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1.
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.
Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.  相似文献   

3.
Abstract

Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.  相似文献   

4.
Mineral prospectivity mapping is an important preliminary step for mineral resource exploration. It has been widely applied to distinguish areas of high potential to host mineral deposits and to minimize the financial risks associated with decision making in mineral industry. In the present study, a maximum entropy (MaxEnt) model was applied to investigate its potential for mineral prospectivity analysis. A case study from the Nanling tungsten polymetallic metallogenic belt, South China, was used to evaluate its performance. In order to deal with model over-fitting, varying levels of β j -regularization were set to determine suitable β value based on response curves and receiver operating characteristic (ROC) curves, as well as via visual inspections of prospectivity maps. The area under the ROC curve (AUC = 0.863) suggests good performance of the MaxEnt model under the condition of balancing model complexity and generality. The relative importance of ore-controlling factors and their relationships with known deposits were examined by jackknife analysis and response curves. Prediction–area (P–A) curves were used to determine threshold values for demarcating high probability of tungsten polymetallic deposit occurrence within small exploration area. The final predictive map showed that high favorability zones occupy 14.5% of the study area and contain 85.5% of the known tungsten polymetallic deposits. Our study suggests that the MaxEnt model can be efficiently used to integrate multisource geo-spatial information for mineral prospectivity analysis.  相似文献   

5.
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   

6.
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  
黎夏  叶嘉安 《地理学报》2002,57(2):159-166
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   

7.
Estimating the number of deposits likely to be found and their size distribution is important in exploration planning. In a frontier basin the geologic information for conducting a resource assessment is mostly limited to the regional level. Some methods, such as the play analysis approach (Crovelli and Balay, 1986) and the conceptual play model (Lee and Wang, 1983), can be used in conceptual plays or frontier basins, but these methods require the knowledge of the number of prospects, pool data characterizing the conditional field size distribution, and information documenting the exploration risk. For unexplored sedimentary basins, such as those in southern Africa, there are neither sufficient data covering reservoir volumetric parameters nor a number of mapped prospects that can be used to conduct such an assessment. In such a case a volumetric method using geologic analogy is applicable, by which a point estimate of hydrocarbon potential can be estimated, but this estimate provides no information on the likely size distribution. As a complement to the volumetric method, we discuss how to use the empirical Pareto law for estimating the number of fields and their size distribution in such a frontier region.  相似文献   

8.
The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73.06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPML and MPMW gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPIL and MPIW.  相似文献   

9.
The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of –1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.  相似文献   

10.
中国男性儿童呼气高峰流量参考值地理分布   总被引：2，自引：0，他引：2  
为弥补健康男性儿童肺部呼气高峰流量参考值制定时忽略地理因素的影响,分析了健康男性儿童肺部呼气高峰流量参考值与地理因素的关系。收集中国各地健康男性儿童呼气高峰流量参考值,运用相关分析将健康男性儿童肺部呼气高峰流量参考值与选取的25项地理因素指标进行了研究,提取其中存在相关性的10项地理因素进行进一步分析。以空间自相关Moran’s指数分析数据,确定数据与空间及地理因素存在关系。用选定的10项指标进行BP人工神经网络与地理要素模拟分析。通过5层神经网络,选取含9个隐含层的1000次训练自学习建立模拟规则,用此规则模拟健康男性儿童呼气高峰流量参考值与地理环境的神经网络模型。运用ArcGIS地统计分析对数据进行分布检测,选取析取克里金法进行插值并输出参考值的地理分布图。研究表明神经网络预测与地统计插值可以很好的结合进行插值出图,分析出中国男性儿童肺部呼气高峰流量参考值与经度、海拔高度、年平均气温、年平均相对湿度、年平均风速、表土石砾含量、表土有机质含量、表土（粘土）阳离子交换量、表土（粉土）阳离子交换量、表土总可交换量存在关系。同时,分析了地理因素与医学指标间的关系,对其影响机制进行了讨论。  相似文献   

