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1.
Radial Basis Function Network for Ore Grade Estimation 总被引：1，自引：0，他引：1
Biswajit Samanta 《Natural Resources Research》2010,19(2):91-102
This paper highlights the performance of a radial basis function (RBF) network for ore grade estimation in an offshore placer
gold deposit. Several pertinent issues including RBF model construction, data division for model training, calibration and
validation, and efficacy of the RBF network over the kriging and the multilayer perceptron models have been addressed in this
study. For the construction of the RBF model, an orthogonal least-square algorithm (OLS) was used. The efficacy of this algorithm
was testified against the random selection algorithm. It was found that OLS algorithm performed substantially better than
the random selection algorithm. The model was trained using training data set, calibrated using calibration data set, and
finally validated on the validation data set. However, for accurate performance measurement of the model, these three data
sets should have similar statistical properties. To achieve the statistical similarity properties, an approach utilizing data
segmentation and genetic algorithm was applied. A comparative evaluation of the RBF model against the kriging and the multilayer
perceptron was then performed. It was seen that the RBF model produced estimates with the R
2 (coefficient of determination) value of 0.39 as against of 0.19 for the kriging and of 0.18 for the multilayer perceptron. 相似文献
2.
Yibin Ren Yang Zhang 《International journal of geographical information science》2019,33(9):1894-1912
Recently, researchers have introduced deep learning methods such as convolutional neural networks (CNN) to model spatio-temporal data and achieved better results than those with conventional methods. However, these CNN-based models employ a grid map to represent spatial data, which is unsuitable for road-network-based data. To address this problem, we propose a deep spatio-temporal residual neural network for road-network-based data modeling (DSTR-RNet). The proposed model constructs locally-connected neural network layers (LCNR) to model road network topology and integrates residual learning to model the spatio-temporal dependency. We test the DSTR-RNet by predicting the traffic flow of Didi cab service, in an 8-km2 region with 2,616 road segments in Chengdu, China. The results demonstrate that the DSTR-RNet maintains the spatial precision and topology of the road network as well as improves the prediction accuracy. We discuss the prediction errors and compare the prediction results to those of grid-based CNN models. We also explore the sensitivity of the model to its parameters; this will aid the application of this model to network-based data modeling. 相似文献
3.
基于LM-BP神经网络的西北地区太阳辐射时空变化研究 总被引：2，自引：1，他引：1
定量模拟太阳辐射对认识西北地区气候变化至关重要,但西北地区辐射站点稀少,而气象站点较多,利用众多的气象站点观测值模拟太阳辐射是获得太阳辐射数据很好的方法之一。利用LM (Levenberg-Marquardt) 算法对普通的BP神经网络进行优化（优化后的BP神经网络简称LM-BP神经网络）模拟太阳辐射,通过与传统气候模型模拟的太阳辐射结果对比发现,LM-BP神经网络模型的模拟精度最高,模拟值与实测值的拟合程度明显优于H-S模型和A-P模型。由此利用西北地区159个气象站点的气象数据和LM-BP神经网络模型模拟了1990~2012年这些气象站点的太阳总辐射月总量,将LM-BP神经网络模拟的气象站点的太阳辐射和25个辐射观测站的实测太阳辐射数据相结合,通过空间插值得到了西北地区太阳总辐射的空间分布,并分析了其时空分布及变化特征。研究结果发现西北地区1990~2012年的年均总辐射月总量变化为262~643 βMJ/m2,呈现“中间高,两端低”的空间分布特征。LM-BP神经网络模型的模拟精度高,是一种很有发展前景的辐射模拟方法,可将其应用在无辐射观测地区的太阳辐射模拟中。 相似文献
4.
One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively. 相似文献
5.
Yin Xin Liu Quansheng Pan Yucong Huang Xing Wu Jian Wang Xinyu 《Natural Resources Research》2021,30(2):1795-1815
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
相似文献6.
