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1.
Mineral targets are local geological anomalies. In a study area of a number of unit cells, mapping mineral prospectivity can be implemented by identifying anomaly cells from the unit cell population. One-class support vector machine (OCSVM) models can yield useful results in anomaly detection in high-dimensional data or without any assumptions on the distribution of the inlying data. The OCSVM model was applied to mapping gold prospectivity of the Laotudingzi-Xiaosiping district, an area with a complex geological background, in Jilin Province, China. The decision function value of each unit cell belonging to an anomaly was computed on the basis of the trained OCSVM model and used to express gold prospectivity of the cell. The receiver operating characteristic (ROC) curve, area under curve (AUC) and data-processing efficiency were used to compare the performance of the OCSVM model and a restricted Boltzmann machine (RBM) model in mapping gold prospectivity. The results show that the OCSVM model outperforms the RBM model in terms of ROC, AUC and data-processing efficiency. Gold targets were optimally delineated by using the Youden index to maximise the spatial association between the delineated gold targets and known gold deposits. The gold targets delineated by the OCSVM model occupy 11% of the study area and contain 88% of the known gold deposits; and the gold targets delineated by the RBM model occupy 10% of the study area and contain 81% of the known gold deposits. Therefore, the OCSVM model is a feasible mineral prospectivity mapping approach. 相似文献
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A prospecting cost-benefit strategy is developed by quantitatively defining the prospecting cost and benefit in mineral potential mapping. Suppose that some mineral deposits have been discovered in a study area of a set of grid cells, the prospecting cost and benefit of a “unique” condition can be defined as the percentage of non-deposit-bearing and deposit-bearing cells within the “unique” condition, respectively. By replacing the false positive and true positive rates in the receiver operating characteristic (ROC) curve analysis with the prospecting cost and benefit, the Youden index, likelihood ratio, and lift index can be computed and used to express the mineral potential of the “unique” condition. Thus, the mineral potential mapping in a study area can be implemented by identifying all the possible “unique” conditions and then computing their mineral potential indicators such as the Youden index, likelihood ratio, and lift index. By integrating an automatic “unique” condition searching algorithm with the techniques for computing the mineral potential indicators for each “unique” condition, the following prospecting cost-benefit strategy is developed for mineral potential mapping: (a) select map patterns closely associated with the discovered mineral deposits using their mineral potential indicators, (b) automatically search for all the possible “unique” conditions, (c) evaluate the mineral potential of each “unique” condition using its mineral potential indicators, and (d) assess mineral potential mapping performance using the mineral potential indicator diagrams. For demonstration purposes, the Baishan district in Southern Jilin Province in China, which has a complex geological setting, is chosen as a case study area. The weights of evidence (WofE) modeling posterior probability, Youden index, likelihood ratio, and lift index are applied in the mineral potential mapping and their performance are assessed using their ROC curves, cumulative lift charts, and Youden and likelihood ratio diagrams. The results show that (a) the likelihood ratio and lift index perform similarly well and (b) the posterior probability performs a little bit worse than the likelihood ratio and lift index while a little bit better than the Youden index. Therefore, the prospecting cost-benefit strategy provides a common paradigm for both mineral potential mapping and the performance assessment. 相似文献
