Relative contributions of crust and mantle to generation of Oligo-Miocene medium-K calc-alkaline I-type granitoids in a subduction setting—a case study from the Nabar Pluton,central Iran

The Nabar pluton with the age of Oligo-Miocene located northwest of Isfahan, the Urumieh-Dokhtar magmatic belt, is composed of gabbro, gabbro diorite, diorite, quartz diorite, tonalite, and quartz monzonite. These rocks contain plagioclase, quartz, alkali-feldspar, magnesiohornblende, actinolite, tremolite-hornblende, actinolite-hornblende, anthophyllite, biotite, and Na-poor pyroxene. Application of the Al-in-hornblende barometry indicates pressures of 2–2.15 kbar, whereas the clinopyroxene barometry shows a pressure of 5 kbar. The temperature (i.e., 750–800°C) is estimated using the amphibole-clinopyroxene thermometry in a dioritic sample. Magmatic water content was greater than 10% at the time of formation of dioritic rocks in the Nabar pluton. Based on chemistry of mafic minerals and geochemical data, the Nabar plutonic complex comprises medium-K, calc-alkaline, and I-type granitoid. The rocks are characterized by enrichment of lithophile elements (LILEs) and depletion of high-field-strength elements (HFSEs). The Nabar rocks have weak concave-upward rare earth element (REE) patterns, suggesting that amphibole played a significant role in their generation during magma segregation. Low (Al₂O₃/(FeO + MgO + TiO₂) and (Na₂O + K₂O)/(FeO + MgO + TiO₂) ratios, and the patterns of trace and rare earth elements suggest that these rocks formed along a destructive plate margin and were derived from a lower crustal source. The magma probably formed by partial melting of lower crustal protoliths (amphibolites). Lower crust contamination with magma derived from partial melting of the upper mantle has an important role in the formation of this intrusive body, and a fractional crystallization of melts in higher crustal levels generated this spectrum of rock types. Mantle-derived gabbroic magmas emplaced into the lower crust are the most likely heat sources for partial melting.     

An alternative direct method towards mean dynamic topography computations

An alternative “direct method” to “mean dynamic topography” (MDT) computations using satellite altimetry-derived “mean sea surface” (MSS) and “global geopotential model” (GGM), without direct application of the geoid, is devised. The developed approach, which is based on derivation of an equipotential surface of the gravity field of the Earth that fits to global MSS in least squares sense, is formulated via a constrained optimization problem. The validity of our method is numerically tested by computing a global MDT model based on DNSC08 MSS model and EGM2008 GGM as input data.     

A bias-free geodetic boundary value problem approach to height datum unification

A geodetic boundary value problem (GBVP) approach has been formulated which can be used for solving the problem of height datum unification. The developed technique is applied to a test area in Southwest Finland with approximate size of 1.5° × 3° and the bias of the corresponding local height datum (local geoid) with respect to the geoid is computed. For this purpose the bias-free potential difference and gravity difference observations of the test area are used and the offset (bias) of the height datum, i.e., Finnish Height Datum 2000 (N2000) fixed to Normaal Amsterdams Peil (NAP) as origin point, with respect to the geoid is computed. The results of this computation show that potential of the origin point of N2000, i.e., NAP, is (62636857.68 ± 0.5) (m²/s²) and as such is (0.191 ± 0.003) (m) under the geoid defined by W ₀ = 62636855.8 (m²/s²). As the validity test of our methodology, the test area is divided into two parts and the corresponding potential difference and gravity difference observations are introduced into our GBVP separately and the bias of height datums of the two parts are computed with respect to the geoid. Obtaining approximately the same bias values for the height datums of the two parts being part of one height datum with one origin point proves the validity of our approach. Besides, the latter test shows the capability of our methodology for patch-wise application.     