Several alternative estimation and interpolation methods for making annual precipitation maps of Asturias are analysed. The
data series in this study corresponds to the year 2003. There exists an evident relationship between precipitation and altitude,
with a high correlation coefficient of 0.70, that reflects the hillside effect; that is, the increase in the amount of precipitation
in more mountainous areas. The direct spatial variability of precipitation and of altitude and the cross variability of precipitation–altitude
are defined by two exponential variogram models: one with a short-range structure (15–30 km) that reflects the control exerted
by the lesser, local mountain ranges over the amount of precipitation; and another with a long-range structure (80 km) that
supposes the influence over precipitation of the major mountainous alignments of the inland areas of the Cantabrian Mountain
Range (Cordillera Cantábrica) situated between 60 and 90 km from the coastline. These variogram models had to be validated for coregionalization by the
Pardo-Igúzquiza and Dowd method so as to be able to make the cokriging map. The geometric estimation methods employed were
triangulation and inverse distance. The geostatistical estimation methods developed were simple kriging, ordinary kriging,
kriging with a trend model (universal kriging), lognormal kriging, and cokriging. In all of these methods, a 3 × 3 km2 grid was selected with a total of 2580 points to estimate, a circular search window of 60 km, and a relatively small number
of samples with the aim of highlighting the local features and variations on isohyet maps. The kriging methods were implemented
using the WinGslib software, incorporating two specific programs, Prog2 and Fichsurf, so as to be able then to make isohyet
maps using the Surfer software. All the methods employed, apart from triangulation, rendered realistic maps with good fits
to the values of the original data (precipitation) of the sample maps. The problem with triangulation lies not in the reliability
of the estimates but in the fact that it gives rise to contrived maps because of the tendency of isohyets to present abundant
triangular facets. The reliability of the methods was based on cross-validation analysis and on evaluation of the different
types of errors, both in their values and in their graphical representations. Substantial differences were not found in the
values of the errors that might discriminate some methods from others in an evident way. Bearing the aforesaid in mind, should
we have to make an evaluation of the different estimation methods in decreasing order of acceptance, this would be: kriging
with a trend model, inverse distance, cokriging, lognormal kriging, ordinary kriging, simple kriging, and triangulation. The
application of other estimation methods such as colocated cokriging, kriging with an external drift, and kriging of variable
local means (residual kriging) is dependent on the availability of a digital model of the terrain with an altitude grid of
the region. 相似文献
7.
中国高铁网络结构特征及其组织模式 总被引：2，自引：4，他引：2
基于2018年高铁网络OD数据,运用社会网络分析方法从高铁网络、城市节点等方面探讨中国城市高铁网络结构特征及其地域组织模式,结果表明：①中国高铁网络整体较为松散,东北地区网络密度最高,东部和中部地区作为整体网络的中介作用明显;②重要高铁线路的“廊道效应”突出,中心度呈现出以京广、京沪和沪昆高铁组成的“三角旗状”空间格局并向两侧城市呈不规则递减的态势;③多层级网络识别出紧密关联高铁线路和四横四纵向八横八纵格局的转变;④高铁网络的地域组织形式表现为点-轴串珠模式、双核组团模式和极核模式,高铁网络的完善使组织模式由单核趋向于网络化转变。 相似文献
8.
SeaWinds散射计海面风场神经网络建模研究 总被引：2，自引：0，他引：2
根据Sea Winds散射计只有两个入射角和两种极化方式的特点,利用其L2A数据和F291海上浮标数据,针对传统建模方法的不足和限制,借助神经网络建立了一个两种极化方式下统一的神经网络地球物理模型函数。该模型的主要特点是建模风矢量全部取自海上浮标测量数据,因而所用风矢量更加客观准确。通过与Qscat-1模型的比较和L2B与浮标风速之间的偏差统计分析,证明了该神经网络模型的有效性,并发现Qscat-1模型存在一定的系统性偏差。 相似文献
9.
