 40 publications

References 39 publications

Estimation of Depth to Bottom of Magnetic Sources Using Spectral Methods: Application on Iran's Aeromagnetic Data

Estimation of Depth to Bottom of Magnetic Sources Using Spectral Methods: Application on Iran's Aeromagnetic Data

Evaluation of different gravimetric methods to Moho recovery in Iran

jrfapp: A Python Package for Joint Inversion of Apparent S-Wave Velocity and Receiver Function Time Series

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