共查询到20条相似文献，搜索用时 671 毫秒
1.
An artificial neural networks (ANN) approach combined with Fourier Transform based selection of time period in the time series
Radon Emission Data has been presented and shown to improve event prediction rates and reduce false alarms in Earthquake Event
Identification over the traditional multiple linear regression techniques. The paper presents a neural networks system using
radial basis function (RBF) network as an alternative to traditional statistical regression technique in isolating Radon Emission
Anomaly caused by seismic activities. The RBF model has been developed to accept and predict earthquakes events based on a
known data set of Radon Emanation, Metrological parameters and actual earthquake events. Subsequently, the model was tested
and evaluated on a future data set and a prediction rate of 87.8%, if a reduced false alarm was achieved, the results obtained
are better than the traditional techniques. 相似文献
2.
Gülhan Özbayoğlu A. Murat Özbayoğlu M. Evren Özbayoğlu 《International Journal of Mineral Processing》2008
In this research, different techniques for the estimation of coal HGI values are studied. Data from 163 sub-bituminous coals from Turkey are used by featuring 11 coal parameters, which include proximate analysis, group maceral analysis and rank. Non-linear regression and neural network techniques are used for predicting the HGI values for the specified coal parameters. Results indicate that a hybrid network which is a combination of 4 separate neural networks gave the most accurate HGI prediction and all of the neural network models outperformed non-linear regression in the estimation process. 相似文献
3.
An artificial neural network (ANN) toolbox is created within GIS software for spatial interpolation, which will help GIS users to train and test ANNs, perform spatial analysis, and display results as a single process. The performance is compared to that of the open source Fast Artificial Neural Network library and conventional interpolation methods by creating digital elevation models (DEMs) given that nearly exact solutions exist. Simulation results show that the advanced backpropagations such as iRprop speed up the learning, while they can get stuck in a local minimum depending on initial weight sets. Besides, the division of input–output examples into training and test data affects the accuracy, particularly when the distribution of the examples is skewed and peaked, and the number of data is small. ANNs, however, show the similar performance to inversed distance weighted or kriging and outperform polynomial interpolations as a global interpolation method in high-dimensional data. In addition, the neural network residual kriging (NNRK) model, which combines the ANN toolbox and kriging within GIS software, is performed. The NNRK outperforms conventional methods and well captures global trends and local variations. A key outcome of this work is that the ANN toolbox created within the de facto standard GIS software is applicable to various spatial analysis including hazard risk assessment over a large area, in particular when there are multiple potential causes, the relationship between risk factors and hazard events is not clear, and the number of available data is small given its performance for DEM generation. 相似文献
4.
Aliakbar Kakouei Mohsen Masihi Behnam Sedaee Sola Ebrahim Biniaz 《Journal of the Geological Society of India》2014,84(3):326-334
Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN), Competitive Learning (CL) and Learning Vector Quantizer (LVQ). All these methods have been applied in four wells of South Pars field, Iran. Data of three wells were employed for the networks training purpose and the fourth one was used to test and verify the trained network predictions. The results have demonstrated that all approaches have the ability of facies modeling with more than 65% of precision. According to the performed analysis, RBF, CL and LVQ methods could model the facies with the accuracy between 66 and 68 percent while PNN and BPNN techniques are capable of making predictions with more than 72% and 88.5% of precision, respectively. It can be concluded that the BPNN can generate most accurate results in comparison to the other type of networks but it is important to note that the other factors such as consuming the amount of time taken, simplicity and the less adjusted parameters as well as the acquired precisions should be considered. As a result, the model evaluation analysis used in this study can be useful for prospective surveys and cost benefit facies identification. 相似文献
5.