Three-dimensional distribution analysis of phosphorus content of limestone through a combination of geostatistics and artificial neural network 总被引：1，自引：0，他引：1
One of the factors that determines the suitability of limestone for industrial use and its commercial value is phosphorus
(P) content, i.e., the weight percentage of phosphorus contained in small quantities of limestone. Because P content changes
locally, geostatistical techniques including semivariogram, ordinary kriging, and conditional indicator sequential simulation
were used in this study to identify the spatial correlation of P content and to estimate its three-dimensional distribution
in an open-pit mine. The P content data at 43,000 points of five different bench levels were analyzed. It was found that the
horizontal semivariograms produced by using the data at the same bench level show anisotropic behavior and are represented
by the sum of two spherical models with different ranges and sills. The twelve vertical semivariograms were also constructed
from P content in boring cores. After these semivariograms were classified into four types, a multilayered neural network
was applied to clarify the horizontal distribution of each one. One of the twelve semivariograms was assigned to an arbitrary
grid point in the study area by the criterion that its type is the same as the one estimated at the point and the borehole
site producing the semivariogram is the nearest to the point. With this technique, ordinary kriging combined with the semivariogram
of borehole data provided a proper estimation of P content in the depth direction. 相似文献
10.
Mousaab Zakhrouf Hamid Bouchelkia Madani Stamboul Sungwon Kim Salim Heddam 《自然地理学》2018,39(6):506-522
The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m3/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m3/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m3/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m3/s, EC = 56.90%, R = .755) during the test period. 相似文献
11.
In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic
neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration
mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed
data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is
that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically
sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation,
they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions.
They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic
neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was
reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive
sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal
gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold
deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type
deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko
massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver
districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of
deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized
by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits
when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should
this preliminary test be expanded to include other deposit types, but that other deposit features should be added 相似文献
12.
采用2007、2012和2017年中国百强物流企业网络数据,运用世界城市网络研究的链锁网络模型方法,分析中国城市网络的等级结构、空间分异、空间联系及其演变特征。研究表明：基于物流企业数据的中国城市网络具有明显的层级特征,并呈现“两主多中心”的区域空间结构,网络联系的离散水平逐渐降低,其稳定性和均衡性有所增加,等级结构趋于合理;各节点城市组织能力空间分异明显,京津冀、长三角、珠三角的组织能力和介中心性始终处于较高水平,城市间物流联系逐渐增强,并有协同发展的趋势;城市网络联系中,长江沿线城市物流网络联系地位上升,上海为中心的T字型空间结构替代以北京为中心的核心网络结构,在全国尺度形成以胡焕庸线为界线,物流联系网络呈中东部密集、西部相对稀疏的空间格局,其演变过程呈现由等级网络联系特征向等级性与空间近邻性网络联系特征转变趋势。 相似文献
13.
The factors determining the suitability of limestone for industrial use and its commercial value are the amounts of calcium oxide (CaO) and impurities. From 244 sample points in 18 drillhole sites in a limestone mine, southwestern Japan, data on four impurity elements, SiO2, Fe2O3, MnO, and P2O5 were collected. It generally is difficult to estimate spatial distributions of these contents, because most of the limestone bodies in Japan are located in the accretionary complex lithologies of Paleozoic and Mesozoic age. Because the spatial correlations of content data are not clearly shown by variogram analysis, a feedforward neural network was applied to estimate the content distributions. The network structure consists of three layers: input, middle, and output. The input layer has 17 neurons and the output layer four. Three neurons in the input layer correspond with x, y, z coordinates of a sample point and the others are rock types such as crystalline and conglomeratic limestones, and fossil types related to the geologic age of the limestone. Four neurons in the output layer correspond to the amounts of SiO2, Fe2O3, MnO, and P2O5. Numbers of neurons in the middle layer and training data differ with each estimation point to avoid the overfitting of the network. We could detect several important characteristics of the three-dimensional content distributions through the network such as a continuity of low content zones of SiO2 along a Lower Permian fossil zone trending NE-SW, and low-quality zones located in depths shallower than 50 m. The capability of the neural network-based method compared with the geostatistical method is demonstrated from the viewpoints of estimation errors and spatial characteristics of multivariate data. To evaluate the uncertainty of estimates, a method that draws several outputs by changing coordinates slightly from the target point and inputting them to the same trained network is proposed. Uncertainty differs with impurity elements, and is not based on just the spatial arrangement of data points. 相似文献
14.
