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1.
Vesa Nykänen 《Natural Resources Research》2008,17(1):29-48
Among the more popular spatial modeling techniques, artificial neural networks (ANN) are tools that can deal with non-linear
relationships, can classify unknown data into categories by using known examples for training, and can deal with uncertainty;
characteristics that provide new possibilities for data exploration. Radial basis functional link nets (RBFLN), a form of
ANN, are applied to generate a series of prospectivity maps for orogenic gold deposits within the Paleoproterozoic Central
Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland, which is considered highly prospective yet clearly under
explored. The supervised RBFLN performs better than previously applied statistical weights-of-evidence or conceptual fuzzy
logic methods, and equal to logistic regression method, when applied to the same geophysical and geochemical data layers that
are proxies for conceptual geological controls. By weighting the training feature vectors in terms of the size of the gold
deposits, the classification of the neural network results provides an improved prediction of the distribution of the more
important deposits/occurrences. Thus, ANN, more specifically RBFLN, potentially provide a better tool to other methodologies
in the development of prospectivity maps for mineral deposits, hence aiding conceptual exploration. 相似文献
2.
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
3.
A recently published study has shown that small-scale geologic map data can reproduce mineral assessments made with considerably
larger scale data. This result contradicts conventional wisdom about the importance of scale in mineral exploration, at least
for regional studies. In order to formally investigate aspects of scale, a weights-of-evidence analysis using known gold occurrences
and deposits in the Central Lapland Greenstone Belt of Finland as training sites provided a test of the predictive power of
the aeromagnetic data. These orogenic-mesothermal-type gold occurrences and deposits have strong lithologic and structural
controls associated with long (up to several kilometers), narrow (up to hundreds of meters) hydrothermal alteration zones
with associated magnetic lows. The aeromagnetic data were processed using conventional geophysical methods of successive upward
continuation simulating terrane clearance or ‘flight height’ from the original 30 m to an artificial 2000 m. The analyses
show, as expected, that the predictive power of aeromagnetic data, as measured by the weights-of-evidence contrast, decreases
with increasing flight height. Interestingly, the Moran autocorrelation of aeromagnetic data representing differing flight
height, that is spatial scales, decreases with decreasing resolution of source data. The Moran autocorrelation coefficient
scems to be another measure of the quality of the aeromagnetic data for predicting exploration targets. 相似文献
4.
5.
Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States 总被引：7，自引：0，他引：7
The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km2 as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence. 相似文献
6.
Andrew A. Skabar 《Natural Resources Research》2011,20(3):143-155
Assuming a study region in which each cell has associated with it an N-dimensional vector of values corresponding to N predictor variables, one means of predicting the potential of some cell to host mineralization is to estimate, on the basis
of historical data, a probability density function that describes the distribution of vectors for cells known to contain deposits.
This density estimate can then be employed to predict the mineralization likelihood of other cells in the study region. However,
owing to the curse of dimensionality, estimating densities in high-dimensional input spaces is exceedingly difficult, and
conventional statistical approaches often break down. This article describes an alternative approach to estimating densities.
Inspired by recent work in the area of similarity-based learning, in which input takes the form of a matrix of pairwise similarities
between training points, we show how the density of a set of mineralized training examples can be estimated from a graphical
representation of those examples using the notion of eigenvector graph centrality. We also show how the likelihood for a test
example can be estimated from these data without having to construct a new graph. Application of the technique to the prediction
of gold deposits based on 16 predictor variables shows that its predictive performance far exceeds that of conventional density
estimation methods, and is slightly better than the performance of a discriminative approach based on multilayer perceptron
neural networks. 相似文献
7.
Brandmeier Melanie Cabrera Zamora Irving Gibran Nykänen Vesa Middleton Maarit 《Natural Resources Research》2020,29(1):71-88
With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.
相似文献8.
