共查询到20条相似文献,搜索用时 15 毫秒
1.
《Comptes Rendus Geoscience》2015,347(1):2-12
Time-frequency peak filtering (TFPF) is an effective method for seismic random noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate random noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method. 相似文献
2.
K. Kumar M. Parida V. K. Katiyar 《International Journal of Environmental Science and Technology》2014,11(3):719-730
This study applies artificial neural network (ANN) for the determination of optimized height of a highway noise barrier. Field measurements were carried out to collect traffic volume, vehicle speed, noise level, and site geometry data. Barrier height was varied from 2 to 5 m in increments of 0.1 m for each measured data set to generate theoretical data for network design. Barrier attenuation was calculated for each height increment using Federal Highway Administration model. For neural network design purpose, classified traffic volume, corresponding traffic speed, and barrier attenuation data have been taken as input parameters, while barrier height was considered as output. ANNs with different architectures were trained, cross validated, and tested using this theoretical data. Results indicate that ANN can be useful to determine the height of noise barrier accurately, which can effectively achieve the desired noise level reduction, for a given set of traffic volume, vehicular speed, highway geometry, and site conditions. 相似文献
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Time-Frequency Peak Filtering (TFPF) is an effective method to eliminate pervasive random noise when seismic signals are analyzed. In conventional TFPF, the pseudo Wigner–Ville distribution (PWVD) is used for estimating instantaneous frequency (IF), but is sensitive to noise interferences that mask the borderline between signal and noise and detract the energy concentration on the IF curve. This leads to the deviation of the peaks of the pseudo Wigner–Ville distribution from the instantaneous frequency, which is the cause of undesirable lateral oscillations as well as of amplitude attenuation of the highly varying seismic signal, and ultimately of the biased seismic signal. With the purpose to overcome greatly these drawbacks and increase the signal-to-noise ratio, we propose in this paper a TFPF refinement that is based upon the joint time-frequency distribution (JTFD). The joint time-frequency distribution is obtained by the combination of the PWVD and smooth PWVD (SPWVD). First we use SPWVD to generate a broad time-frequency area of the signal. Then this area is filtered with a step function to remove some divergent time-frequency points. Finally, the joint time-frequency distribution JTFD is obtained from PWVD weighted by this filtered distribution. The objective pursued with all these operations is to reduce the effects of the interferences and enhance the energy concentration around the IF of the signal in the time-frequency domain. Experiments with synthetic and real seismic data demonstrate that TFPF based on the joint time-frequency distribution can effectively suppress strong random noise and preserve events of interest. 相似文献
5.
Acta Geotechnica - The random finite element method has been widely used to evaluate slope uncertainty and reliability. To determine the probability of failure, the safety factor sampling often... 相似文献
6.
In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with
a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis
methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage
method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate
model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and
the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed
data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied
only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can
be used to predict regional storm surges and may be used to develop a forecast system. 相似文献
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In this paper, we propose a technique of random noise attenuation from seismic data using discrete and continuous wavelet transforms. Firstly, the discrete wavelet transform (DWT) is applied to denoise seismic data using the threshold method. After, we calculate the continuous wavelet transform of the denoised seismic seismogram, the final denoised seismic seismogram is the continuous wavelet transform coefficients at the lower scale. Application to a synthetic seismogram shows the robustness of the proposed tool for random noise attenuation. Application to real vertical seismic profile recorded in Algeria clearly shows the efficiency of the proposed tool for random noise attenuation. 相似文献
9.
Sajad Haghir Chehreghani Aref Alipour Mehdi Eskandarzade 《Journal of the Geological Society of India》2011,78(3):271-277
One important decision in design of surface mine is the selection of mine equipment and plant. Demand for mechanical excavation
is growing in mining industry because of its high productivity and excavation in large scale with lower costs. Several models
have been developed over the years to evaluate the ease of excavation and machine performance against rock mass properties.
Due to complexity of excavation process and large number of effective parameters, approaches made for this purpose are essentially
empirical. There are many uncertainties in results of these models. An attempt is made in this paper to revise the exisiting
models. Neural network models for estimation of rock mass excavatability and production rate of VASM-2D excavating machine
at Limestone quarry in Retznei, Austria, is presented. Input parameters of this model are Uniaxial compressive strength, tensile
strength and discontinuities spacing of rocks. Output is the specific excavation rate per power consumption (bcm/Kwh) as the
productivity indicator. Average of deviation between actual data and results estimated by neural network model was only 15%
which is in an acceptable range. 相似文献
10.
