Petrology of Igneous Rocks in Northern Golpayegan, Iran

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Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery

Reconstruction of seismic data is routinely used to improve the quality and resolution of seismic data from incomplete acquired seismic recordings. Curvelet‐based Recovery by Sparsity‐promoting Inversion, adapted from the recently‐developed theory of compressive sensing, is one such kind of reconstruction, especially good for recovery of undersampled seismic data. Like traditional Fourier‐based methods, it performs best when used in conjunction with randomized subsampling, which converts aliases from the usual regular periodic subsampling into easy‐to‐eliminate noise. By virtue of its ability to control gap size, along with the random and irregular nature of its sampling pattern, jittered (sub)sampling is one proven method that has been used successfully for the determination of geophone positions along a seismic line. In this paper, we extend jittered sampling to two‐dimensional acquisition design, a more difficult problem, with both underlying Cartesian and hexagonal grids. We also study what we term separable and non‐separable two‐dimensional jittered samplings. We find hexagonal jittered sampling performs better than Cartesian jittered sampling, while fully non‐separable jittered sampling performs better than separable jittered sampling. Two other 2D randomized sampling methods, Poisson Disk sampling and Farthest Point sampling, both known to possess blue‐noise spectra, are also shown to perform well.     

Petrography and geochemistry of Silurian Niur sandstones,Derenjal Mountains,East Central Iran: implications for tectonic setting,provenance and weathering

Petrographical and geochemical studies of Silurian Niur sandstones, Derenjal Mountains, Central Iran, were carried out to infer their provenance and tectonic setting. Modal analysis data of 37 medium sand size and well-sorted samples revealed that most quartz is composed of monocrystalline grains with straight to slightly undulos extinction and about 3 % polycrystalline quartz has inclusions, such as rutile needles. The sandstones are classified as quartzarenite, sublitharenite, and subarkose types based on framework composition and geochemistry. Petrographic studies reveal that these sandstones contain quartz, feldspars, and fragments of sedimentary rocks. The detrital modes of these sandstones indicate that they were derived from recycled orogen and stable cratonic source. Major and trace element contents of them are generally depleted (except SiO₂) relative to upper continental crust which is mainly due to the presence of quartz and absence of Al-bearing minerals. Modal composition (e.g., quartz, feldspar, and lithic fragments) and discrimination diagrams based on major elements, trace elements (Ti, La, Th, Sc, and Zr), and also such ratios as La/Sc, Th/Sc, La/Co, and Th/Co, in sandstones suggest a felsic igneous source rock and quartzose polycyclic sedimentary provenance in a passive continental margin setting. Furthermore, high Zr/Sc values in these sandstones are considered as a sign of recycling. We indicated paleo-weathering conditions by modal compositions, the CIA index and Al₂O₃?+?K₂O?+?Na₂O% vs. SiO₂% bivariate for these sandstones. Based on these results, although recycling is important to increase the maturity of the Niur sandstones, humid climate conditions in the source area have played a decisive role.     

Assessment of drought hazard impact on wheat cultivation using standardized precipitation index in Iran

The assessment of drought hazard impacts on wheat cultivation as a strategic crop in Iran is essential for making mitigation plans to reduce the impact of drought. Standardized precipitation index has gained importance in recent years as a potential drought indicator and is being used more frequently for assessment of drought hazard in many countries. In the present study, the calculated standardized precipitation index for 48 stations dataset in the 30-year time scale fulfilled 30 statistical matrices. The drought hazard index map was produced by sum overlaying the spatial representations of 30 statistical matrices and categorized into four levels of low, moderate, high, and very high, which demonstrated probability of drought occurrences of 10–20 %, 20–30 %, 30–40 %, and 40–50 %, respectively. Finally, after the general division of zonal statistics in drought hazard index map of Iran, major drought hazard zones were geographically classified into five zones. The statistical analysis showed a significant correlation (R ²?=?0.701 to 0.648) between drought occurrences and wheat cultivation including surface area and total production for these drought hazard zones.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

Facies Analysis and Sequence Stratigraphy of Silurian Carbonate Ramps in the Turan (Kopeh-Dagh) and Central Iran Plates with Emphasis on Gondwana Tectonic Event

Two sections from the Silurian deposits in the Central Iran Micro and Turan Plates were measured and sampled. These deposits are mostly composed of submarine volcanic rocks, skeletal and non-skeletal limestone, shale and sandstone that were deposited in low to high energy conditions (from tidal flat to deep open marine). According to gradual deepening trend, wide lateral distribution of facies as well as absence of resedimentation deposits, a depositional model of a homoclinal ramp was proposed for these deposits. Field observations and facies distribution indicate that, two depositional sequences were recognized in both sections. These sections show similarities in facies and depositional sequence during the Early Silurian in the area. Although there are some opinions and evidences that demonstrated Paleo-Tethys rifting phase started at the Late Ordovician-Early Silurian, similarities suggest that the Turan and Iran Plates were not completely detached tectonic block during this time, and that their depositional conditions were affected by global sea level changes and tectonic events.     

Precision rectification of high resolution satellite imagery without ephemeris data

The huge capability of high resolution satellite imageries (HRSI), that includes spatial, spectral, temporal and radiometric resolutions as well as stereoscopic vision introduces them as a powerful new source for the Photogrammetry, Remote Sensing and GIS communities. High resolution data increases the need for higher accuracy of data modeling. The satellite orbit, position, attitude angles and interior orientation parameters have to be adjusted in the geometrical model to achieve optimal accuracy with the use of a minimum number of Ground Control Points (GCPs). But most high resolution satellite vendors do not intend to publish their sensor models and ephemeris data. There is consequently a need for a range of alternative, practical approaches for extracting accurate 2D and 3D terrain information from HRSI. The flexibility and good accuracy of the alternative models demonstrated with KFA-1000 and the well-known SPOT level 1A images. A block of eight KFA-1000 space photos in two strips with 60% longitudinal overlap and 15% lateral sidelap and SPOT image with rational function, DLT, 2D projective, polynomials, affine, conformal, multiquadric and finite element methods were used in the test. The test areas cover parts of South and West of Iran. Considering the quality of GCPs, the best result was found with the DLT method with a RMSE of 8.44 m for the KFA-1000 space photos.     

Watershed modeling of rainfall excess transformation into runoff

In this paper an attempt is made to present a distributed physiographic conceptual model that uses the principles of flow continuity and momentum. For this purpose, the watershed under study is divided into subwatersheds keeping in view the drainage patterns and characteristics. Then the main tributaries are identified and their drainage areas are delineated to form tributary subwatersheds. The main channel subwatersheds have taken care of the remaining area in the vicinity of the main channel. The kinematic wave theory is applied for the overland runoff computations from these subwatersheds. Further, the overland flows are superimposed onto the main channel. The dynamic wave theory is used to route the flows through the main channel to compute the watershed responses at the outlet. The proposed model is tested onto a natural watershed. The computations were performed for few storm events. Comparison of the significant parameters of the computed and the observed hydrographs shows that the maximum relative error in prediction is 5.8%. Thus, the results are satisfactory. Better results can be obtained when measured rainfall-excess data are available or a more realistic loss index is adopted for rainfall-excess separation.