人工神经网络在环境灾害预测中的应用进展 总被引：1，自引：0，他引：1
李英柳 《地质灾害与环境保护》2010,21(1):8-11
综述了近年来人工神经网络在环境灾害预测中的应用进展,包括对污染型环境灾害、生态环境灾害、地质环境灾害以及气象环境灾害等灾害的成灾因素分析、危险性预测以及灾情预测和动态演变规律预测,揭示了人工神经网络探索灾害隐含信息方面的优越性,并对其在环境灾害预测中的应用前景和发展趋势进行了展望。 相似文献
6.
Prediction of rock burst classification using the technique of cloud models with attribution weight 总被引：4，自引：2，他引：2
Rock burst is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst classification problem. Then, the attribution weight method is presented to quantify the contribution of each rock burst indicator for classification. The approach is implemented to predict the classes of rock burst intensity for the 164 rock burst instances collected. The clustering figures are generated by cloud models for each rock burst class. The computed weight values of the indicators show that the stress ratio $ Ts = \sigma_{\theta } /\sigma_{c} $ Ts = σ θ / σ c is the most vulnerable parameter and the elastic strain energy storage index W et and the brittleness factor $ B = \sigma_{c} /\sigma_{t} $ B = σ c / σ t take the second and third place, respectively, contributing to the rock burst classification. Besides, the predictive performance of the strategy introduced in this study is compared with that of some empirical methods, the regression analysis, the neural networks and support vector machines. The results turn out that cloud models perform better than the empirical methods and regression analysis and have superior generalization ability than the neural networks in modelling the rock burst cases. Hence, cloud models are feasible and applicable for prediction of rock burst classification. Finally, different models with varying indicators are investigated to validate the parameter sensitivity results obtained by cloud clustering analysis and regression analysis in context to rock burst classification. 相似文献
7.
《Chemie der Erde / Geochemistry》2021,81(4):125826
Regression or regression-like models are often employed in mineral prospectivity modeling, i.e., for the targeting of resources, either based on 2D map images or 3D geomodels both in raster mode or based on spatial point processes. Machine learning techniques like artificial neural networks are often applied and give decent results in the prediction of target events. However, they typically provide little insight into the problem regarding the importance, or relevance, of covariables. On the other hand, logistic regression has a well understood statistical foundation and uses an explicit model from which knowledge can be gained about the underlying phenomenon. Establishing such an explicit model is rather difficult for real world problems. In the context of mineral prospectivity modeling additional challenges arise, such as rare events, i.e. only a small fraction of data instances describes a positive target event, which is the event of interest.In this paper, we propose a model selection procedure applied to logistic regression incorporating explicit nonlinearities. The model selection procedure, based on the Wald test and the Bayes' information criterion (BIC), as proposed in this paper is new. The performance regarding the predictive power of the obtained model is comparable to logistic regression using a stepwise model selection and to neural networks on several real world datasets, one of them a dataset for the detection of gold mineralizations in Ghana. However, our new method is significantly faster than standard stepwise selection, while selecting fewer variables for the final model. In our numerical experiments, the prediction accuracy is also comparable to a neural network, which is currently in use in industry.In applications, the method can aid the model building process through an explicit model. Furthermore, it may be used as preprocessing step for other machine learning algorithms such as neural networks. In this paper, we intend to present mathematics of prospectivity modeling with the potential to contribute to bridging the gap between statistical and machine learning. Big Data and Artificial Intelligence are of increasing importance in mineral exploration. At the same time there is a growing demand for mathematically rigorous machine learning methods, which can still be interpreted by experts. This paper is a contribution to this field. 相似文献
8.
