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1.
《地质科技情报》2021,40(1)
回顾了国内外在矿产资源定量预测研究领域的发展历程,对近十年来国外相关方向的文献进行了统计对比分析,结果显示机器学习方法已经成为矿产资源定量预测研究领域的热点方向,并主要在如下3个方面发挥了积极的作用:(1)提取和挖掘复杂数据中隐藏的难以识别的矿化信息;(2)致矿异常信息关联与转换;(3)多源地学数据的致矿异常信息融合、预测和发现矿床。对逻辑回归、人工神经网络、随机森林与支持向量机等主要机器学习算法与模型在矿产资源定量预测实践中的应用效果进行了评述,并探讨了在实际应用过程中存在的样本选择、错分代价、不确定性评价以及模型性能评价等主要问题及目前的解决方案。最后提出基于大数据与机器学习的矿产资源定量预测是未来发展的重要趋势。 相似文献
2.
我国积累的大量高质量、多元素、多尺度的地球化学数据，为矿产勘查与环境评价提供了有效的数据支撑。如何对这些数据进行二次开发和再利用，提取有价值的地球化学异常信息并带动找矿突破，是缓解当前矿产资源短缺的重要途径之一。在覆盖区和深部的找矿实践中，由于矿体埋深和覆盖层的影响，往往在表生介质中形成弱小的地球化学异常，识别和评价弱小地球化学异常是当前勘查地球化学数据处理的重要方向之一。本文围绕地球化学异常信息的提取和评价，主要从以下几个方面讨论了相关的国内外研究进展和发展趋势：勘查地球化学数据处理与异常识别方法和模型，勘查地球化学数据闭合效应的影响及其解决方案，基于大数据和机器学习的勘查地球化学数据处理以及弱小地球化学异常的识别和评价。研究发现，在地质环境的约束下,基于大数据思维和机器学习相结合的方法，注重地球化学空间分布模式与已发现矿床的相关关系，同时使用所有地球化学变量能有效刻画具有非线性特征的地球化学空间分布模式，可识别出传统方法无法识别的异常，为开展地球化学空间模式识别与异常提取提供了新的途径。 相似文献
3.
《物探化探计算技术》2019,(6)
为了解甘肃瓜州照壁山地区地球化学元素的分布特征,应用地统计学方法对研究区1:10 000地球化学数据进行分析,共分析Cu、Pb、Zn、Ag、Au和Hg等6种元素。为对研究区地球化学数据进行异常识别与信息提取,应用奇异性理论和方法进行地球化学奇异值制图,得到各元素异常分布图。为进一步了解组合元素异常分布规律,将主成分分析法和S-A法相结合,提取组合异常,为研究区矿产资源潜力评价和靶区圈定提供借鉴。 相似文献
4.
在Sklearn的Python语言代码基础上,开发了基于孤独森林和一类支持向量机的多元地球化学异常识别方法程序。选择吉林省和龙地区为实验区,从1∶5万水系沉积物资料中提取地球化学异常。把实验区已知矿点的空间分布位置作为"地真"数据,绘制两种机器学习算法的ROC曲线并计算AUC值,用来对比两种方法的多元地球化学异常识别效果。研究结果表明:两种机器学习算法都能够有效识别多元地球化学异常,所提取的多元地球化学异常与已知矿点具有显著的空间关联性;孤独森林算法在数据处理耗时和多元地球化学异常识别效果方面略优于一类支持向量机。 相似文献
5.
作为近年来爆炸式发展的方法模型,机器学习为地质找矿提供了新的思维和研究方法。本文探讨矿产预测研究的理论方法体系,总结机器学习在矿产预测领域的特征信息提取和信息综合集成两个方面的应用现状,并讨论机器学习在矿产资源定量预测领域面临的训练样本稀少且不均衡、模型训练中缺乏不确定性评估、缺少反哺研究、方法选择等困难和挑战。进一步以闽西南马坑式铁矿为实例论述基于机器学习方法的矿产预测基本流程:(1)通过成矿系统研究建立成矿模型,确定矿床控矿要素;(2)通过勘查系统研究建立找矿模型,并为评价预测提供相关的勘查数据;(3)通过预测评价系统研究,建立预测模型,并提取预测要素;(4)利用机器学习模型对预测要素进行信息综合集成,获取成矿有利度图;(5)对预测性能和结果进行不确定性评估;(6)找矿靶区/成矿远景区圈定及资源量估算。最后,总结建立以地学大数据和地球系统理论为指导,以“地球系统-成矿系统-勘查系统-预测评价系统”为研究路线的基于地学大数据的矿产资源定量预测理论和方法体系的研究愿景。 相似文献
6.
