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.     

Predicting the Shear Behavior of Cemented and Uncemented Carbonate Sands Using a Genetic Algorithm-Based Artificial Neural Network

Carbonate sands that are known as problematic soils, have some unusual features like particle crushability and compressibility that discriminate their behavior from other types of soil. Because of their vast diversity, they have a wide range of mechanical behavior. In recent decades, there have been many attempts to model the mechanical behavior of carbonate sands but all these efforts have been focused on experimental and case studies of some especial sands and there is still no unique way which can appraises all types of carbonate sands behavior and describes their various aspects. In this paper, a new approach is presented based on the integration of Genetic Algorithm (GA) into an Artificial Neural Network (ANN) to predict the shear behavior of carbonate sands. In the proposed approach, the GA was utilized to optimize the connection weights of the ANN. The network was trained and tested using a comprehensive set of triaxial tests that were carried out on three different carbonate sands in both grouted and ungrouted (cemented and uncemented) condition. The network prediction was then compared to the experimental results and it was concluded that the GA-based ANN has a good potential in predicting the behavior and generalizing the training data to simulate new unseen data.     

A preliminary study of graphite-bearing rocks at Wadi Sikait area, South Eastern Desert, Egypt

The studied graphite-bearing rocks are located at Wadi Sikait in the southern part of the Eastern Desert of Egypt to the west of Marsa Alam on the Red Sea coast. They are intruded by granitic rocks and they have low radioactivity level. Mica-graphite schists are considered as a matrix of ophiolitic mélange. Graphite occurs in mica-graphite schists as disseminated grains and in talc carbonates rocks as bands or veins. Petrographically, the mica-graphite schists are mainly composed of quartz, plagioclase, muscovite, biotite, and graphite. Geochemical characteristics show that trace elements analysis reflects higher content in Cr and Ni in ash-free graphite than mica-graphite schists. Spectrometrically, the graphite-bearing rocks at Wadi Sikait are showing eU values greater than eTh values, indicating that the graphite-bearing rocks gain U from the country rocks. The U/eU ratio range from 2.7 to 11 manifesting enrichment of chemical uranium (U) may be related to recent uranium transported from the mineralized country rocks. There is a role of graphite and carbonaceous matter in the genesis of U deposits.     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.     

Distribution of radioactive pollution of 238U, 232Th, 40K and 137Cs in northwestern coasts of Persian Gulf, Iran

A reconnaissance study has been made of the distribution of 238U, 232Th, 40K and 137Cs and geochemical features in soils and sediments samples at various locations in the northwestern coast of Persian Gulf. Activity concentration levels due to radionuclides were measured in 30 soil and sediment samples collected from this region. From the measured spectra, activity concentrations were determined for 40K (range from 146 to 500 Bq kg(-1)), 137Cs (from 5 to 20 Bq kg(-1)), 238U (from 21 to 65 Bq kg(-1)) and 232Th (from 15 to 45 Bq kg(-1)) with lowest limit detection (LLD) of 68, 3.2, 4.3 and 4.3 Bq kg(-1), respectively. The dose rate from ambient air at the soil ranges was between 19 and 58 nGy h(-1) with an average of 37.41+/-9.66 nGy h(-1).     

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.     

Integration of the MODIS Snow Cover Produced Into Snowmelt Runoff Modeling

Because of the difficulty of monitoring and measuring snow cover in mountainous watersheds, satellite images are used as an alternative to mapping snow cover to replace the ground operations in the watershed. Snow cover is one of the most important data in simulation snowmelt runoff. The daily snow cover maps are received from Moderate Resolution Imaging Spectroradiometer (MODIS), and are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). Simulating Snowmelt runoff is presented using SRM model as one of the major applications of satellite images processing and extracting snow cover in the Ghara - Chay watershed. The first results of modeling process show that MODIS snow covered area product can be used for simulation and forecast of snowmelt runoff in Ghara - Chay watershed. The studies found that the SCA results were more reliable in the study area.     

Application of Geographic Information Systems to Analyses and Modeling of Hydrocarbons Anomaly in Yakla,North Tarim Basin,Xinjiang,China

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Assessment of bioclimatic comfort conditions based on Physiologically Equivalent Temperature (PET) using the RayMan Model in Iran

In this study thermal comfort conditions are analyzed to determine possible thermal perceptions during different months in Iran through the Physiologically Equivalent Temperature (PET). The monthly PET values produced using the RayMan Model ranged from ?7.6°C to 46.8°C. Over the winter months the thermal comfort condition (18–23°C) were concentrated in southern coastal areas along the Persian Gulf and Oman Sea. Most of the country experienced comfort conditions during the spring months, in particular in April, while during the summer months of July and August no thermal comfort conditions were observed. In November coastal areas of the Caspian Sea had the same physiological stress level of thermal comfort as April. The map produced showing mean annual PET conditions demonstrated the greatest spatial distribution of comfortable levels in the elevation range from 1000 to 2000 meter a.s.l., with annual temperatures of 12–20°C and annual precipitation of under 200 mm. The statistical relationship between PET conditions and each controlling parameter revealed a significant correlation in areas above 2000 meter, annual temperature over 20°C and annual precipitation of 200–400 mm with a correlation coefficient (R ²) of 0.91, 0.97 and 0.96, respectively.