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In this research, we conduct a case study of mapping polymetallic prospectivity using an extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer served as hardware platform. Almeida's Python program was used to construct the ELM regression model to map polymetallic prospectivity of the Lalingzaohuo district in Qinghai Province in China. Based on geologic, metallogenic, and statistical analyses of the study area, one target and eight predictor map patterns and two training sets were then used to train the ELM regression and logistic regression models. ELM regression modeling using the two training sets spends 61.4 s and 65.9 s; whereas the logistic regression modeling using the two training sets spends 1704.0 s and 1628.0 s. The four trained regression models were used to map polymetallic prospectivity. Based on the polymetallic prospectivity predicted by each model, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was estimated. The ROC curves show that the two ELM-regression-based models somewhat dominate the two logistic-regression-based models over the ROC performance space; and the AUC values indicate that the overall performances of the two ELM-regression-based models are somewhat better than those of the two logistic-regression-based models. Hence, the ELM-regression-based models slightly outperform the logistic-regression-based models in mapping polymetallic prospectivity. Polymetallic targets were optimally delineated by using the Youden index to maximize spatial association between the delineated polymetallic targets and the discovered polymetallic deposits. The polymetallic targets predicted by the two ELM-regression-based models occupy lower percentage of the study area (2.66–2.68%) compared to those predicted by the two logistic-regression-based models (4.96%) but contain the same percentage of the discovered polymetallic deposits (82%). Therefore, the ELM regression is a useful fast-learning data-driven model that slightly outperforms the widely used logistic regression model in mapping mineral prospectivity. The case study reveals that the magmatic complexes, which intruded into the Baishahe Formation of the Paleoproterozoic Jinshuikou Group or the Carboniferous Dagangou and Shiguaizi Formations, and which were controlled by northwest-western/east-western trending deep faults, are critical for polymetallic mineralization and need to be paid much attention to in future mineral exploration in the study area. 相似文献
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Ratiranjan Jena Biswajeet Pradhan Sambit Prasanajit Naik Abdullah M. Alamri 《地学前缘(英文版)》2021,12(3):101110
Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km2), vulnerability (480.98 km2), and risk (34,586.10 km2) was estimated. 相似文献
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Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area. 相似文献
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基于机器学习的区域滑坡灾害预警模型研究 总被引：1，自引：0，他引：1
中国滑坡灾害严重,区域滑坡灾害预警是防灾减灾的重要手段之一,预警模型是开展区域滑坡灾害预警的关键问题。本文系统开展了基于机器学习的区域滑坡灾害预警模型研究,并以四川省青川县为例,基于近10年地质与气象数据,构建了青川县区域滑坡灾害预警模型并开展实例校验。研究得出如下结论:(1)提出了基于机器学习的区域滑坡灾害预警模型的构建方法,主要包括训练样本集构建、样本学习训练与优化建模、模型保存与预警输出等几个关键步骤。(2)提出了区域滑坡训练样本集的构建方法,即以正样本为基础,在时空约束条件下随机采样获取负样本,最终获得完整的训练样本集。(3)样本学习训练中,以训练样本集的80%作为训练集,20%作为测试集,进行5折交叉验证,采用精确度、ROC曲线和AUC值校验模型准确度和模型泛化能力。采用贝叶斯优化算法进行模型优化。(4)实际预警中,调用训练好的预警模型输出滑坡灾害可能发生的概率。依据概率大小,分级确定预警等级。分级依据为:当输出概率P≥40%且P＜60%时,发布黄色预警;当输出概率P≥60%且P＜80%时,发布橙色预警;当输出概率P≥80%时,发布红色预警。(5)以青川县为例,构建了青川县区域滑坡训练样本集,采用6种机器学习算法进行模型训练,结果显示随机森林算法表现最好,其准确率最高(0.963),模型无过拟合现象,模型泛化能力最好(AUC=0.986);其次为逻辑回归算法;再次为人工神经网络算法和决策树算法。选取2018年6月26日的青川县日常预警业务进行实例校验。结果显示:当日17处滑坡灾害点中,100%的灾害点全部落入预警区范围内,其中:70.6%的滑坡落在红色预警区内,17.6%的滑坡落在橙色预警区内,11.8%的滑坡落在黄色预警区内。 相似文献
8.
加权Logistic回归是基于GIS成矿预测的主要方法之一,其模型是不同于线性模型的一种类型。它具有强大的空间分析功能、适用性强、不受任何独立条件的约束、预测结果更可靠,因此在矿产资源评价研究中得到了很多地质学家的青睐。以矿床模型和成矿理论为基础,加权Logistic回归分析模型在成矿预测中的应用主要包括三部分:加权Logistic回归模型的建立及其应用、成矿有利度综合评价、成矿远景区圈定。本文以中国—哈萨克斯坦边境地区扎尔玛—萨吾尔成矿带斑岩型铜矿为例,探讨了基于GIS的加权Logistic回归模型在成矿预测中的应用。 相似文献
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A Hybrid Neuro-Fuzzy Model for Mineral Potential Mapping 总被引：5，自引：0，他引：5
A GIS-based hybrid neuro-fuzzy approach to mineral potential mapping implements a Takagi–Sugeno type fuzzy inference system in a four-layered feed-forward adaptive neural network. In this approach, each unique combination of predictor patterns is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent predictor patterns. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location), extracted from a set of all feature vectors, is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a favorability map. The procedure is applied to regional-scale base metal potential mapping in a study area located in the Aravalli metallogenic province (western India). The adaptive neuro-fuzzy inference system demarcates high favorability zones occupying 9.75% of the study area and identifies 96% of the known base metal deposits. This result is significant both in terms of reduction in search area and the percentage of deposits identified. 相似文献
10.