在大数据蓬勃发展的时代背景下,矿产资源定量预测作为地质大数据的核心部分,其综合分析挖掘多元信息的基本思路与大数据的理念不谋而合。以四川拉拉铜矿为例,开展基于机器学习的三维矿产资源定量预测。通过建立三维地质模型,提取成矿有利信息,构建研究区定量预测模型;基于"立方块预测模型"找矿方法,采用机器学习随机森林算法,计算出研究区成矿概率分布,以此圈定出5个找矿远景区。结果表明,随机森林具有更高的预测准确度与稳定性,且能够对控矿要素重要性做出定量评价。该研究成功地将机器学习应用于三维矿产定量预测,为今后的矿产资源预测评价做出了积极的探索。 相似文献
7.
传统机器学习算法已广泛应用于矿产预测,但面对地质大数据的高维稀疏、不平衡小样本等特性仍缺乏有效处理和分析的方法,设计适合地质大数据特点的机器学习算法是智能矿产预测亟需解决的新问题。本文以内蒙古浩布高地区的铅锌多金属矿产预测为例,提出了一种面向地质大数据的半监督协同训练矿产预测模型。首先对研究区地质找矿信息和地球化学异常信息进行定量分析,提取断裂构造、二叠系地层、燕山期侵入岩、地层与岩体接触带、围岩蚀变及Pb、Zn、Sn、Cu地球化学异常共9种找矿因子。然后利用递归特征消除法优选找矿因子组合,不包括Sn异常在内的8个找矿因子组合被选为最优组合。最后,利用支持向量机和随机森林算法作为基分类器进行半监督协同训练矿产预测,绘制成矿概率分布图。ROC曲线和预测度曲线分析结果表明,半监督协同训练模型的AUC值和预测效率都高于随机森林和支持向量机模型。研究结果也为大数据环境下的智能矿产预测提供了一种新的思路。 相似文献
8.
多重地球化学背景下地球化学弱异常增强识别与信息提取 总被引：1，自引：0，他引：1
为对钦州湾-杭州湾成矿带(南段)庞西垌地区地球化学数据进行异常识别研究与信息提取,利用含量-面积法(C-A)得出庞西垌地区成矿主元素的异常下限,得到各元素异常分布图,并与已知矿(床)点进行叠加分析,发现已知矿(床)点与C-A法分析得到的异常区基本吻合,可根据该异常区预测未知矿床,从而为该研究区矿产资源潜力评价提供依据。为进一步从研究区复杂的地球化学背景中分离出与成矿有关的地球化学异常,采用分形滤波技术(S-A)提取致矿异常。研究表明,S-A法可在C-A法揭示的区域异常的基础上更深层次地提取出与矿化有关的局部异常用以反映研究区的多重地球化学背景,S-A法可有效地使弱异常增强进而提取出致矿异常,为庞西垌地区探寻隐伏矿体提供依据。 相似文献
9.
成矿区三维可视化与立体定量预测——以钦-杭成矿带庞西垌地区下园垌铅锌矿区为例 总被引：3，自引：1，他引：2
矿产资源是人类生存与社会进步的根本物质保障。近年来,随着地表矿、浅部矿产资源的日益枯竭,采用新技术、新方法的深部矿产资源预测成为地质勘查的主要研究方向。基于数字化、三维可视化及矿产定量预测为主的三维地质建模技术,为当前矿产资源远景预测与找矿工作提供了有力的工具。本文在现代成矿预测理论研究基础上,运用三维地质建模技术建立了钦杭成矿带下园垌矿区地质、地球物理、地球化学、钻孔等三维模型,揭示了区内构造地质特征、地球化学异常表征及地层岩体要素,据此探讨了矿床的成因及矿体分布特征。并在此基础上,采用证据权方法对研究区地质、地球物理、地球化学等多源信息进行融合,运用断裂缓冲区、地球化学异常、东岗岭组沉积岩地层等为证据因子来计算单位体积成矿后验概率,进行立体成矿预测,并圈定出铁锰矿、方铅闪锌矿、铅锌银综合矿等3处找矿有利靶区及估算出预测区内矿产资源储量总量为88710吨。研究结果表明:综合分析地质、地球物理、地球化学及钻孔数据进行矿区的三维地质空间定位、定量预测研究,可以有效的识别矿致异常信息,圈定找矿远景区,为成矿预测研究领域提供了新方向,可以将此方法应用至其他矿山。 相似文献
10.
