共查询到20条相似文献，搜索用时 578 毫秒
1.
The Bayesian approach is an effective method of identifying the probability of mineralogical and geochemical type (MGT) mineralization of trace elements in galena, pyrite and other distributions in ore mineralization. Monomineralic samples have been identified using a computer-based Bayesian method and exploration geochemical techniques of Au deposits for MGT. In order to employ the method, a data bank was used consisting of the results of analysis of more than 12,000 monomineralic samples collected from the main hydrothermal Au deposits in Tajikistan (a territory of CIS). The Bayesian approach applied to geochemical data, such as posterior probabilities and discriminant analysis, provide numerical and graphical means through which the relationships between the trace elements and samples can be studied. The method used here, along with GIS, to find MGT can be used as geochemical indicators of regions with Au mineralization. The results of analyzing 100 monomineralic samples of pyrite from the Au–Ag Shkolnoe deposit (Tajikistan) show a multi-MGT anomaly superposition which is a combination of three MGT: (1) Au–Ag type (85% and more), (2) Au–sulfide-polymetallic type (46%), and (3) Au–sulfide type (40%). Mineralogical and geochemical maps (MGM) can be drawn based on results of MGT anomalies in a GIS environment. These maps can replace traditional metallogenic maps. The advantage of MGM substitutions is that a qualitative tool is replaced by a quantitative one. This helps one to make optimal managerial and more economical decisions. 相似文献
2.
Aykut Akgun 《Landslides》2012,9(1):93-106
The main purpose of this study is to compare the use of logistic regression, multi-criteria decision analysis, and a likelihood
ratio model to map landslide susceptibility in and around the city of İzmir in western Turkey. Parameters, such as lithology,
slope gradient, slope aspect, faults, drainage lines, and roads, were considered. Landslide susceptibility maps were produced
using each of the three methods and then compared and validated. Before the modeling and validation, the observed landslides
were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy
of models was measured by fitting them to a validation set of observed landslides. For validation process, the area under
curvature (AUC) approach was applied. According to the AUC values of 0.810, 0.764, and 0.710 for logistic regression, likelihood
ratio, and multi-criteria decision analysis, respectively, logistic regression was determined to be the most accurate method
among the other used landslide susceptibility mapping methods. Based on these results, logistic regression and likelihood
ratio models can be used to mitigate hazards related to landslides and to aid in land-use planning. 相似文献
3.
Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model 总被引：3，自引：0，他引：3
An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor??s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09?%. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration. 相似文献
4.
This method of assigning weights based on expert opinion introduces bias when we are evaluating the relative importance of evidence values. In this paper, we used a prediction–area (P–A) plot method and content–area (C–A) fractal model to estimate the weight of each evidence map. In this paper, we used the content region (C–A) fractal model to divide the evidence maps to the threshold of the corresponding dimensions. The P–A plot approach is an objective data-driven approach for evaluating map weights. Using geochemical layer and remote sensing, hydroxyl layers as weight evidence maps are the highlights of this study. We use the P–A method from which we can evaluate the predictive ability of each evidence map with respect to the known ore occurrences. We used the P–A plot for weighting each evidence map and choosing the appropriate threshold for predictor maps in the Luchun area of Yunnan Province, China. The method adopted in this paper can improve the prediction efficiency of ore prospecting. 相似文献
5.
Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples. 相似文献
6.
模糊证据权方法在镇沅(老王寨)地区金矿资源评价中的应用 总被引：11，自引：0，他引：11
采用模糊证据权方法和GeoDASGIS技术开展了镇沅(老王寨)及其邻区的金矿资源潜力评价.分别采用GeoDASGIS软件提供的局部奇异性分析技术、S-A异常分解技术、主成分分析技术、证据权、模糊证据权等技术对相关地球化学元素进行了系统的处理和分析.应用主成分分析方法确定了可能的2种不同成矿类型,并采用主成分得分确定了组合异常点,在此基础上分别采用普通证据权和模糊证据权方法编制了成矿后验概率图,圈定了有利成矿地段.对比普通证据权方法与模糊证据权方法所得结果表明,模糊证据权方法可减小图层离散化造成的有用信息损失,提高预测结果精度. 相似文献
7.