典型矿集区成矿地质事件研究和填图方法初探 总被引：1，自引：0，他引：1
针对中等尺度范围(或中等比例尺)区域成矿潜力和已知矿山的深部及外围资源评价等问题，试图用成矿地质事件方法开展相关研究。成矿地质事件评价方法，就是在恢复矿床形成过程并建立成矿模式的基础上，通过对矿源岩系的构造变形岩相形迹追索来恢复与成矿有重要意义的地质事件及其演化过程，研究在这一系列地质事件中成矿物质的分布规律和逐步富集成矿的标志及程度，并用以进一步探索、指导区域资源潜力评价和填图找矿的方法。应用这一思路对南岭中段锡多金属矿和胶东矿集区金矿进行了区域成矿地质事件研究和地质填(编)图方法的初步探讨。 相似文献
11.
F. P. Agterberg 《Mathematical Geosciences》1974,6(4):373-395
Various statistical methods for predicting mineral potential from geological maps are reviewed. It is pointed out that, if the features are coded in more detail for relatively small cells, several new problems arise because of the dichotomous nature of the resulting variables. The objective of this paper is to present a method for the automatic contouring of both discovered and undiscovered deposits of a given type in terms of the geological framework. It is based on the assumption that the probability of occurrence of a deposit is fully determined by a combination of functions of the mappable geological attributes in a region. Application of the logistic model is proposed for the situation in which relatively few deposits of a given type are known to exist in the study region. 相似文献
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概率神经网络是一种分类准确率高、泛用性强、可以包容一定数量错误样本的人工神经网络,极其适合勘查地球化学找矿中的预测找矿靶区。本文以四川雅江县木绒锂矿为例,运用概率神经网络搭建智能找矿模型,以已知区的Li元素及与其相关性强的Rb-Cs-Al-Fe元素作为训练指标,对模型进行训练,经过多次训练后将Spread值确定为0.08,使模型在训练集和测试集的准确率均大于80%,实现非线性的指标与成矿潜力的对应,得到本矿区的PNN模型,然后对预测区的样本数据进行预测,成功圈定了1处靶区。为检验靶区准确性,以Li、Rb、Cs元素数据累计频率的80%作为异常下限,圈出的异常区域与靶区位置基本重叠。对预测区进行了实地查证工作,发现两条红柱石带,其中一条与靶区位置吻合,表明该神经网络模型准确性高,可用于矿产勘查的预测研究。 相似文献
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《Ore Geology Reviews》2003,22(1-2):117-132
A data-driven application of the theory of evidential belief to map mineral potential is demonstrated with a redefinition of procedures to estimate evidential belief functions. The redefined estimates of evidential belief functions take into account not only the spatial relationship of an evidence with the target mineral deposit but also consider the relationships among the subsets of spatial evidences within a set of evidential data layer. Proximity of geological features to mineral deposits is translated into spatial evidence and evidential belief functions are estimated for the proposition that mineral deposits exist in a test area. The integrated maps of degrees of belief for the proposition that mineral deposits exist in a test area is classified into a binary mineral potential map. For the Baguio district (Philippines), the binary gold potential map delineates (a) about 74% of the training data (i.e., locations of large-scale gold deposits) and (b) about 64% of the validation data (i.e., locations of small-scale gold deposits). The results demonstrate the usefulness of a geologically constrained mineral potential mapping using data-driven evidential belief functions to guide further surficial exploration work in the search for yet undiscovered gold deposits in the Baguio district. The results also indicate the usefulness of evidential belief functions for mapping uncertainties in the geologically constrained integrated predictive model of gold potential. 相似文献
15.
Andrew Skabar 《Mathematical Geology》2007,39(5):439-451
Multilayer perceptrons (MLPs) can be used to discover a function which can be used to map from a set of input variables onto
a value representing the conditional probability of mineralization. The standard approach to training MLPs is gradient descent,
in which the error between the network output and the target output is reduced in each iteration of the training algorithm.
