卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例   总被引：1，自引：0，他引：1  

刘艳鹏  朱立新  周永章 《岩石学报》2018,34(11):3217-3224

大数据人工智能地质学刚刚起步,基于大数据智能算法的地质研究是非常有意义的探索性实验。利用大数据和机器学习解决矿产预测问题,有助于人们克服不能全面考虑地质变量的困难及评估当前模型在已有数据中的可靠性。元素地表分布特征量主要受原岩成分、成矿作用影响和地表过程的影响,它们携带某些指示矿体就位的信息,即矿体在地下空间就位时在地表的响应,且未在地表过程中消失。以往的地球化学勘查工作仅仅识别异常,但未能发现矿体在地表响应的成矿特征量。本文以安徽省兆吉口铅锌矿床为例,通过机器学习,利用卷积神经网络算法,不断挖掘元素Pb分布特征与矿体地下就位空间的耦合相关性。经过1000次训练后,可以得到准确率0. 93,损失率0. 28的卷积神经网络模型。这种神经网络模型就是矿体在地下就位时元素在地表分布的响应,可以用来进行矿产资源预测。应用该模型对未知区进行预测,结果显示第53号区域具有很大概率存在尚未发现的矿体。  相似文献   

长白山火山下方地幔转换带中滞留的俯冲太平洋板块存在空缺吗?   总被引：5，自引：3，他引：2  

雷建设  赵大鹏  徐义刚  樊祺诚  米琦  杜沫霏  鲁明文 《岩石学报》2018,34(1):13-22

针对近年来长白山火山下方地幔转换带中是否存在低波速异常指示的太平洋板块"空缺"而引起的不同科学认识的热烈辩论,本文主要回顾了我国东北地区地幔转换带的体波成像结果。使用相对走时残差的远震体波成像结果显示,长白山火山以西地幔转换带中存在低波速异常指示的太平洋板块"空缺";而使用绝对走时残差的区域成像和全球成像结果,尽管展示出长白山火山以西比以东略低的波速异常,但长白山火山以东至我国东北重力梯度带区域下方的地幔转换带均展示出明显的连续的高波速异常。在接收函数分析时,如果以全球平均值660km而非我国东北地区平均值670km作为基准,来分析660km间断面是抬升还是下沉;以全球平均值250km而非我国东北地区平均值260km作为基准,来分析地幔转换带是增厚还是减薄的话,则可以得到长白山火山以东至我国东北重力梯度带区域660km间断面下沉与地幔转换带增厚的认识。这种与绝对走时残差成像结果展示的地幔转换带为连续的高波速异常结果相一致的结果,说明太平洋板块俯冲前缘已由日本海沟抵达我国东北松辽盆地与大兴安岭交界处。结合高温高压实验、数值模拟与岩石地球化学研究结果,本文并不支持长白山火山以西的地幔转换带存在低波速异常指示的板块"空缺"和地幔转换带"减薄"的认识。长白山火山的深部起源与太平洋板块深俯冲至我国东北松辽盆地与大兴安岭交界处形成的"大地幔楔"结构动力学相关。  相似文献   

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow     

Michael J. Friedel  Massimo Buscema  Luiz Eduardo Vicente  Fabio Iwashita  Andréa Koga-Vicente 《International Journal of Digital Earth》2018,11(7):670-690

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.  相似文献   

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data     

Milad Janalipour 《International Journal of Digital Earth》2018,11(12):1241-1268

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.  相似文献   

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery     

Erik H. Schmidt  Budhendra L. Bhaduri  Nicholas Nagle  Bruce A. Ralston 《地理信息系统科学与遥感》2018,55(6):860-879

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.  相似文献   

The effects of visual realism,spatial abilities,and competition on performance in map-based route learning in men     

Arzu Çöltekin  Rebecca Francelet  Kai-Florian Richter  John Thoresen  Sara Irina Fabrikant 《制图学和地理信息科学》2018,45(4):339-353