There is growing interest in the use of back‐propagation neural networks to model non‐linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back‐propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back‐propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back‐propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
8.
基于模糊预测对象的证据权方法及其在土地沙漠化评价中的应用 总被引：3，自引：3，他引：0
为了使在地理信息系统中被广泛用于点事件预测的证据权方法能对面事件进行评价和预测, 提出了一种新的基于模糊训练层的证据权方法.它是一种更广泛的证据权方法, 与普通证据权方法所不同的是, 它的训练层是模糊集合, 其取值是它的隶属度.通过适当的变换也可以把点训练层转换为模糊集合.因此, 该方法可以对面事件、点事件和线事件进行评价和预测.该方法可以处理训练层和证据层均为模糊集合的情况, 被称为双重模糊证据权方法.作为该方法的一个应用实例, 本文介绍毛乌素沙漠边缘的晋陕蒙地区土地沙漠化评价的应用实例. 相似文献
9.
Landslides are one of the most destructive phenomena of nature that cause damage to both property and life every year, and therefore, landslide susceptibility zonation (LSZ) is necessary for planning future developmental activities. In this paper, apart from conventional weighting system, objective weight assignment procedures based on techniques such as artificial neural network (ANN), fuzzy set theory and combined neural and fuzzy set theory have been assessed for preparation of LSZ maps in a part of the Darjeeling Himalayas. Relevant thematic layers pertaining to the causative factors have been generated using remote sensing data, field surveys and Geographic Information System (GIS) tools. In conventional weighting system, weights and ratings to the causative factors and their categories are assigned based on the experience and knowledge of experts about the subject and the study area to prepare the LSZ map (designated here as Map I). In the context of objective weight assignments, initially the ANN as the black box approach has been used to directly produce an LSZ map (Map II). In this approach, however, the weights assigned are hidden to the analyst. Next, the fuzzy set theory has then been implemented to determine the membership values for each category of the thematic layer using the cosine amplitude method (similarity method). These memberships are used as ratings for each category of the thematic layer. Assuming weights of each thematic layer as one (or constant), these ratings of the categories are used for the generation of another LSZ map (Map III). Subsequently, a novel weight assignment procedure based on ANN is implemented to assign the weights to each thematic layer objectively. Finally, weights of each thematic layer are combined with fuzzy set derived ratings to produce another LSZ map (Map IV). The maps I–IV have been evaluated statistically based on field data of existing landslides. Amongst all the procedures, the LSZ map based on combined neural and fuzzy weighting (i.e., Map IV) has been found to be significantly better than others, as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area. 相似文献
10.
11.
In this study, both the fuzzy weights of evidence (FWofE) and random forest (RF) methods were applied to map the mineral prospectivity for Cu polymetallic mineralization in southwestern Fujian Province, which is an important Cu polymetallic belt in China. Recent studies have revealed that the Zijinshan porphyry–epithermal Cu deposit is associated with Jurassic to Cretaceous (Yanshanian) intermediate to felsic intrusions and faulting tectonics. Evidence layers, which are key indicators of the formation of Zijinshan porphyry–epithermal Cu mineralization, include: (1) Jurassic to Cretaceous intermediate–felsic intrusions; (2) mineralization-related geochemical anomalies; (3) faults; and (4) Jurassic to Cretaceous volcanic rocks. These layers were determined using spatial analyses in support by GeoDAS and ArcGIS based on geological, geochemical, and geophysical data. The results demonstrated that most of the known Cu occurrences are in areas linked to high probability values. The target areas delineated by the FWofE occupy 10% of the study region and contain 60% of the total number of known Cu occurrences. In comparison with FWofE, the resulting RF areas occupy 15% of the study area, but contain 90% of the total number of known Cu occurrences. The normalized density value of 1.66 for RF is higher than the 1.15 value for FWofE, indicating that RF performs better than FWofE. Receiver operating characteristics (ROC) were used to validate the prospectivity model and check the effects of overfitting. The area under the ROC curve (AUC) was greater than 0.5, indicating that both prospectivity maps are useful in Cu polymetallic prospectivity mapping in southwestern Fujian Province. 相似文献
12.
