The natural radioactivity levels in sediment samples of the northern coast of Oman Sea, covering the coastal strip from Hormoz canyon to Goatr seaport, as the first time has been determined. The results of measurements will serve as background reference level for Oman Sea coastlines. Sediments from 36 coastal and near shore locations were collected for analysis. Analysis on the collected samples were carried out to determine (235)U, (238)U, (232)Th, (40)K and (137)Cs using two high purity germanium detectors with 38.5% and 55% relative efficiencies. The concentration of (235)U, (238)U, (232)Th, (40)K and (137)Cs in sediment samples ranged between 1.01 and 2.87Bq/kg, 11.83 and 22.68Bq/kg, 10.7 and 25.02Bq/kg, 222.89 and 535.07Bq/kg and 0.14 and 2.8Bq/kg, respectively. The radium equivalent activity was well below the defined limit of 370Bq/kg. The external hazard indices were found to be less than 1, indicating a low dose. 相似文献
The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers. 相似文献
In pore network modeling, the void space of a rock sample is represented at the microscopic scale by a network of pores connected by throats. Construction of a reasonable representation of the geometry and topology of the pore space will lead to a reliable prediction of the properties of porous media. Recently, the theory of multi-cellular growth (or L-systems) has been used as a flexible tool for generation of pore network models which do not require any special information such as 2D SEM or 3D pore space images. In general, the networks generated by this method are irregular pore network models which are inherently closer to the complicated nature of the porous media rather than regular lattice networks. In this approach, the construction process is controlled only by the production rules that govern the development process of the network. In this study, genetic algorithm has been used to obtain the optimum values of the uncertain parameters of these production rules to build an appropriate irregular lattice network capable of the prediction of both static and hydraulic information of the target porous medium. 相似文献
In the presence of errors in measuring a random displacement field (under the normal distribution assumption of displacement field), stochastic behaviors of principal components of deformation tensors (strain tensor and tensor of change of curvature (TCC)), based on the intrinsic assumption of geometrical modeling of surface deformation analysis, are discussed. We divided the contents into two parts: In the first, we considered independent random vectors of repeated tensor measurements. In the second step, we considered correlations among repeated measurements. Then, covariance components between tensor elements by Helmert estimator, based on prior information of variance components, are estimated. As a case study, both assumptions are applied to the estimation of principal components of deformation rate tensor observations in Zagros region (Western Iran). The results of numerical analysis showed that greatest shortening is accommodated in oblique orientation (NS) with respect to the Main Recent Fault (MRF), northwest part of North Zagros, Central Iran block and MRF, respectively. Most of the extensions occurred in the east part of the belt. The pattern of eigenspace components of TCC shows highest positive values across the NW region, nearly in orthogonal direction to the MRF and Main Zagros Fault (MZF). The pattern has insignificant values in the Central Zagros. It takes the significant negative values across the SW part, especially along the SPF and Persian Gulf shore. The effect of non-independent observations on the estimation of eigenspace components of deformation tensors (strain tensor and TCC) shows that the estimation of covariance components has influence on the confidence intervals of eigenspace components, especially in seismically active regions of the belt (along the Persian Gulf shore, NW of the belt and region between the Central Iran block and MRF). The results demonstrate the importance of considering the correlation structure among the observations on statistical behavior of principal components of deformation tensors in seismically active regions. 相似文献
During the past earthquakes, many valuable acceleration time histories were recorded by analog and digital accelerometers. These records are important sources of information in the field of earthquake engineering and engineering seismology. However, a large number of these records are contaminated by noise and it is necessary to correct them for practical applications. On the other hand, only a few records can be corrected using conventional filtering because of mathematical limitations of the method. However, advances in the field of time–frequency analysis and wavelet transform theory provide useful non-linear and adaptive de-noising methods for removing of non-stationary and high-energy noise from the recorded signals. In this paper, the characteristics and capabilities of the modified non-linear adaptive wavelet de-noising method are examined for correction of highly noisy strong motion records. In the frequency domain, it is shown that this method can attenuate the noise in the whole frequency range of engineering interest while in the time domain it can detect and remove non-stationary noise. In addition, the displacement response spectra of these wavelet de-noised records are more stable than conventional filtered records with respect to different correction functions. It is found that a large number of noisy acceleration records that are usually discarded from sets of records used for estimating the ground motions can be corrected using this new method. 相似文献
Qanat is an ancient underground structure to abstract groundwater without the need for external energy. A recognized world heritage, Qanat has enabled civilization in arid and semi-arid regions that lack perennial surface water resources. These important structures, however, have faced significant challenges in recent decades due to increasing anthropogenic pressures. This study uses remote sensing to investigate land-use changes and the loss of 15,983 Qanat shafts in the Mashhad plain, northeast of Iran, during the past six decades. This entails obtaining a rare aerial imagery from 1961, as well as recent satellite imagery, over a region with the highest density of Qanats in Iran, the birthplace of Qanat. Results showed that only 5.59% of the Qanat shafts in 1961 remained intact in 2021. The most prominent Qanat-impacting land-use changes were agriculture and urban areas, that accounted for 42.93 and 31.81% Qanat shaft destruction in the study area, respectively. This study also showed that groundwater table decline, demographic changes, and reduction in the appeal of working in the Qanat maintenance and construction industry among the new generation are existential threats to Qanats, and may result in the demise of these ancient structures in the future. Findings of this study can be used for urban planning in arid and semi-arid areas with the aim of protecting these historic water structures. 相似文献
Natural Hazards - Vehicles can be easily swept away by floodwaters once the flow velocity and depth reach certain critical limits, with probabilities toward fatality reported to be nearly 50%.... 相似文献
Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (Tcrit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of Tcrit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.