11.
A geologic anomaly is a geologic feature or structure that departs markedly from its surrounding environment with respect to composition, texture, or genesis. The analysis of geologic anomalies related to mineralization is based upon specific geologic factors and a combination of features, such as structural, temporal, and spatial, and draws upon special effects that are due to ore-forming processes. An analysis of the geologic anomalies in the, middle-lower Yangtze area in southeastern China has led to an interpretation of the relation between anomaly subtypes and the occurrence and spatial distribution of ore deposits. Consequently, the following conclusions have been reached: the type of anomaly reflects the controlling factors that led to the formation of iron, copper, and gold deposits in the area; sedimentary geologic anomalies are most closely associated with stratiform deposits; structral complexity anomalies are most closely associated with Cu−Fe−Au deposits; magmatic anomalies reflect geologic processes in which Fe and Cu elements were separated from magma and enriched into ores; and the geologic combination entropy anomaly is proposed as a comprehensive variable that is related to favorable ore-forming environments and that can serve as a quantitative index that can be used in mineral exploration.  相似文献   

12.

The potential for mining hydrothermal mineral deposits on the seafloor, such as seafloor massive sulfides, has become technically possible, and some companies (currently not many) are considering their exploration and development. Yet, no present methodology has been designed to quantify the ore potential and assess the risks relative to prospectivity at prospect and regional scales. Multi-scale exploration techniques, similar to those of the play analysis that are used in the oil and gas industry, can help to fulfill this task by identifying the characteristics of geologic environments indicative of ore-forming processes. Such characteristics can represent a combination of, e.g., heat source, pathway, trap and reservoir that all dictate how and where ore components are mobilized from source to deposition. In this study, the understanding of these key elements is developed as a mineral system, which serves as a guide for mapping the risk of the presence or absence of ore-forming processes within the region of interest (the permissive tract). The risk analysis is carried out using geoscience data, and it is paired with quantitative resource estimation analysis to estimate the in-place mineral potential. Resource estimates are simulated stochastically with the help of available data (bathymetric features in this study), conventional grade–tonnage models and Monte Carlo simulation techniques. In this paper, the workflow for a multi-scale quantitative risk analysis, from the definition to the evaluation of a permissive tract and related prospect(s), is described with the help of multi-beam data of a known hydrothermal vent site.

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13.
Huang  Jixian  Mao  Xiancheng  Chen  Jin  Deng  Hao  Dick  Jeffrey M.  Liu  Zhankun 《Natural Resources Research》2020,29(1):439-458

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.

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14.
ABSTRACT

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.  相似文献   

15.
Exploration for volcanogenic massive sulfide deposits of the kuroko-type is underway in many places. Clarifying the spatial patterns of the metals in kuroko deposits will be useful for understanding their genetic mechanisms and for future exploration of such types of deposits. This study represents a spatial distribution analysis on the contents of principal metals of kuroko deposits: Cu, Pb, and Zn, in the Hokuroku district, northern Japan, by a feedforward neural network and 1917 sample data at 143 drillhole sites. The network, which consists of three layers, was trained by the principle of SLANS in which the numbers of neurons in the middle layer and training data are changed to improve estimation accuracy. Using the weight coefficients connecting adjacent neurons, sensitivity analysis of the neural network was carried out to identify factors influencing spatial distributions of the three metals. The coordinates depth (z) direction, Bouguer gravity, and specific lithology such as dacite were determined to be influencing factors. The high frequency of the z coordinate signifies that the metal contents differ to a large extent by depth. The sensitivity vector was defined using sensitivity coefficients for x, y, and z coordinates of an estimation point. We determined that the directions of large vectors were different inside and outside of the Hanawa-Ohdate area. This characteristic is considered to originate from the differences in the permeability of fractures that became the paths for rising ore solutions, and the depths that the solutions mixed with sea water.  相似文献   

16.