V.F. Rodriguez-Galiano M. Chica-Olmo M. Chica-Rivas 《International journal of geographical information science》2014,28(7):1336-1354
Mineral exploration activities require robust predictive models that result in accurate mapping of the probability that mineral deposits can be found at a certain location. Random forest (RF) is a powerful machine data-driven predictive method that is unknown in mineral potential mapping. In this paper, performance of RF regression for the likelihood of gold deposits in the Rodalquilar mining district is explored. The RF model was developed using a comprehensive exploration GIS database composed of: gravimetric and magnetic survey, a lithogeochemical survey of 59 elements, lithology and fracture maps, a Landsat 5 Thematic Mapper image and gold occurrence locations. The results of this study indicate that the use of RF for the integration of large multisource data sets used in mineral exploration and for prediction of mineral deposit occurrences offers several advantages over existing methods. Key advantages of RF include: (1) the simplicity of parameter setting; (2) an internal unbiased estimate of the prediction error; (3) the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; (4) the capability to use categorical predictors; and (5) the capability to determine variable importance. Additionally, variables that RF identified as most important coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, gold prospectivity maps are also prepared using the logistic regression (LR) method. Statistical measures of map quality indicate that the RF method performs better than LR, with mean square errors equal to 0.12 and 0.19, respectively. The efficiency of RF is also better, achieving an optimum success rate when half of the area predicted by LR is considered. 相似文献
9.
John R. Dymond Paul G. Luckman 《International journal of geographical information science》2013,27(4):357-367
Abstract Rule-based classifiers are used regularly with geographical information systems to map categorical attributes on the basis of a set of numeric or unordered categorical attributes. Although a variety of methods exist for inducing rule-based classifiers from training data, these tend to produce large numbers of rules when the data has noise. This paper describes a method for inducing compact rule-sets whose classification accuracy can, at least in some domains, compare favourably with that achieved by larger less succinct rule-sets produced by alternative methods. One rule is induced for each output class. The condition list for this rule represents a box in n-dimensional attribute space, formed by intersecting conditions which exclude other classes. Despite this simplicity, the classifier performed well in the test application prediction of soil classes in the Port Hills, New Zealand, on the basis of regolith type and topographic attributes obtained from a digital terrain model. 相似文献
10.
Use of a Probabilistic Neural Network to Reduce Costs of Selecting Construction Rock 总被引：2，自引：0，他引：2
Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early. 相似文献
11.
The Pb–Zn sulfide concentrations hosted by dolomitized Cambrian carbonates in Southeast Missouri are world-class Mississippi Valley Type (MVT) deposits. These deposits commonly are in sites where local Precambrian basement highs resulted in depositional pinchouts of the basal Cambrian sandstones that served as a regional aquifer for basinal fluid migration driven by late Paleozoic Ouachita deformation. Mineralization also appears to be spatially related to regional faults that probably served as local fluid conduits. Understanding spatial associations between sites of known mineralization and regional geology, geochemistry, and geophysics in Southeast Missouri will be a useful guide in future exploration efforts in this region and for similar geologic settings globally. The weights-of-evidence method is used to evaluate regional geology, geochemistry, and geophysical datasets and produce favorability maps for MVT deposits in Southeast Missouri. Host rock characteristics, regional structural controls, stream sediment geochemistry, and proximity to basement highs appear to be the most useful data for predicting the location of the major deposits. This work illustrates the potential utility of mineral potential modeling to prioritize areas for exploration and identify permissive areas for undiscovered MVT mineralization.
相似文献12.
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
13.
A non-parametric method for supervised pattern recognition is presented. The method is of the classmodelling type, meaning that a classification rule is developed for each class, using the dissimilaritiesbetween the objects of the class. The dissimilarities between the objects within a class are related to thedistances between all pairs of training objects. As distance metric, a measure is proposed that takes thecorrelation between the interval-scale variables into account, and that moreover can be used for mixedtypes of variables. The classification rule is based on the construction of a boundary in the measurementspace. For the determination of the class boundary, several strategies are proposed and compared. The performance of the technique is evaluated on the basis of several data sets. Comparison with theclass modelling technique UNEQ shows its usefulness for practical applications. 相似文献
14.