Bin Zou Zhong Zheng Neng Wan Yonghong Qiu Jeff Gaines Wilson 《International journal of geographical information science》2016,30(4):727-747
GIS-based proximity models are one of the key tools for the assessment of exposure to air pollution when the density of spatial monitoring stations is sparse. Central to exposure assessment that utilizes proximity models is the ‘exposure intensity–distance’ hypothesis. A major weakness in the application of this hypothesis is that it does not account for the Gaussian processes that are at the core of the physical mechanisms inherent in the dispersion of air pollutants.
Building upon the utility of spatial proximity models and the theoretical reliability of Gaussian dispersion processes of air pollutants, this study puts forward a novel Gaussian weighting function-aided proximity model (GWFPM). The study area and data set for this work consisted of transport-related emission sources of PM2.5 in the Houston-Baytown-Sugar Land metropolitan area. Performance of the GWFPM was validated by comparing on-site observed PM2.5 concentrations with results from classical ordinary kriging (OK) interpolation and a robust emission-weighted proximity model (EWPM). Results show that the fitting R2 between possible exposure intensity calculated by GWFPM and observed PM2.5 concentrations was 0.67. A variety of statistical evidence (i.e., bias, root mean square error [RMSE], mean absolute error [MAE], and correlation coefficient) confirmed that GWFPM outperformed OK and EWPM in estimating annual PM2.5 concentrations for all monitoring sites. These results indicate that a GWFPM utilizing the physical dispersing mechanisms integrated may more effectively characterize annual-scale exposure than traditional models. Using GWFPM, receptors’ exposure to air pollution can be assessed with sufficient accuracy, even in those areas with a low density of monitoring sites. These results may be of use to public health and planning officials in a more accurate assessment of the annual exposure risk to a population, especially in areas where monitoring sites are sparse. 相似文献
15.
针对常规农用地分等模型因子权重计算存在人为干扰和神经网络模型自身优化过程中易陷入局部最优的情况,该文综合了BP神经网络非线性权重数据挖掘特性和粒子群的全局优化能力,建立了农用地分等计算的粒子群神经网络混合模型(PSO-BP网络模型),并应用于广东省揭西县农用地分等计算中,发现PSO-BP网络模型能避免定级因子权重确定的人为干扰,同时具有较高的优化效率,应用效果较好。 相似文献
16.
以湖北省鄂州程潮铁矿和黄石大冶铁矿为例,利用GIS空间分析功能对研究区数据进行提取分级、赋值统计及归一化等处理,构建了包括高程、坡度、地层、地下开采点的分布密度、相距最近地下开采点的距离、开采厚度与深度比值、蚀变接触带缓冲区、地下水深度以及地表地物类型的矿区采空塌陷易发性评价指标数据集;借助IDL语言调用Matlab神经网络工具箱,将研究区2011和2012年的指标数据集作为输入数据,塌陷易发性作为期望输出,建立基于BP神经网络的矿区采空塌陷易发性预测模型;通过选取并优化训练样本,实现对2013年矿山塌陷易发性的预测。结果表明,高易发区及以上的区域包含89.91%的采空塌陷,随着易发等级的提高,采空塌陷面积占易发等级面积比也随之增大;采空塌陷的分布具有明显的地带性,高易发区基本沿着岩体与围岩的接触带分布。模型解决了塌陷预测中的非线性映射问题,预测结果与实际调查情况基本吻合。BP神经网络模型与GIS技术相结合预测矿区采空塌陷的易发性具有可行性。 相似文献
17.
18.
The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it. 相似文献
19.
Bui Xuan-Nam Nguyen Hoang Le Hai-An Bui Hoang-Bac Do Ngoc-Hoan 《Natural Resources Research》2020,29(2):571-591
Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R2?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R2, and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.
相似文献20.
A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types. 相似文献