In the well-log data processing, the principal advantage of the nuclear magnetic resonance (NMR) method is the measurement of fluid volume and pore size distribution without resorting to parameters such as rock resistivity. Preliminary processing of the well-log data allowed first to have the petrophysical parameters and then to evaluate the performances of the transverse relaxation time T 2 NMR. Petrophysical parameters such as the porosity of the formation as well as the effective permeability can be estimated without having recourse the fluid type. The well-log data of five wells were completed during the construction of intelligent models in the Saharan oil field Oued Mya Basin in order to assess the reliability of the developed models. Data processing of NMR combined with conventional well data was performed by artificial intelligence. First, the support vector regression method was applied to a sandy clay reservoir with a model based on the prediction of porosity and permeability. NMR parameters estimated using intelligent systems, i.e., fuzzy logic (FL) model, back propagation neural network (BP-NN), and support vector machine, with conventional well-log data are combined with those of NMR, resulting in a good estimation of porosity and permeability. The results obtained during the processing are then compared to the FL and NN regression models performed by the regression method during the validation stage. They show that the correlation coefficients R 2 estimated vary between 0.959 and 0.964, corresponding to the root mean square error values of 0.20 and 0.15. 相似文献
9.
Issanova Gulnura Jilili Abuduwaili Semenov Oleg 《Arabian Journal of Geosciences》2014,7(11):4513-4521
Deflation processes are important in arid environments such as deserts. The deserts of Kazakhstan mostly cover lowlands and extend from the eastern coast of the Caspian Sea to the piedmonts of the Tien-Shan Mountain. Desert areas are also major source areas of dust/sand storm activities. We considered deflation processes in the southern Pre-Balkhash deserts. In Kazakhstan, desertification processes due to wind erosion in the form of dust/sand storms were observed in semi-desert and desert landscapes. During analysis of numerous long-term meteorological data and cartographic materials, we revealed the sand movement directions which allow prediction of future potential sand movement patterns or processes in southern Pre-Balkhash deserts. The Taukum, Moiynkum deserts, Ili river deltas and valleys, and southern coastal of Lake Balkhash are most prone to dust/sand storms. The most frequent storms were observed in the Bakanas weather station (Ile river valley). Sand/dust transport occurs mainly in the east, south-east north-east direction in the southern Pre-Balkhash deserts. The high amount of sand transportation was observed at the Kuigan weather station; low amounts were encountered at the Naimansuiek weather station. The amount of airborne sand/dust varies in accordance with the general and local meteorological features, the complexity of relief forms, soil conditions and properties, lithology, and various contributions of the human activities. Thus, our study on deflation processes in the southern Pre-Balkhash deserts has great importance towards aiding in the prediction and monitoring of dust/sand storms and movement patterns. 相似文献
10.
Soil temperature has an important role in agricultural, hydrological, meteorological and climatological studies. In the present research, monthly mean soil temperature at four different depths (5, 10, 50 and 100 cm) was estimated using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). The monthly mean soil temperature data of 31 stations over Iran were employed. In this process, the data of 21 and 10 stations were used for training and testing stages of used models, respectively. Furthermore, the geographical information including latitude, longitude and altitude as well as periodicity component (the number of months) was considered as inputs in the mentioned intelligent models. The results demonstrated that the ANN and ANFIS models had good performance in comparison with the GEP model. Nevertheless, the ANFIS generally performed better than ANN model. 相似文献
11.
Oscar Veses Rosa Mosteo Maria P. Ormad Jose L. Ovelleiro 《Environmental Earth Sciences》2014,72(8):2917-2929
This study was carried out to evaluate sediment pollution related to trace elements such as Cd, Cu, Ni, Pb, Zn, Hg, As and Cr and eight polycyclic aromatic hydrocarbons (PAHs) in 127 sites located in 85 rivers in Spain. Sediment samples were classified according to similar chemical characteristics by means of statistical multivariate techniques (principal component analysis, PCA) and artificial neural networks such as self-organizing maps (SOM). Sediment sample classification provided by PCA was not as useful as the one provided by the SOM, revealing itself as a powerful tool to be incorporated in the first steps of sediment quality assessments. The use of sediment quality guidelines such as the mean-probable effects concentration quotient (m-PECQ) predicted sediment quality and gave an overall view of sediment pollution throughout Spain. Most of the samples (118 out of 127) showed m-PECQ values below 0.5 highlighting their relative low potential risk to cause adverse effects on the benthic fauna. However, some samples presented m-PECQ values higher than 0.5 suggesting a clear potential risk to these fauna. Besides, unusual high concentrations of trace elements and PAHs were related to the human activities carried out near each sampling point. 相似文献
12.