We present a mineral systems approach to predictive mapping of orogenic gold prospectivity in the Giyani greenstone belt (GGB) by using layers of spatial evidence representing district-scale processes that are critical to orogenic gold mineralization, namely (a) source of metals/fluids, (b) active pathways, (c) drivers of fluid flow and (d) metal deposition. To demonstrate that the quality of a predictive map of mineral prospectivity is a function of the quality of the maps used as sources of spatial evidence, we created two sets of prospectivity maps — one using an old lithologic map and another using an updated lithological map as two separate sources of spatial evidence for source of metals/fluids, drivers of fluid flow and metal deposition. We also demonstrate the importance of using spatially-coherent (or geologically-consistent) deposit occurrences in data-driven predictive mapping of mineral prospectivity. The best predictive orogenic gold prospectivity map obtained in this study is the one that made use of spatial evidence from the updated lithological map and spatially-coherent orogenic gold occurrences. This map predicts 20% of the GGB to be prospective for orogenic gold, with 89% goodness-of-fit between spatially-coherent inactive orogenic gold mines and individual layers of spatial evidence and 89% prediction-rate against spatially-coherent orogenic gold prospects. In comparison, the predictive gold prospectivity map obtained by using spatial evidence from the old lithological map and all gold occurrences has 80% goodness-of-fit but only 63% prediction-rate. These results mean that the prospectivity map based on spatially-coherent gold occurrences and spatial evidence from the updated lithological map predicts exploration targets better (i.e., 28% smaller prospective areas with 9% stronger fit to training gold mines and 26% higher prediction-rate with respect to validation gold prospects) than the prospectivity map based on all known gold occurrences and spatial evidence from the old lithological map. 相似文献
13.
W. M. Brown T. D. Gedeon D. I. Groves R. G. Barnes 《Australian Journal of Earth Sciences》2013,60(4):757-770
A multilayer feed‐forward neural network, trained with a gradient descent, back‐propagation algorithm, is used to estimate the favourability for gold deposits using a raster GIS database for the Tenterfield 1:100 000 sheet area, New South Wales. The database consists of solid geology, regional faults, airborne magnetic and gamma‐ray survey data (U, Th, K and total count channels), and 63 deposit and occurrence locations. Input to the neural network consists of feature vectors formed by combining the values from co‐registered grid cells in each GIS thematic layer. The network was trained using binary target values to indicate the presence or absence of deposits. Although the neural network was trained as a binary classifier, output values for the trained network are in the range [0.1, 0.9] and are interpreted to indicate the degree of similarity of each input vector to a composite of all the deposit vectors used in training. These values are rescaled to produce a multiclass prospectivity map. To validate and assess the effectiveness of the neural‐network method, mineral‐prospectivity maps are also prepared using the empirical weights of evidence and the conceptual fuzzy‐logic methods. The neural‐network method produces a geologically plausible mineral‐prospectivity map similar, but superior, to the fuzzy logic and weights of evidence maps. The results of this study indicate that the use of neural networks for the integration of large multisource datasets used in regional mineral exploration, and for prediction of mineral prospectivity, offers several advantages over existing methods. These include the ability of neural networks to: (i) respond to critical combinations of parameters rather than increase the estimated prospectivity in response to each individual favourable parameter; (ii) combine datasets without the loss of information inherent in existing methods; and (iii) produce results that are relatively unaffected by redundant data, spurious data and data containing multiple populations. Statistical measures of map quality indicate that the neural‐network method performs as well as, or better than, existing methods while using approximately one‐third less data than the weights of evidence method. 相似文献
14.
GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang,China 总被引：6，自引：2，他引：4
Shibiao Bai Guonian Lü Jian Wang Pinggen Zhou Liang Ding 《Environmental Earth Sciences》2011,62(1):139-149
Landslides have had a huge effect on human life, the environment and local economic development, and therefore they need to
be well understood. In this study, we presented an approach for the analysis and modeling of landslide data using rare events
logistic regression and applied the approach to an area in Lianyungang, China. Digital orthophotomaps, digital elevation models
of the region, geological maps and different GIS layers including settlement, road net and rivers were collected and applied
in the analysis. Landslides were identified by monoscopic manual interpretation and validated during the field investigation.