Use of artificial neural network for spatial rainfall analysis 总被引:1,自引:0,他引:1
TSANGARATOS PARASKEVAS ROZOS DIMITRIOS BENARDOS ANDREAS 《Journal of Earth System Science》2014,123(3):457-465
In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies. 相似文献
11.
为了更准确地提取蚀变信息,本文选择新疆、甘肃和内蒙古三省交界部位为研究区,结合小波包变换和随机森林提取ASTER蚀变信息。首先,选择主要蚀变类型的诊断性波段进行特征向量主成分分析,得到主分量影像;接着,对主分量影像进行小波包变换,使用代价函数选择最优小波包树,并提取高低频信息构造分类向量;然后,经过特征筛选构造随机森林分类模型,并提取矿化蚀变信息;最后,通过野外采样、薄片鉴定对提取结果进行精度评价。铁染、Al-OH及Mg-OH蚀变信息的主成分分析波段组合分别选择Band 1、2、3、4,Band 1、3、4、6及Band 1、5、8、9。结果表明,本文方法提取铁染、Al-OH基团及Mg-OH基团蚀变信息的总体精度可达到88.7443、85.5469及91.7594,Kappa分别为0.7767、0.6732及0.8362,与成矿区带及已有的该区域的成矿特征相关性较好。本研究采用的最优小波包树能充分利用矿物光谱的能量特征,随机森林可削弱矿物组分的噪声干扰,研究结果可为遥感蚀变信息提取提供技术参考。 相似文献
12.
H. Aghamohammadi M. S. Mesgari A. Mansourian D. Molaei 《International Journal of Environmental Science and Technology》2013,10(5):931-939
In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network’s capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation. 相似文献
13.
Prediction and controlling of flyrock in blasting operation using artificial neural network 总被引:3,自引:1,他引:3
M. Monjezi Amir Bahrami Ali Yazdian Varjani Ahmad Reza Sayadi 《Arabian Journal of Geosciences》2011,4(3-4):421-425
Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine. 相似文献
14.
The residual strength of clay is very important to evaluate long term stability of proposed and existing slopes and for remedial measure for failure slopes. Various attempts have been made to correlate the residual friction angle (r) with index properties of soil. This paper presents a neural network model to predict the residual friction angle based on clay fraction and Atterberg's limits. Different sensitivity analysis was made to find out the important parameters affecting the residual friction angle. Emphasis is placed on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of soil properties on the residual shear angle. A prediction model equation is established with the weights of the neural network as the model parameters. 相似文献
15.
An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %. 相似文献
16.
Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model. 相似文献
17.
Forecasting of groundwater level in hard rock region using artificial neural network 总被引:2,自引:0,他引:2
In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has
already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs
of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has
urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the
aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and
potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a
method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction
tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development
of groundwater resources. 相似文献
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The compression index is a one of the important soil parameters that is essential to geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming, empirical formulas based on soil parameters can be useful. Over the decades, a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, liquid limit, plasticity index, specific gravity, and others. Each of the existing empirical formulas yields good results for a particular test set, but cannot accurately or reliably predict the compression index from various test sets. In this study, an alternative approach, an artificial neural network (ANN) model, is proposed to estimate the compression index with numerous consolidation test sets. The compression index was modeled as a function of seven variables including the natural water content, liquid limit, plastic index, specific gravity, and soil types. Nine hundred and forty-seven consolidation tests for soils sampled at 67 construction sites in the Republic of Korea were used for the training and testing of the ANN model. The predicted results showed that the neural network could provide a better performance than the empirical formulas. 相似文献
20.
This paper describes the application of the artificial neural network model to predict the lateral load capacity of piles in clay. Three criteria were selected to compare the ANN model with the available empirical models: the best fit line for predicted lateral load capacity (Qp) and measured lateral load capacity (Qm), the mean and standard deviation of the ratio Qp/Qm and the cumulative probability for Qp/Qm. Different sensitivity analysis to identify the most important input parameters is discussed. A neural interpretation diagram is presented showing the effects of input parameters. A model equation is presented based on neural network parameters. 相似文献