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
13.
文章以莱州湾凹陷垦利油田沙河街组储层为例,对传统的回归统计模型和基于BP神经网络的人工智能预测模型评价储层渗透率方法和效果进行了对比研究。目标储量报告里定火沙三段中孔、中渗;岩性（粒度）和孔隙度是储层渗透率的主要影响因素。根据岩心及测井数据,建立了孔隙度——粒度二元回归渗透率统计评价模型和BP神经网络渗透率预测模型。通过检验样本集精度对比,分析了隐含层数、隐含层节点数等网络结构参数变化对模型预测结果的影响,重点分析了不同的测井参数输入对BP神经网络模型预测结果的影响。优化后的BP神经网络模型对检验样本集的渗透率预测结果精度最高,其平均相对误差为37%,比传统的二元回归统计模型精度提高了26%。对目标油田三口井连续处理,BP神经网络模型渗透率预测结果更加合理,可以满足开发层段产能分析等生产需求。 相似文献
14.
The present work reports on the isotopic characterization of rainfall and groundwater at Mt. Vesuvius. Values of δ 18O, monthly measured on rain samples collected during the period 2002–2004 in a rain-gauge network composed of 10 stations, were compared with meteorological and DEM data. Air temperature, controlled by the local orographic structure, was identified as the main factor influencing rain isotopic composition. Another important role is played by orographic clouds, whose condensation over the top of Mt. Vesuvius is responsible for anomalously high δ 18O values recorded in rain samples from the summit area of the volcanic edifice. A spatial model of rain isotopic composition, based on a 3D distribution of temperature derived by a 1 × 1 km DEM, was implemented and used for calculating the theoretical isotopic signature of seepage, further compared with data measured in the groundwater monitoring network. The analysis evidenced the role of local meteoric recharge as the main source feeding Mt. Vesuvius aquifers. The unique exception is the Olivella drainage gallery, located on the north-eastern flank of the volcanic edifice, whose isotopic composition is slightly more positive than the one expected for its altitude, likely caused by both evaporation processes and mixing with condensed hydrothermal vapor. 相似文献
15.
为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。 相似文献
16.
Measuring unconfined compressive strength (UCS) using standard laboratory tests is a difficult, expensive, and time-consuming task, especially with highly fractured, highly porous, weak rock. This study aims to establish predictive models for the UCS of carbonate rocks formed in various facies and exposed in Tasonu Quarry, northeast Turkey. The objective is to effectively select the explanatory variables from among a subset of the dataset containing total porosity, effective porosity, slake durability index, and P-wave velocity in dry samples and in the solid part of samples. This was based on the adjusted determination coefficient and root-mean-square error values of different linear regression analysis combinations using all possible regression methods. A prediction model for UCS was prepared using generalized regression neural networks (GRNNs). GRNNs were preferred over feed-forward back-propagation algorithm-based neural networks because there is no problem of local minimums in GRNNs. In this study, as a result of all possible regression analyses, alternative combinations involving one, two, and three inputs were used. Through comparison of GRNN performance with that of feed-forward back-propagation algorithm-based neural networks, it is demonstrated that GRNN is a good potential candidate for prediction of the unconfined compressive strength of carbonate rocks. From an examination of other applications of UCS prediction models, it is apparent that the GRNN technique has not been used thus far in this field. This study provides a clear and practical summary of the possible impact of alternative neural network types in UCS prediction. 相似文献
17.
