排序方式： 共有211条查询结果，搜索用时 281 毫秒
151.
Haoyuan Hong Seyed Amir Naghibi Mostafa Moradi Dashtpagerdi Hamid Reza Pourghasemi Wei Chen 《Arabian Journal of Geosciences》2017,10(7):167
Forest fire is known as an important natural hazard in many countries which causes financial damages and human losses; thus, it is necessary to investigate different aspects of this phenomenon. In this study, performance of four models of linear and quadratic discriminant analysis (LDA and QDA), frequency ratio (FR), and weights-of-evidence (WofE) was investigated to model forest fire susceptibility in the Yihuang area, China. For this purpose, firstly, a forest fire locations map was prepared implementing MODIS satellite images and field surveys. Then, it was classified into two groups including training (70%) and validation (30%) by a random algorithm. In addition, 13 forest fire effective factors were prepared and used such as slope degree, slope aspect, altitude, Topographic Wetness Index (TWI), plan curvature, land use, Normalized Difference Vegetation Index (NDVI), annual rainfall, distance from roads and rivers, wind effect, annual temperature, and soil texture. Using the training dataset and effective factors, LDA, QDA, FR, and WofE models were applied and forest fire susceptibility maps were prepared. Finally, area under the curve (AUC) of receiver operating characteristics (ROC) was implemented for investigating the performance of the models. The results depicted that WofE had the best performance (AUC = 82.2%), followed by FR (AUC = 80.9%), QDA (AUC = 78.3%), and LDA (AUC = 78%), respectively. The results of this study showed the high contribution of altitude, slope degree, and temperature. On the other hand, it was seen that slope aspect and soil had the lowest importance in forest fire susceptibility mapping. From the AUC results, it can be concluded that FR, WofE, LDA, and QDA had acceptable performance and could be used for forest fire susceptibility mapping at the regional scale. 相似文献
152.
Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam,Iran 总被引：1，自引：1，他引：0
Khalil Rezaei Bernard Guest Anke Friedrich Farajollah Fayazi Muhammad Nakhaei Ali Beitollahi Seyed Mahmoud Fatemi Aghda 《Acta Geophysica》2009,57(2):271-293
An application of the artificial neural network (ANN) approach for predicting mean grain size using electric resistivity data
from Bam city is presented. A feed forward back propagation network was developed employing 45 sets of input data. The input
variables in the ANN model are the electrical resistivity, water table as a Boolean value and depth; the output is the mean
grain size. To demonstrate the authenticity of this approach, the network predictions are compared with those from interpolation
methods and the same data. This comparison shows that the ANN approach performs better results. The predicted and observed
mean grain size values were compared and show high correlation coefficients. The ANN approach maps show a high degree of correlation
with well data based grain size maps and can therefore be used conservatively to better understand the influence of input
parameters on sedimentological predictions. 相似文献
153.
Retraction Note to: Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike
Basser Hossein Shamshirband Shahaboddin Karami Hojat Petković Dalibor Akib Shatirah Jahangirzadeh Afshin 《Natural Hazards》2020,103(2):2627-2628
Natural Hazards - The Editors-in-Chief have retracted this article [1] because validity of the content of this article cannot be verified. 相似文献
154.
Alaminia Zohreh Mehrabi Behzad Razavi Seyed Mohammad Hossein Tecce Francesca 《Mineralogy and Petrology》2020,114(1):15-38
Mineralogy and Petrology - The Ghorveh-Seranjic (GS) skarn is located in the northern part of the Sanandaj-Sirjan zone, NW Iran, which is part of Alpine-Himalaya orogenic belt. The GS metamorphic... 相似文献
155.
Determination of Salinity in Fluid Inclusions with Laser Raman Spectroscopy Technique 总被引：2，自引：0，他引：2
Seyed Javad Moghaddasi 《中国地质大学学报(英文版)》2000,11(4)
ＩＮＴＲＯＤＵＣＴＩＯＮＬａｓｅｒＲａｍａｎｍｉｃｒｏｐｒｏｂｅｈａｓｂｅｅｎｆｏｕｎｄｉｎｍａｎｙｇｅｏｌｏｇｉｃａｌａｐｐｌｉｃａｔｉｏｎｓ (Ｓｈｏｖａｌｅｔａｌ.,1995 ;ＭｃＭｉｌｌａｎａｎｄＨｏｆｍｅｉｓｔｅｒ,1988;ＭｃＭｉｌｌａｎ ,1984;Ｄｕｂｅｓｓｙｅｔａｌ.,1982 ;Ｇｒｉｆｆｉｔｈ ,1975 )ａｎｄａｓａｎｏｎ ｄｅｓｔｒｕｃｔｉｖｅａｎｄｉｎｓｉｔｕｔｅｃｈｎｉｑｕｅ,ｉｔｈａｓｈａｄａｔｒｅｍｅｎｄｏｕｓｉｍｐａｃｔｏｎｄｅｖｅｌｏｐｍｅｎｔｏｆｆｌｕｉｄｉｎｃｌｕｓｉｏｎｃｏｍｐｏｓｉ… 相似文献
156.