We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users.  相似文献   

基于深度学习的岩石矿物智能识别     

任伟  张盛  乔计花  黄金明 《地质论评》2021,67(Z1):67z1281-67z1282

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中国东北新生代火山区CO_2释放规模与成因     

赵文斌  郭正府  刘嘉麒  张茂亮  孙玉涛  雷鸣  马琳  李菊景 《岩石学报》2021,37(4):1255-1269

板片俯冲过程将地表碳带入地球内部,火山作用将深部碳输送至地球外部圈层,两者构成了地球深部碳循环的主要方式,进而影响了地史时期的气候变化。我国东北新生代火山活动被认为是太平洋板片深俯冲作用的产物,板片俯冲导致岩浆源区强烈的碳酸盐组分交代作用,进而使东北亚上地幔成为一个新生代时期的巨型深部碳库,它的活动和释放将会对全球的气候与环境变化造成重要影响。然而,有关该深部碳库向当今大气圈输送CO_2气体的规模及其演化过程尚不清楚,从而影响了进一步定量评估该碳库在全球变化研究中的地位和作用。针对上述科学问题,本文对我国东北长白山、五大连池和阿尔山火山释放CO_2气体的规模与成因进行了研究。结果表明,东北新生代火山区的土壤CO_2释放通量介于9.6～41.2g·m~(-2)·d~(-1)之间,每年向当今大气圈释放CO_2气体约为2.1Mt(其中,长白山火山区为0.94Mt,五大连池火山区为1.2Mt)。气体地球化学研究证实,长白山与五大连池火山气体均起源于太平洋板片深俯冲环境;但是,与长白山相比,五大连池火山气体具有较高比例壳源组分贡献。阿尔山火山气体的成分与长白山和五大连池火山区存在着明显的差异,它们以N_2为主( 95%),并且其δ~(15)N_(N_2)值高于空气值(1.3‰～1.9‰),~3He/~4He比值较低(0.14～0.18R_A),δ~(13)C_(CO_2)较轻(-13.7‰～-6.2‰),表明壳源富氮有机沉积物的贡献占比较大的比例。上述特征进一步表明,阿尔山火山气体在上升经过地下水时可能滞留了较长时间,混染了大量的陆壳组分,其源区并未受到太平洋板片俯冲物质的显著影响。  相似文献   

2011~2020年勘查地球化学数据处理研究进展     

左仁广  王健  熊义辉  王子烨 《矿物岩石地球化学通报》2021,40(1):81-93

我国建立了包含海量数据的高质量的勘查地球化学数据库,为矿产勘查、环境评价和地质调查等提供了重要的数据支撑。如何高效处理勘查地球化学数据,并从中发掘和识别深层次信息一直是勘查地球化学学科研究的热点和前沿领域。本文在系统调研国内外学者过去十年发表的论著基础上,对勘查地球化学数据处理方法进行分析与对比,从勘查地球化学数据库建设、地球化学异常识别及其不确定性评价等方面概述了我国近十年来在该领域取得的主要研究进展,包括:(1)分形与多重分形模型由于考虑了地球化学空间模式的复杂性和尺度不变性,在全球范围内得到极大的发展和推广,我国学者引领了基于分形与多重分形的勘查地球化学数据处理;(2)机器学习和大数据思维开始在该领域启蒙,并迅速得到关注,正在成为研究热点和前沿领域,我国学者率先开展基于机器学习算法的勘查地球化学大数据挖掘研究;(3)我国学者需要进一步加强勘查地球化学数据缺失值处理以及成分数据闭合效应研究。今后该领域应进一步加强对弱缓地球化学异常识别、异常不确定性评价以及异常识别与其形成机理相结合等方面的研究。  相似文献   

机器学习与成矿预测:以闽西南铁多金属矿预测为例     

张振杰  成秋明  杨玠  武国朋  葛云钊 《地学前缘》2021,28(3):221-235

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