Slake durability study of shaly rock and its predictions 总被引：2，自引：0，他引：2
More than 35% of the earths crust is comprised of clay-bearing rocks, characterized by a wide variation in engineering properties and their resistance to short term weathering by wetting and drying phenomenon. The resistance to short-term weathering can be determined by slake durability index test. There are various methods to determine the slake durability indices of weak rock. The effect of acidity of water (slaking fluid) on slake durability index of shale in the laboratory is investigated. These methods are cumbersome and time consuming but they can provide valuable information on lithology, durability and weather ability of rock. Fuzzy set theory, Fuzzy logic and Artificial Neural Networks (ANN) techniques seem very well suited for typical complex geotechnical problems. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. During this investigation a model was developed and compared with two other models i.e., Neuro-fuzzy systems (combination of fuzzy and artificial neural network systems) and artificial neural network system, for the prediction of slake durability index of shaly rock to evaluate the performance of its prediction capability. 相似文献
18.
建立于煤矿开采基础之上的矿山开采沉陷理论和预测方法并不适用于象金川这样厚大、陡倾的金属矿床开采的岩移问题,因此,本文探讨利用神经网络来对地表岩移进行预测。根据Elman神经网络能够逼近任意非线性函数的特点和具有反映系统动态特性的能力,提出了利用Elman神经网络建立地表岩移时序预报模型的方法。利用金川二矿区GPS监测所得到的时间序列数据,通过对Elman神经网络模型预测值与GPS实测值之间的比较,结果表明模型预测显示了良好的准确性,特别是在时间步长较短情况下,应用于实际预测一定程度上可以弥补金属矿山岩移预测方法不足的缺憾。 相似文献
19.
致密砂岩气层压裂产能及等级预测方法 总被引：1，自引：0，他引：1
致密砂岩储层孔隙度小、渗透率低、含气饱和度低,基本上没有自然产能,需要进行压裂,因此进行压裂产能的预测很有必要。笔者研究了鄂尔多斯盆地苏里格东部地区盒8段致密砂岩气层的压裂产能及等级预测。利用反映储层流动性质的测井参数(电阻率、自然伽马、声波时差、中子、密度)与反应压裂施工情况的压裂施工参数(单位厚度砂体积、砂比、砂质量浓度、单位厚度排量、单位厚度入井总液量),选择数学统计方法神经网络法进行致密砂岩气层压裂产能等级预测。分析比较Elman神经网络、支持向量回归(SVR)、广义回归神经网络(GRNN)3个神经网络预测致密砂岩气层压裂产能模型的网络结构参数、回判及预测精度以及运行耗费时间。结果表明:3个模型中,GRNN网络参数只有1个,回判和预测精度最高,运行时间小于1 s。因此,选择GRNN模型预测致密砂岩气层压裂产能,并用于苏里格东部地区致密砂岩气层压裂产能的等级预测。等级预测准确率达到90%。 相似文献
20.
Nils Reiche Tanja Westerkamp Steffen Lau Helko Borsdorf Peter Dietrich Claudia Schütze 《Environmental Earth Sciences》2014,72(5):1435-1441
Today, ground-based optical remote sensing (ORS) has become an intensively used method for quantifying pollutant or greenhouse gas emissions from point or area sources, and for the validation of airborne or satellite remote sensing data. In this study, we present the results of a release experiment using acetylene (C2H2) as a tracer gas, where three ORS techniques were simultaneously tested for two main purposes: (1) the detection of emission sources and (2) the quantification of release rates. Therefore, passive and active open-path Fourier transform infrared spectroscopy (OP-FTIR) and open-path tunable diode laser absorption spectroscopy (TDLAS) were applied and evaluated. The concentration results of the active ORS methods are compared to those estimated by a Lagrangian stochastic atmospheric dispersion model. Our results reveal that passive OP-FTIR is a valuable tool for the rapid detection and imaging of emission sources and the spatial tracer gas distribution; while with active OP-FTIR and TDLAS, C2H2 concentrations in the sub-ppm range could be quantified that correlated well with the concentration data obtained by our modeling approach. 相似文献