Maryam Sadat Mirkamali Abdolrahim Javaherian Hossein Hassani Mohammad Reza Saberi Seyed Abolfazl Hosseini 《Geophysical Prospecting》2020,68(7):2195-2216
Quality, availability and consistency of the measured and interpreted well log data are essential in the seismic reservoir characterization methods, and seismic petrophysics is the recommended workflow to achieve data consistency between logs and seismic domains. This paper uses seismic petrophysics workflow to improve well logs and pore geometry interpretations for an oil carbonate reservoir in the Fahliyan Formation in the southwest of Iran. The petrophysical interpreted well logs, rock physics and well-to-seismic tie analysis are integrated into the proposed workflow. Our implementation incorporates revising petrophysical well log interpretations and updating pore geometry characteristics to obtain a better well-tie quality. We first propose an improved pore-type characterization approach based on both P- and S-wave velocities for quantifying pore geometry. Then, seismic logs are estimated accordingly, and the results are used in the well-to-seismic analysis. The quality of the well-tie is improved, furthermore, by iterating on the petrophysical interpreted well logs as well as the calculated pore geometries. For the intervals with high-quality data, our workflow improves the consistency between the results of measured and modelled seismic logs. For the intervals with problematic well logs, the application of our proposed workflow results in the successful replacement of the poor data and subsequently leads to an improved wavelet estimation and well-tie results. In both cases, a higher quantification of pore geometries is achieved, which in turn is confirmed by the core images and formation micro-imager analysis. 相似文献
157.
Water Resources - The lack of water resources as a critical substance and the increase of water demands and also the complexity of water resources management issues are made the integrated water... 相似文献
158.
Non‐linear stochastic inversion of gravity data via quantum‐behaved particle swarm optimisation: application to Eurasia–Arabia collision zone (Zagros,Iran) 
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下载免费PDF全文 Potential field data such as geoid and gravity anomalies are globally available and offer valuable information about the Earth's lithosphere especially in areas where seismic data coverage is sparse. For instance, non‐linear inversion of Bouguer anomalies could be used to estimate the crustal structures including variations of the crustal density and of the depth of the crust–mantle boundary, that is, Moho. However, due to non‐linearity of this inverse problem, classical inversion methods would fail whenever there is no reliable initial model. Swarm intelligence algorithms, such as particle swarm optimisation, are a promising alternative to classical inversion methods because the quality of their solutions does not depend on the initial model; they do not use the derivatives of the objective function, hence allowing the use of L1 norm; and finally, they are global search methods, meaning, the problem could be non‐convex. In this paper, quantum‐behaved particle swarm, a probabilistic swarm intelligence‐like algorithm, is used to solve the non‐linear gravity inverse problem. The method is first successfully tested on a realistic synthetic crustal model with a linear vertical density gradient and lateral density and depth variations at the base of crust in the presence of white Gaussian noise. Then, it is applied to the EIGEN 6c4, a combined global gravity model, to estimate the depth to the base of the crust and the mean density contrast between the crust and the upper‐mantle lithosphere in the Eurasia–Arabia continental collision zone along a 400 km profile crossing the Zagros Mountains (Iran). The results agree well with previously published works including both seismic and potential field studies. 相似文献
159.
The third-generation wave model, WAVEWATCH III, was employed to simulate bulk wave parameters in the Persian Gulf using three different wind sources: ERA-Interim, CCMP, and GFS-Analysis. Different formulations for whitecapping term and the energy transfer from wind to wave were used, namely the Tolman and Chalikov (J Phys Oceanogr 26:497–518, 1996), WAM cycle 4 (BJA and WAM4), and Ardhuin et al. (J Phys Oceanogr 40(9):1917–1941, 2010) (TEST405 and TEST451 parameterizations) source term packages. The obtained results from numerical simulations were compared to altimeter-derived significant wave heights and measured wave parameters at two stations in the northern part of the Persian Gulf through statistical indicators and the Taylor diagram. Comparison of the bulk wave parameters with measured values showed underestimation of wave height using all wind sources. However, the performance of the model was best when GFS-Analysis wind data were used. In general, when wind veering from southeast to northwest occurred, and wind speed was high during the rotation, the model underestimation of wave height was severe. Except for the Tolman and Chalikov (J Phys Oceanogr 26:497–518, 1996) source term package, which severely underestimated the bulk wave parameters during stormy condition, the performances of other formulations were practically similar. However, in terms of statistics, the Ardhuin et al. (J Phys Oceanogr 40(9):1917–1941, 2010) source terms with TEST405 parameterization were the most successful formulation in the Persian Gulf when compared to in situ and altimeter-derived observations. 相似文献
160.
Zhou Jian Koopialipoor Mohammadreza Murlidhar Bhatawdekar Ramesh Fatemi Seyed Alireza Tahir M. M. Jahed Armaghani Danial Li Chuanqi 《Natural Resources Research》2020,29(2):625-639
Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled.
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