In this study, deposit- and district-scale three-dimensional (3D) fault-and-intrusion structure models were constructed, based on which a numerical simulation was implemented in the Jiaojia gold district, China. The numerical simulation of the models shows the basic metallogenic path and trap of the gold deposits using mineral system theory. The objective of this study was to delineate the uncertainty of the geometry or buffer zones of the ore-forming and ore-controlling fault-and-intrusion domains in 3D environment representing the exploration criteria extraction and the gold potential targeting in the study area. The fast Lagrangian analysis of continua in three dimensions was used as the platform to define the stress deformation fracture ore storage and the hydrothermal seepage channel zone based on the gold deposit features and metallogenic model in the study area. The validity of the numerical simulation was verified by comparing it with robust 3D geological models of the large Xincheng gold deposit. The potential targeting zones are analyzed for uncertainty and then evaluated by Boolean operation in a 3D geological model using the computer-aided design platform. The research results are summarized as follows. (1) In the pre-mineralization period, the Jiaodong fault’s left lateral movement created the Jiaojia network faults and formed a fracture zone with NW- to NNW-trending dips of 20° to 40°. (2) During the mineralization period, hydrothermal flow was associated with the intrusion geometry and features. However, it was constrained by the Jiaojia fault, which blocked the vadose flow into the upper wall rock and made the hydrothermal route close to the fault in the footwall fracture zones. (3) Three gold potential targets were identified by the numerical simulation results in the study area: the NW-trending Sizhuang gold deposit, the NW-trending zone of Jiaojia gold deposit, and the NE-trending zone of the Xincheng gold deposit. (4) The numerical simulation results show the fault-and-intrusion metallogenic domain and the hydrothermal alteration zones, which reflect the main ore-controlling and ore-forming factors of mineralization. The information obtained through the numerical simulation discussed here can be used to define exploration criteria in the study area.

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17.
《The Journal of geography》2012,111(8):385-390
Abstract

An intensive and comprehensively planned program of mineral exploration and discovery is the most important single factor in maintaining an adequate supply of available minerals.

This program requires the services of a large staff of geologists and geophysicists adequately equipped with the essential instruments of exploration.

Exploration, even tho it results in substantial discoveries, will not be adequate without the aid of the miners, metallurgists, engineers, architects, and contributions from deposits in foreign countries.

Developments in mining—such as diamond drilling, block caving, and removal of overburden—have contributed toward making ore deposits of low grade economically exploitable, thereby increasing the supply of metal that can be made available.  相似文献   

18.
A new and efficient method for fault seal analysis using seismic data is presented. It uses multiple seismic attributes and neural networks to enhance fluid migration pathways, including subtle features that are not detectable using single attributes only. The method may be used as a first estimate of fault seal or to calibrate results from other techniques. The results provide information about which faults and fault segments are sealing or leaking. Fluid flow along individual faults appears to be focused along zones of weakness, and fault seal research should thus be focused on finding such weak locations within fault zones, a task that is best done using three‐dimensional (3D) seismic data. Under certain conditions, it is suggested that fluids migrate along fault planes by a diapiric fluid flow mechanism. The results assist in calibrating the bulk hydraulic properties of faults and rock formations and can be used in basin modelling.  相似文献   

19.
基于神经网络的元胞自动机及模拟复杂土地利用系统   总被引：57，自引：9，他引：57  
黎夏  叶嘉安 《地理研究》2005,24(1):19-27
本文提出了基于神经网络的元胞自动机(CellularAutomata),并将其用来模拟复杂的土地利用系统及其演变。国际上已经有许多利用元胞自动机进行城市模拟的研究,但这些模型往往局限于模拟从非城市用地到城市用地的转变。模拟多种土地利用的动态系统比一般模拟城市演化要复杂得多,需要使用许多空间变量和参数,而确定模型的参数值和模型结构有很大困难。本文通过神经网络、元胞自动机和GIS相结合来进行土地利用的动态模拟,并利用多时相的遥感分类图像来训练神经网络,能十分方便地确定模型参数和模型结构,消除常规模拟方法所带来的弊端。  相似文献   

20.
Conventional evaluation of quantitative mineral potential has focused on target selection at small scales. Mapping at small scales usually results in large-area targets, which may be suitable for grass-roots exploration or regional evaluation of potential. Unfortunately, the estimates in small-scale exploration are commonly associated with large uncertainties. Large-scale estimation is used for optimal in-fill drilling design and step-out drilling target selection. In-fill drilling helps to confirm ore-grade continuities and translate a portion of geological resources into minable reserves, whereas step-out target estimation is useful for finding new orebodies in the vicinity of known ore deposits. Both of these processes are necessary for mine development and production planning. A comprehensive methodology is proposed here, particularly for large-scale mineral exploration. The central information synthesizer is canonical or indicator favorability analysis. A case study is presented to demonstrate the methodology for large-scale target selection. The study involves a gold-mining district where step-out drilling targets are being sought to expand the resource base. Several drilling targets were delineated in the study region. Two of them were tested through surface sampling with positive results.  相似文献   

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