No abstract

No abstract

SUMMARY While the crustal structure across the Iranian plateau is fairly well constrained from controlled source and passive seismic data, the lithospheric mantle structure remains relatively poorly known, in particular in terms of lithology. Geodynamics rely on a robust image of the present-day thermochemical structure interpretations of the area. In this study, the 3-D crustal and upper mantle structure of the Iranian plateau is investigated, for the first time, through integrated geophysical–petrological modelling combining elevation, gravity and gravity gradient fields, seismic and petrological data. Our modelling approach allows us to simultaneously match complementary data sets with key mantle physical parameters (density and seismic velocities) being determined within a self-consistent thermodynamic framework. We first elaborate a new 3-D isostatically balanced crustal model constrained by available controlled source and passive seismic data, as well as complementary by gravity data. Next, we follow a progressively complex modelling strategy, starting from a laterally quasi chemically homogeneous model and then including structural, petrological and seismic tomography constraints. Distinct mantle compositions are tested in each of the tectonothermal terranes in our study region based on available local xenolith suites and global petrological data sets. Our preferred model matches the input geophysical observables (gravity field and elevation), includes local xenolith data, and qualitatively matches velocity anomalies from state of the art seismic tomography models. Beneath the Caspian and Oman seas (offshore areas) our model is defined by an average Phanerozoic fertile composition. The Arabian Plate and the Turan platform are characterized by a Proterozoic composition based on xenolith samples from eastern Arabia. In agreement with previous studies, our results also suggest a moderately refractory Proterozoic type composition in Zagros-Makran belt, extending to Alborz, Turan and Kopeh-Dagh terranes. In contrast, the mantle in our preferred model in Central Iran is defined by a fertile composition derived from a xenolith suite in northeast Iran. Our results indicate that the deepest Moho boundary is located beneath the high Zagros Mountains (∼65 km). The thinnest crust is found in the Oman Sea, Central Iran (Lut Block) and Talesh Mountains. A relatively deep Moho boundary is modelled in the Kopeh-Dagh Mountains, where Moho depth reaches to ∼55 km. The lithosphere is ∼280 km thick beneath the Persian Gulf (Arabian–Eurasian Plate boundary) and the Caspian Sea, thinning towards the Turan platform and the high Zagros. Beneath the Oman Sea, the base of the lithosphere is at ∼150 km depth, rising to ∼120 km beneath Central Iran, with the thinnest lithosphere (<100 km) being located beneath the northwest part of the Iranian plateau. We propose that the present-day lithosphere–asthenosphere topography is the result of the superposition of different geodynamic processes: (i) Arabia–Eurasia convergence lasting from mid Jurassic to recent and closure of Neo-Tethys ocean, (ii) reunification of Gondwanian fragments to form the Central Iran block and Iranian microcontinent, (iii) impingement of a small-scale convection and slab break-off beneath Central Iran commencing in the mid Eocene and (iv) refertilization of the lithospheric mantle beneath the Iranian microcontinent.

No abstract

Numerous studies on capabilities of de-noising and separation by wavelet were performed, and their all aims more and less was elimination of possible largest nongeological factors, noise, and to achieve pure regional effects free from residuals. De-noising could be used for removal of non-desired effects like latitude, terrain, tides, drift etc., from our desired portion of data as target. Separations of anomalies that are not of interest conclude shallow structure is suitable to be optimal. Hence detection and removal of ever larger surface anomalies to obtain optimal separation is of interest. At up to now studies, large deviation of primarily original signal has been prevented. In this paper controlling factors which limit the overall deviation of transformed signal from the original one have been replaced with two new parameters that simultaneously cause extracting the maximum surplus signals, residuals, and also preserving the original form ever possible. Results of artificial models along with application of separation to real data indicate the usefulness of discrete stationary wavelet transform in order to optimal separation of anomalies with various wavelengths.

No abstract

In this paper, a new method is developed based on downward continuation with a varying shape window of hyperbolic S-transforms to estimate the depth of subsurface structures from gravity data. To increase the sensitivity of the algorithm to better define the top surface of a buried structure (i.e., enhanced resolution of short wavelength features), the Fourier transform operator in the downward continuation equation is replaced by a hyperbolic S-transform window with critical shape parameters. The hyperbolic S-transform allows us to focus on the high wavenumber features in the gravity data. Using the downward continuation equation, the greater the continuation level, the more unstable the values of the continued gravity data will be because of the exponentially increasing effects of continuation level within the formula. One of the objectives of our method is to employ a new formula for depth estimation based on a variant version of downward continuation, where the exponential operator is replaced by a logarithmic operator. Because the logarithm has a slow increase initially (gravity anomalies are usually located at the early portion of the position–wavenumber space), a result is the gradual increase in depth values, which avoids large and unrealistic variations in the calculated depths. Unlike the Fourier transform, the S-transform provides depth values dependent on both position and wavenumber. This allows us to average different calculated depths at each station to obtain a value for the depth in each position. The estimated depth values for gravity sources are referenced to the x-axis, because the S-transform provides the localized Fourier spectrum of data. The depth values must be averaged to decrease spatial shifting of locally obtained depths. Moreover, the averaging neutralizes the repetitive depth information that occurs through S-transforms of the original gravity data, rather than the S-transform of the analytic signal of gravity data. Analysis of synthetic and field gravity data illustrates the usefulness of the hyperbolic S-transform for estimating depth values of geologic structures.

No abstract