In order to prevent overfitting, a split-sample validation procedure is used, in which the data is partitioned into two sets:
a training set, which is used for weight optimization, and a validation set, which is used to optimize various parameters
that can be used to prevent overfitting. One of the problems with this approach is that the resulting maps can display significant
variability which stems from (i) the (randomly initialized) starting weights and (ii) the particular training/validation set
partition (also determined randomly). This problem is especially pertinent on mineral potential mapping tasks, in which the
number of deposit cells is a very small proportion of the total number of cells in the study area. In contrast to gradient
descent methods, Bayesian learning techniques do not find a single weight vector; rather, they infer the posterior distribution
of the weights given the data. Predictions are then made by integrating over this distribution. An important advantage of
the Bayesian approach is that the optimization of parameters such as the weight decay regularization coefficient can be performed
using training data alone, thus avoiding the noise introduced through split-sample validation. This paper reports results
of applying Bayesian learning techniques to the production of maps representing gold mineralization potential over the Castlemaine
region of Victoria, Australia. Maps produced using the Bayesian approach display significantly less variability than those
produced using gradient descent training. They are also more reliable at predicting the presence of unknown deposits. 相似文献
16.
Lei Wang Tao Wei Nan Li Shen-shen Chi Chuang Jiang Su-yang Fang 《Environmental Earth Sciences》2018,77(21):740
Probability integral method is an official prediction method for mining subsidence in China. However, how to obtain the probability integral method parameters based on the measured data is the premise of realizing the accurate prediction of the probability integral method. Simulated annealing (SA) is an effective nonlinear optimization algorithm that has recently been introduced into the mining subsidence field to obtain the parameters of the probability integration method. To solve the problems of slow convergence speed and easily falling into the local optimal solution in the method of parameters inversion in probability integral method based on SA (MPIPIMSA), the method of parameters inversion in probability integral method based on quantum annealing (MPIPIMQA) is proposed by combining the quantum fluctuation mechanism and simulated annealing theory. The simulation experimental results show that MPIPIMQA is superior to MPIPIMSA in the accuracy and stability of parameters, and MPIPIMQA has a stronger anti-interference ability for local losing observation points, random errors and gross errors in observation data. Finally, the parameters of probability integral method for the 1414(1) working face of the Guqiao Coal Mine in Huainan mining area were obtained by using MPIPIMQA, namely, q?=?0.9916, tanβ?=?1.9277, b?=?0.4190, θ?=?84.3381, Su = ??7.3715, Sd = ??14.7126, Sl = 59.0695, and Sr = 32.6381, and the fitting error is 106.8863 mm. The research results have important reference values for accurate inversion of probability integral parameters. 相似文献
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新疆塔尔巴哈台-萨吾尔地区多重分形地球化学异常提取及成矿预测 总被引：2，自引：2，他引：0
塔尔巴哈台-萨吾尔地区位于中国新疆西北部,目前已发现若干处铜、金矿床,具有很好的成矿潜力。成矿定量预测方法常被用于综合成矿标志信息,进行成矿远景区的定量预测和评价。本文首先结合多重分形理论-奇异性指数模型进行地球化学异常提取,之后通过对区域成矿条件进行综合分析,基于地球化学异常以及构造、岩浆岩、地层与矿化的相关关系构建了塔尔巴哈台-萨吾尔地区铜-金成矿预测模型;研究进一步基于新近的找矿成果,以已知矿床和新近发现的矿化点信息作为依据,利用证据权重方法对研究区铜-金矿化的远景区进行了定量预测。预测结果显示出塔尔巴哈台-萨吾尔地区具有良好的找矿前景,区内存在多个新的成矿远景区,可作为新的找矿勘探的目标,开展进一步找矿勘查工作。 相似文献
20.
Chang-Jo F. Chung 《Mathematical Geosciences》1983,15(1):47-58
A system of interactive graphic computer programs for multivariate statistical analysis of geoscience data (SIMSAG)has been developed to facilitate the construction of statistical models to evaluate potential mineral and energy resources from geoscience data. The system provides an integrated interactive package for graphic display, data management, and multivariate statistical analysis. It is specifically designed to analyze and display spatially distributed information which includes the geographic locations of observations. SIMSAG enables the users not only to perform several different types of multivariate statistical analysis but also to display the data selected or the results of analyses in map form. In the analyses of spatial data, graphic displays are particularly useful for interpretation, because the results can be easily compared with known spatial characteristics of the data. The system also permits the user to modify variables and select subareas imposed by cursor. All operations and commands are performed interactively via a graphic computer terminal. A case study is presented as an example. It consists of the construction of a statistical model for evaluating potential areas for explorations of uranium from geological, geophysical, geochemical, and mineral occurrence map data quantified for equalarea cells in Kasmere Lake area in Manitoba, Canada. 相似文献