To validate the quality of mapping, the data from the study area were divided into a training set and validation set. The
result map showed that 4.26% of the study area was identified as having very high susceptibility to landslides, whereas the
others were classified as having very low susceptibility (47.2%), low susceptibility (22.21%), medium susceptibility (14.39%)
and high susceptibility (11.93%). The quality of the landslide-susceptibility map produced in this paper was validated, and
it can be used for planning protective and mitigation measures. The landslide-susceptibility map is a fundamental part of
the Lianyungang city landslide risk assessment. 相似文献
15.
Saeedeh Eskandari 《Arabian Journal of Geosciences》2017,10(8):190
The presented research was performed in order to model the fire risk in a part of Hyrcanian forests of Iran. The fuzzy sets integrated with analytic hierarchy process (AHP) in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. The used factors included four major criteria (topographic, biologic, climatic, and human factors) and their 17 sub-criteria. Fuzzy AHP method was used for estimating the importance (weight) of the effective factors in forest fire. Based on this modeling method, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas mean was analyzed based on fuzzy extent analysis. Then, the fuzzy weights of criteria and sub-criteria were obtained. The major criteria models and fire risk model were presented based on these fuzzy weights. On the other hand, the spatial data of 17 sub-criteria were provided and organized in GIS to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risk of its classes to fire occurrence. Then, all sub-criteria maps were converted to fuzzy format using fuzzy membership function in GIS. The fuzzy map of each major criterion (topographic, biologic, climatic, and human criteria) was obtained by weighted overlay of its sub-criteria fuzzy maps considering to major criterion model in GIS. Finally, the fuzzy map of fire risk was obtained by weighted overlay of major criteria fuzzy maps considering to fire risk model in GIS. The actual fire map was used for validation of fire risk model and map. The results showed that the fuzzy estimated weights of human, biologic, climatic, and topographic criteria in fire risk were 0.301, 0.2595, 0.2315, and 0.208, respectively. The results obtained from the fire risk map showed that 38.74% of the study area has very high and high risk for fire occurrence. Results of validation of the fire risk map showed that 80% of the actual fires were located in the very high and high risk areas in fire risk map. It can show the acceptable accuracy of the fire risk model and map obtained from fuzzy AHP in this study. The obtained fire risk map can be used as a decision support system for predicting of the future fires in the study area. 相似文献
16.
The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea 总被引：13，自引：0，他引：13
The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility. 相似文献
17.
A major challenge for mineral exploration geologists is the development of a transparent and reproducible approach to targeting exploration efforts, particularly at the regional to camp scales, in terranes under difficult cover where exploration and opportunity costs are high. In this study, a three-pronged approach is used for identifying the most prospective ground for orogenic gold deposits in the Paleoproterozoic Granite-Tanami Orogen (GTO) in Western Australia.A key input to the analyses is the recent development of a 4D model of the GTO architectural evolution that provides new insights on the spatio-temporal controls over orogenic gold occurrences in the area; in particular, on the role of pre-mineralization (pre-1795 Ma) DGTOE–DGTO1–DGTO2 architecture in localization of gold deposits and the spatial distribution of rock types in 3D. This information is used to build up a model of orogenic gold minerals system in the area, which is then integrated into the three mutually independent but complementary mineral prospectivity maps namely, a concept-driven “manual” and “fuzzy” analysis; and a data-driven “automated” analysis.The manual analysis involved: (1) generation of a process-based gold mineral systems template to aid target selection; (2) manual delineation of targets; (3) manual estimation of the probability of occurrence of each critical mineralization process based on the available information; and (4) combining the above probabilities to derive the relative probability of occurrence of orogenic gold deposits in each of the targets. The knowledge-based Geological Information