Effect of topographic bias on geoid and reference ellipsoid of Venus, Mars, and the Moon

Since the continuation of an external gravity field inside topographic masses by a harmonic function results in topographic bias, geoid computation by means of global gravity models (GGMs) in terms of external-type series of spherical harmonics, at locations where the GGMs are evaluated inside the topographic masses, will be biased. Consequently, if the reference ellipsoid is defined based on the geoid, it will also be biased. In this paper, the effects of topographic bias on the geoid and reference ellipsoid of Venus, Mars, and the Moon are studied. Moreover, a thorough error analysis in the geoid and reference ellipsoid computation is presented, and it is shown that the estimated standard deviation (STD) of the geoid potential value, the geoidal heights, and the semimajor and semiminor axes of the reference ellipsoid are independent of the topographic bias. According to the results, the effects of topographic bias on the geoid potential value and the semimajor and semiminor axes of the reference ellipsoid in comparison with their estimated STDs are insignificant for Venus, Mars, and the Moon. Moreover, the effect of topographic bias on the geoidal heights of Venus as compared with the estimated STD of its geoidal heights is insignificant. However, the effects of topographic bias on the geoidal heights of Mars and the Moon can be significant, especially in high mountains such as the Tharsis volcanic region on Mars.     

Discrimination of sand dunes and loess deposits using grain-size analysis in northeastern Iran

Identification and characterization of aeolian deposits in arid environments provide information on mechanisms of loess and sand accumulation. The objectives of this study were to (i) identify the distribution of aeolian deposits, (ii) discriminate loess and sand deposits using granulometric data, and (iii) describe the aeolian deposition in Sarakhs area, northeastern Iran. Particle size distributions of 26 surface samples were determined using a laser grain-size analyzer. Fine sand, very fine sand, and very coarse silt were dominant fractions in studied sediments, and the sum of these fractions ranged from 46.9% in loess deposits to 93.8% in sand dunes. The mean grain size (M _z ) of sand dunes ranged from 3.31 to 3.54 ?, which gradually changed to 4.09 to 5.50 ? in loess deposits. Sorting, skewness, and kurtosis ranged from 0.84 to 1.94 ?, 0.18 to 0.49, and 0.76 to 2.38, respectively. Aeolian deposits in the area resulted in the incorporation of Hariroud River system and Kopeh Dagh Mountains for aeolian particle production and accumulation. Alluvial comminution in Hariroud River is suggested the main mechanism of sand and silt production and flood plain environment the main reservoir of these particles. The mountains of Kopeh Dagh act as a barrier and play a key role for sand and loess accumulation.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

Automatic Selection of Landmarks for Navigation Guidance

Although current navigation services provide significant benefits to people's mobility, the turn‐by‐turn instructions they provide are sometimes ineffective. These instructions require people to maintain a high level of attention and cognitive workload while performing distance or angle measurements on their own mental map. To overcome this problem, landmarks have been identified as playing a major role in turn‐by‐turn instructions. This requires the availability of landmarks in navigation databases. Landmarks are commonly selected manually, which involves time‐consuming and tedious tasks. Automatic selection of landmarks has recently gained the attention of researchers but currently there are only a few techniques that can select appropriate landmarks. In this article, we present a technique based on a neural network model, where both static and dynamic features are used for selecting landmarks automatically. To train and test this model, two labeling approaches, manual labeling and rule‐based labeling, are also discussed. Experiments on the developed technique were conducted and the results show that rule‐based labeling has a precision of approximately 90%, which makes the technique suitable and reliable for automatic selection of landmarks.     

Assessing desertification by using soil indices

Desertification generally refers to land degradation in arid, semiarid, and dry semi-humid climatic zones. It involves five principal processes: vegetation degradation, water erosion, wind erosion, salinization and waterlogging, and soil crusting and compaction. The aim of this study is assessing desertification using soil criteria. For this purpose, nine indices including sodium absorption ratio (SAR), soil gypsum percentage, soil texture, the content of HCO₃ ^?, the percentage of the organic matter, electrical conductivity (EC), pH, the content of the soil sodium, and chloride were used. The soil samples were taken in the north of Zayandeh-Rood River in Isfahan province of Iran, using soil data randomly sampled in a depth of 0–20 cm. After assessing the normality of the samples using Kolmogorov-Smirnov test, indices were imported into GIS environment and interpolated with IDW and normal and discrete kriging methods for delineating soil characteristics maps based on MEDALUS model. In this model, the data were firstly changed from 100 to 200. Thus 100 and 200 are estimated as the best and worst quality, respectively. Then the final map of soil criteria has been created by geometric mean of its indicators. The results showed that the maximum area is related to the medium class of desertification and is equal to 44,746 ha. The areas of severe and very severe classes of desertification are equal to 30,949 and 351 ha, respectively. The results also revealed that the indices of the organic matter and soil gypsum percentage are the most influential indices which affect desertification phenomenon.