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.     

An Agent‐Based Simulation of Residential Location Choice of Tenants in Tehran,Iran

Residential location choice modeling is one of the substantial components of land use and transportation models. While numerous aggregated mathematical and statistical approaches have been developed to model the residence choice behavior of households, disaggregated approaches such as the agent‐based modeling have shown interesting capabilities. In this article, a novel agent‐based approach is developed to simulate the residential location choice of tenants in Tehran, the capital of Iran. Tenants are considered as agents who select their desired residential alternatives according to their characteristics and preferences for various criteria such as the rent, accessibility to different services and facilities, environmental pollution, and distance from their workplace and former residence. The choice set of agents is limited to their desired residential alternatives by applying a constrained NSGA‐II algorithm. Then, agents compete with each other to select their final residence among their alternatives. Results of the proposed approach are validated by comparing simulated and actual residences of a sample of tenants. Results show that the proposed approach is able to accurately simulate the residence of 59.3% of tenants at the traffic analysis zone level.     

Estimation of ship velocities from MODIS and OCM

Detection of ships and their tracks in the atmosphere from satellites was earlier demonstrated by Porch, Noone, and Kaufman, among others. In this letter, we have gone one step further to estimate the ship speed and direction by locating them and their tracks from multisatellite imagery. Exhausts from the ships create streaks of clouds in the atmosphere that help identify the same ship from two satellites. Ship velocities are estimated from displacements of ships. We have used optical sensors data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean Color Monitor (OCM) to demonstrate this technique. Estimated velocities of ships are within the expected range. Application of this approach has general interest to the navy, coast guards, shipping corporations, commercial ship owners, and fishermen. More satellite observations can be used to continuously monitor the ship velocities.     

Impacts of Depositional Facies and Diagenesis on Reservoir Quality: A Case Study from the Rudist-bearing Sarvak Formation, Abadan Plain, SW Iran

An integrated geological-petrophysical analysis of the rudist-bearing sequence of the Cretaceous Sarvak Formation is given one giant oilfield, and provides an improved understanding of this main reservoir in the Abadan Plain, in the Zagros Basin, SW Iran. The main objective of this study is to evaluate reservoir potential of the Sarvak Formation, and then to utilize the calibrated well log signature to correlate reservoir potential in un-cored wells. Eight main facies are recognized and categori...     

Formation rates of Subantarctic mode water and Antarctic intermediate water within the South Pacific

The formation of Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW) significantly contributes to the total uptake and storage of anthropogenic gases, such as CO₂ and chlorofluorocarbons (CFCs), within the world's oceans. SAMW and AAIW formation rates in the South Pacific are quantified based on CFC-12 inventories using hydrographic data from WOCE, CLIVAR, and data collected in the austral winter of 2005. This study documents the first wintertime observations of CFC-11 and CFC-12 saturations with respect to the 2005 atmosphere in the formation region of the southeast Pacific for SAMW and AAIW. SAMW is 94% and 95% saturated for CFC-11 and CFC-12, respectively, and AAIW is 60% saturated for both CFC-11 and CFC-12. SAMW is defined from the Subantarctic Front to the equator between potential densities 26.80-27.06 kg m⁻³, and AAIW is defined from the Polar Front to 20°N between potential densities 27.06-27.40 kg m⁻³. CFC-12 inventories are 16.0×10⁶ moles for SAMW and 8.7×10⁶ moles for AAIW, corresponding to formation rates of 7.3±2.1 Sv for SAMW and 5.8±1.7 Sv for AAIW circulating within the South Pacific. Inter-ocean transports of SAMW from the South Pacific to the South Atlantic are estimated to be 4.4±0.6 Sv. Thus, the total formation of SAMW in the South Pacific is approximately 11.7±2.2 Sv. These formation rates represent the average formation rates over the major period of CFC input, from 1970 to 2005. The CFC-12 inventory maps provide direct evidence for two areas of formation of SAMW, one in the southeast Pacific and one in the central Pacific. Furthermore, eddies in the central Pacific containing high CFC concentrations may contribute to SAMW and to a lesser extent AAIW formation. These CFC-derived rates provide a baseline with which to compare past and future formation rates of SAMW and AAIW.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

Evaluation of the physico-mechanical parameters affecting the deterioration rate of Ahlat ignimbrites (Bitlis,Turkey)