由于受到覆盖区矿体埋藏深度以及表生地球化学作用的影响,隐伏矿致异常以及弱缓化探异常信息的识别一直是地球化学勘查的难点。张八岭-管店地区位于安徽东部张八岭构造带北部,地表多被第四系覆盖,已有研究显示该区具有较好的Au成矿潜力。本文以Au矿床作为找矿目标,开展了面积性深层土壤取样,并利用多重分形理论的局部奇异性指数分析方法对深层土壤Au元素地球化学异常信息进行提取。结果表明,深层土壤Au元素的地球化学异常能够有效识别已知的金多金属矿(化)点,同时较好的反映出深部隐伏断裂构造与已知矿(化)点的空间关系;较之传统方法,基于非线性理论的奇异性指数方法能够有效降低地球化学背景场的影响,具有更好的异常识别效果,可应用于隐伏或亚出露环境下的地球化学异常识别研究。 相似文献
11.
Sarat Kumar Das Pijush Samui Shakilu Zama Khan Nagarathnam Sivakugan 《Central European Journal of Geosciences》2011,3(4):449-461
Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil. 相似文献
12.
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection. 相似文献
13.
《地学前缘(英文版)》2021,12(3)
Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace(RS) coupled with Artificial Neural Network(ANN), Random Forest(RF), and Support Vector Machine(SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment.The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve(ROC) were employed.The value of the Area Under the Curve(AUC) of ROC was above 0.80 for all models.For flood susceptibility modelling, the Dagging model performs superior, followed by RF,the ANN, the SVM, and the RS, then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage. 相似文献
14.
15.
以长江中下游1∶20万铜区域地球化学数据为基础,研究了铜元素地球化学异常特征,认为不同尺度的地球化学异常图具有不同的研究意义:(1)1∶20万地球化学异常可以圈定矿床异常,用于大型矿床预测。研究区内13个大型矿床中有12个落在具有三层套合结构的地球化学异常中,已知矿床储量与异常面金属量、异常面积之间的相关系数分别为0.94、0.95,显示区域地球化学异常规模与储量之间的较好相关性。(2)1∶50万地球化学异常可以圈定矿区异常,用于在成矿带中预测有利成矿区。(3)1∶100万地球化学异常可以圈定大型矿集区或成矿带,用于矿集区预测。如果把研究区内面积大于1 000km2且含有3个以上已知矿床的异常作为矿集区的话,则长江中下游存在3个大型矿集区:马鞍山—南京矿集区、九江—瑞昌—大冶矿集区和德兴—黄山—安庆—铜陵矿集区(实际上包含德兴和铜陵2个矿集区)。大型矿床多产于多层套合的地球化学异常中,大型矿集区所形成的异常具有至少3层套合结构,浓集中心与大型矿床存在对应关系,这些规律的发现为在不同成矿域预测新的大型矿集区提供了重要地球化学标志。 相似文献
16.
In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models. 相似文献
17.
T. G. Sitharam Pijush Samui P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517
Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock
is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site
amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore,
India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability
of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector
Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of
rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore,
where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the
Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation
algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine
based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the
reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced
level of rock has been presented and discussed. 相似文献
18.
Slope stability analysis: a support vector machine approach 总被引：5，自引：0，他引：5
Pijush Samui 《Environmental Geology》2008,56(2):255-267
Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem.
However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which
is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of
this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that
aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over
the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability
status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than
previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%. 相似文献
19.
水资源是支撑粮食生产的重要因素之一,气候变化驱动下的水资源变化及对粮食产量的影响是当前研究的国际前沿和热点问题。以汾河流域冬小麦和夏玉米2种主要粮食作物为研究对象,利用线性回归、人工神经网络、支持向量机、随机森林、径向基网络、极限学习机等6种机器学习算法构建粮食产量模拟模型,基于气候弹性系数法分析水资源量对气候变化响应关系,在流域尺度上研究粮食产量对气候变化驱动水资源变化的综合响应。结果表明:①机器学习算法能够较好地模拟汾河流域的冬小麦和夏玉米产量;②降水增加10%导致汾河流域水资源量增加19.4%,气温升高1℃导致水资源量减少4.3%;③当降水减少10%~30%时,冬小麦产量减少6.4%~19.3%,夏玉米产量减少4.0%~15.0%;④当气温升高0.5~3.0℃时,冬小麦产量预计增加1.8%~17.1%,夏玉米产量预计增加1.2%~7.9%;⑤汾河流域冬小麦产量对降水和气温变化的敏感性大于夏玉米。相关成果对于区域水资源管理和农业生产策略制定具有重要的科学意义和实用价值。 相似文献