System (GIS) analysis attempts to replicate the expert knowledge used in the manual approach, but queried in a more systematic format to eliminate human heuristic bias. This involves representing the critical mineralization processes in the form of spatial predictor maps and systematically querying them through the use of a fuzzy logic model to integrate the predictor maps and to derive the western GTO orogenic gold prospectivity map. The data-driven ‘empirical’ GIS analysis uses no expert knowledge. Instead it employs statistical measures to evaluate the spatial associations between known deposits and predictor maps to establish weights for each predictor layer then combines these layers into a predictive map using a Weights of Evidence (WofE) approach.Application of a mineral systems approach in the manual analysis and the fuzzy analysis is critical: potential high value targets identified by these approaches in the western GTO lie largely under cover, whereas traditional manual targeting is biased to areas of outcrop or sub-crop amenable to direct detection technology such as exploration geochemistry, and therefore towards areas that are data rich.The results show the power of combining the three approaches to prioritize areas for exploration. While the manual analysis identifies and employs human intuition and can see through incomplete datasets, it is difficult to filter out human bias and to systematically apply to a large region. The fuzzy method is more systematic, and highlights areas that the manual analysis has undervalued, but lacks the intuitive power of the human mind that refines the target by seeing through incomplete datasets. The empirical WoE method highlights correlations with favorable host stratigraphy and highlights the control of an early set of structures potentially undervalued in the knowledge driven approaches, yet is biased due to the incomplete nature of exploration datasets and lack of abundant gold deposits due to the extensive cover.The results indicate that the most prospective areas for orogenic gold in western GTO are located in the central part of the study area, largely in areas blind to previous exploration efforts. According to our study, the procedure to follow should be to undertake the analyses in the following order: manual prospectivity analysis, followed by the conceptual fuzzy approach, followed by the empirical GIS-based method. Undertaking the manual analysis first is important to prevent explorationists from being biased by the automated GIS-based outputs. It is however emphasized that all of the prospectivity outputs from these three methods are possible, and they should not be treated as ‘treasure maps’, but instead, as decision-support aids. Therefore, a final manual prospectivity analysis redefined by the mutual consideration of output from all of the methods is required.The strategy employed in this study constitutes a new template for best-practice in terrane- to camp-scale exploration targeting that can be applied to different terranes and deposit types, particularly in terranes under cover, and provides a step forward in managing uncertainty in the exploration targeting process. 相似文献
18.
Andréhette Verster Daan de Waal Robert Schall Chris Prins 《Mathematical Geosciences》2012,44(1):91-100
The metallurgical recovery processes in diamond mining may, under certain circumstances, cause an under-recovery of large
diamonds. In order to predict high quantiles or tail probabilities we use a Bayesian approach to fit a truncated Generalized
Pareto Type distribution to the tail of the data consisting of the weights of individual diamonds. Based on the estimated
tail probability, the expected number of diamonds larger than a specified weight can be estimated. The difference between
the expected and observed frequencies of diamond weights above an upper threshold provides an estimate of the number of diamonds
lost during the recovery process. 相似文献
19.
DUAN Jilin LIU Yanpeng ZHU Lixin MA Shengming GONG Qiuli Alla DOLGOPOLOVA Simone A. LUDWIG 《《地质学报》英文版》2023,97(4):1252-1267
Geochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert’s knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb-Zn deposit, Southeast China. Three hundred fifty two samples were collected, and ea... 相似文献
20.
基于证据权法构建滑坡地质灾害评价模型,进行杭州市滑坡地质灾害危险性区划研究。主要数据源包括1930－2009年杭州市域采集到的1 905个地质灾害个例以及杭州市地质图、土地利用数据及数字高程模型（DEM）等。利用Arcgis空间分析及信息提取功能,筛选强降水、地层岩性、坡度、坡向、坡高、河网与道路缓冲等证据因子,并运用证据权法客观确定各因子权重, 最后通过Arc-WofE扩展模块对多种优选因子的叠加,计算任意格网单元的滑坡发生概率,实现对潜在滑坡点位的空间预测。经分离样本法验证,区划准确率为88.3%,分析结果与现有滑坡的分布情况比较吻合。据此表明证据权法在多指标评价及其权重确定等方面具有普适性,值得在滑坡地质灾害危险性区划等方面推广应用。 相似文献