Abera Tessema 《Natural Resources Research》2017,26(4):465-488
Radial basis function link neural network (RBFLN) and fuzzy-weights of evidence (fuzzy-WofE) methods were used to assess regional-scale prospectivity for chromite deposits in the Western Limb and the Nietverdiend layered mafic intrusion of the Bushveld Complex in South Africa. Five predictor maps derived from geological, geochemical and geophysical data were processed in a GIS environment and used as spatial proxy for critical processes that were most probably responsible for the formation of the chromite deposits in the study area. The RBFLN was trained using input feature vectors that correspond to known deposits, prospects and non-deposits. The training was initiated by varying the number of radial basis functions (RBFs) and iterations. The results of training the RBFLN provided optimum number of RBFs and iterations that were used for classification of the input feature vectors. The results show that the network classified 73% of the validation deposits into highly prospective areas for chromite deposit, covering 6.5% of the study area. The RBFLN entirely classified all the non-deposit validation points into low prospectivity areas, occupying 86.6% of the study area. In general, the efficiency of the RBFLN in classifying the validation deposits and non-deposits indicates the degree of spatial relationship between the input feature vectors and the training points, which represent chrome mines and prospects. The RBFLN and fuzzy-WofE analyses used in this study are important in guiding identification of regional-scale prospect areas where further chromite exploration can be carried out. 相似文献
15.
Dennis P. Cox 《Natural Resources Research》1993,2(2):82-91
Quantitative mineral resource assessments used by the United States Geological Survey are based on deposit models. These assessments consist of three parts: (1) selecting appropriate deposit models and delineating on maps areas permissive for each type of deposit; (2) constructing a grade-tonnage model for each deposit model; and (3) estimating the number of undiscovered deposits of each type. In this article, I focus on the estimation of undiscovered deposits using two methods: the deposit density method and the target counting method.In the deposit density method, estimates are made by analogy with well-explored areas that are geologically similar to the study area and that contain a known density of deposits per unit area. The deposit density method is useful for regions where there is little or no data. This method was used to estimate undiscovered low-sulfide gold-quartz vein deposits in Venezuela.Estimates can also be made by counting targets such as mineral occurrences, geophysical or geochemical anomalies, or exploration plays and by assigning to each target a probability that it represents an undiscovered deposit that is a member of the grade-tonnage distribution. This method is useful in areas where detailed geological, geophysical, geochemical, and mineral occurrence data exist. Using this method, porphyry copper-gold deposits were estimated in Puerto Rico. 相似文献
16.
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
17.
This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor
patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership
values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic
uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns
are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior
probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model
is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic
province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently
to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits.
An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence
in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit
occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation
and in integration of evidential features to map relative potential for mineral deposit occurrence. 相似文献
18.
PETER B.HARRINGTON 《地理学报(英文版)》1991,(5)
A fuzzy multivariate rule-building expert system (FuRES) has been devised which also functions as aminimal neural network.This system builds rules from training sets of data that use featuretransformation in their antecedents.The rules are constructed using the ID3 algorithm with a fuzzyexpression of classification entropy.The rules are optimal with respect to fuzziness and can accommodateoverlapped and underlapped clusters of data.The FuRES algorithm combines the benefits obtained fromsimulated annealing and gradient optimization,which provide robustness and efficiency respectively.FARES classification trees support OR logic in their inference.The system automatically generatesmeaningful and consistent certainty factors during rule construction.Unlike other neural networks,FuRES uses local processing which furnishes qualitative information in the rule structure of itsclassification trees and variable loadings of the weight vectors. 相似文献
19.
Pouran Behnia 《Natural Resources Research》2007,16(2):157-158
The metallogeny of Central Iran is characterized mainly by the presence of several iron, apatite, and uranium deposits of
Proterozoic age. Radial Basis Function Link Networks (RBFLN) were used as a data-driven method for GIS-based predictive mapping
of Proterozoic mineralization in this area. To generate the input data for RBFLN, the evidential maps comprising stratigraphic,
structural, geophysical, and geochemical data were used. Fifty-eight deposits and 58 ‘nondeposits’ were used to train the
network. The operations for the application of neural networks employed in this study involve both multiclass and binary representation
of evidential maps. Running RBFLN on different input data showed that an increase in the number of evidential maps and classes
leads to a larger classification sum of squared error (SSE). As a whole, an increase in the number of iterations resulted
in the improvement of training SSE. The results of applying RBFLN showed that a successful classification depends on the existence
of spatially well distributed deposits and nondeposits throughout the study area.
An erratum to this article can be found at 相似文献
20.
基于神经网络的单元自动机CA及真实和优化的城市模拟 总被引：78，自引：8，他引：78
提出了一种基于神经网络的单元自动机（CA）。CA已被越来越多地应用在城市及其它地理现象的模拟中。CA模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。 相似文献