The paper principally focuses on the durability assessment of various stratigraphic levels of Ahlat ignimbrites collected from the eastern region of Turkey. A total of four different ignimbrite types with dissimilar color, texture and particularly welding degree were tested in laboratory. The laboratory tests performed on the ignimbrite specimens indicate that the welding degree as well as the lithic material content mainly controls the strength and capillarity properties of the ignimbrites. In addition, the durability of highly porous ignimbrites strongly depends upon the degree of welding. The effect of several weathering agents on the ignimbrites was evaluated on the basis of decay constant parameter. Accordingly, salt and ice crystallization pressures are a couple of major destructive agents acting within the micropores of the ignimbrites. Conversely, the investigated specimens are relatively durable against cyclic wetting–drying. Statistical evaluations reveal that the pore diameter is the major controlling factor on the deterioration rate of the ignimbrites after specifically recurrent freeze–thaw cycles. Moreover, the dry unit weight of the ignimbrites is more significant than the uniaxial compressive strength considering the deterioration rates during wetting–drying and salt crystallization. A less significant relationship was obtained between pore diameter and salt crystallization decay constant.     

Denoising and improving the quality of seismic data using combination of DBM filter and FX deconvolution

Seismic data denoising, random noise attenuation (RNA) and spike-like noise suppression, is a main consideration for improving the quality of records. RNA could increase signal to noise ratio (S/N) to avoid misinterpretation of seismic data. In this research, a novel method is created by using the combination of frequency-offset deconvolution (FXD) and decision-based median (DBM) filter for RNA from seismic data. The method is applied in two main phases; FXD is focused to remove the Gaussian noise and DBM filter is focused to attenuate the impulsive noise and spikes. To implement and verify the method, three types of data are used: two synthetic models (a model with linear events and a model with hyperbolic events) and an observed seismic section. The ability of the proposed method (FXD-DBM) in comparison of applying each in seismic RNA application is proven. The noise level is reduced obviously, and hence, the S/N of all examined seismic records is increased considerably after denoising by the combination of FX deconvolution and DBM filter. About the real seismic section, suppressing random noise and spikes show up improving the seismic reflector continuity and hence enhancing the interpretability of data. Moreover, some masked events by random noise are clarified in different parts of data after denoising using the planned method.     

Sea level variability at Antalya and Mente? tide gauges in Turkey: atmospheric, steric and land motion contributions

Sea level trends and interannual variability at Antalya and Menteş tide gauges are investigated during the 1985–2001 period, quantifying the roles of atmospheric, steric and local land motion contributions. Tide gauge sea level measurements, temperature/salinity climatologies and GPS data are used in the analyses and the results are compared with the output of a barotropic model forced by atmospheric pressure and wind. The overall sea level trends at two tide gauges collocated with GPS are in the range of 5.5 to 7.9 mm/yr during the study period, but showing different behaviour in the sub-periods 1985–1993 and 1993–2001 due to variations in the contributing factors both in space and time. After the removal of the atmospheric forcing and steric contribution from sea level records, the resulting trends vary between 1.9 to 4.5 mm/yr in Antalya and −1.2 to −11.6 mm/yr in Menteş depending on the period considered. Vertical land movement estimated from GPS data seems to explain the high positive residual trend in Antalya during the whole period. On the other hand, the source of the highly negative sea level trend of about −14 mm/yr in Menteş during 1985–1993 could not be resolved with the available datasets. Interannual variability of wind and atmospheric pressure appear to dominate the sea level at both tide gauges during the study period. Atmospheric and steric contributions together account for ∼50% of the total sea level variance at interannual time scales. Mass induced sea level variations which were not considered in this study may help to close the sea level trend budgets as well as to better explain the interannual sea level variance.     

Observations of Giant Pulses of the Crab Pulsar

The Crab Pulsar was observed at 1540 MHz with the 25m radio telescope at Urumqi with a filterbank de-dispersion backend. A total of 2436 giant pulses with pulse energies larger than 4300 Jy μs were detected in two observing sets. All of these giant pulses are located in the main pulse (MP) and inter pulse (IP) windows of the average profile of the Crab Pulsar. The ratio of the numbers of giant pulses detected in the IP and MP windows is about 0.05. Our results show that, at 1540 MHz, the emission in the IP is contributed by giant and normal pulses, while that in the MP is almost dominated by giant pulses. The distribution of energy of the 2436 giant pulses at 1540 MHz can be described by a power-law with index α=3.13±0.09. The intrinsic threshold of giant pulse energy in the MP window is about 1400 Jy μs at 1540 MHz.