地名识别与匹配的概率统计方法

建立了一个试验用地名库和地理语料库,在此基础上构建对地名用字可信度的统计分析模型。通过分析地名在中文文档中的使用习惯和规律,总结出经常与地名一起使用的且具有地名指示含义的辅助字或词,以此为基础建立地名识别辅助词词库和地名识别的规则库。对地名库和地理语料库的用字进行统计分析,通过设定地名用字可信度概率阈值和辅助词指示作用对文本中潜在地名进行初步的筛选形成候选地名;在粗筛选产生的候选地名基础上结合地名识别规则进一步确认,以提高地名识别的准确率。     

Developing the tools for geological shape analysis,with regional‐ to local‐scale examples from the Kalgoorlie Terrane of Western Australia

Geological map data are often underused in mineral‐exploration programs, which rely increasingly on regolith geochemistry and geophysical and other remotely sensed data to generate exploration targets. However, solid geology maps, which are progressively being upgraded due to improved interpretations of superior, remotely sensed images and airborne geophysical data, can be useful in targeting specific types of mineral deposits, which formed late in the evolutionary history of the host terrane. In such terranes, the present map geometry is essentially the same as that at the time of deposit formation. This is the case for orogenic lode‐gold deposits, which commonly show predictable structural controls and/or structural geometry. Thus, the shape of a rock body, or combinations of structures and rock bodies, may provide an important guide to the exploration potential for orogenic lode‐gold deposits. However, until recently, there has been a dearth of techniques to quantify the various properties of shape, and hence test the potential of the two‐dimensional shape of geological bodies in map view as an exploration tool. Integrating techniques from the field of pattern recognition with a modern Geographical Information System (GIS) can provide the shape‐analysis tools required to investigate the geometries of geological shapes. Two‐dimensional shape analysis is now possible through the calculation of several shape metrics including, but not restricted to, aspect ratio, blockiness, elongation, compactness, complexity, roundness, spreadness and squareness. Methods are developed for describing the geometries of rock units about mineral deposits, or any geological features, at any scale, which for the first time makes it possible to compare shapes. These shape‐analysis techniques are tested using orogenic lode‐gold deposits, particularly those in the Kalgoorlie Terrane of the highly auriferous Late Archaean Norseman‐Wiluna Belt of Western Australia. On a global scale, shape analysis indicates that those greenstone belts whose volcanic rock sequences have high elongation and relative low roundness, complexity and aspect ratio (e.g. Kalgoorlie Terrane) are likely to be the most richly endowed in gold. On a more local scale, characteristics of the shape of geological features around the Golden Mile deposit are calculated and used to test the likelihood of occurrence of gold deposits with similar geometry elsewhere in the Kalgoorlie Terrane. The area with the most closely matching shape, on the basis of a 2 km clipping‐circle radius, chosen on the basis of available proximity‐analysis data, corresponds to the recently discovered Ghost Crab deposit, illustrating the potential of the shape analysis methodology in mineral exploration. Shape analysis is, at least in part, scale dependent, due to the inherent problem of being able to define rock boundaries more precisely in units that have strong geophysical signatures than those with weak signatures in poorly exposed terranes. Overcoming this problem is a challenge to the application of this methodology.     

Comprehensive Assessment of Seawater Quality on an Improved Attribute Recognition Model

The attribute recognition model （ARM） has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model （IARM） is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.     

用雷达回波资料识别雹云的最新进展

本文评述了当前国内外利用雷达回波资料识别雹云的现状,着重介绍了笔者近年来利用雷达回波资料建立雹云识别模式的研究工作,并展望了雹云识别的未来发展前景。     

EXPLORING SPATIAL FAMILIARITY*

Spatial familiarity is a natural language concept that has no well-defined technical interpretation. We explore here some possible dimensions of the concept, provide subjective measurements of the dimensions, and evaluate their independence. The four dimensions examined are locational knowledge, visual recognition, name identification, and interaction frequency. We suggest that both cognitive and behavioral dimensions are embedded in the concept of spatial familiarity and must be part of the meaning of the concept and of any attempt to make it operational.     

来自大别山深成侵入岩图像深度迁移学习的可解释性研究

岩石图像识别是以深度学习为代表的感知智能在地质领域的典型应用场景。已有研究显示网络结构简单的深度卷积神经网络能够在岩石图像上取得比复杂网络结构高的分类准确率。这与ImageNet数据集上网络结构越深越好的趋势相悖。如何解释这一现象？深成侵入岩为显晶质,自形—半自形粒状结构,块状构造,其分类的依据是其矿物成分及相对含量。大别山地区岩浆活动广泛,中生代深成侵入岩广泛出露。岩石类型包括超镁铁质岩类、辉长岩类、闪长岩类、正长岩类、二长岩类和花岗岩类,基本覆盖IUGS推荐的深成侵入岩分类方案中的岩石类型。选取大别山地区中生代深成岩图像开展不同网络结构预训练模型迁移学习对比试验,能够专注于深度学习对矿物成分特征的学习解释,降低构造因素的影响。借助局部可理解的模型解释技术和特征图可视化技术,分别从全连接层分类决策区域可视化和卷积隐层可视化两方面对深度学习模型开展可解释性研究。结果表明简单网络结构的卷积神经网络能够提取不同矿物所表现出的颜色特征以及不同矿物组合所表现出的纹理特征。AlexNet模型的削减试验进一步证明：对于岩石图像深度学习,网络结构并不总是越深越好。     

油源断裂内油气运移优势通道识别方法及其应用

为了研究含油气盆地下生上储式生储盖组合中油源断裂附近油气分布规律,在油源断裂内油气运移优势通道及其影响因素研究的基础上,通过确定油源断裂凸面脊和断裂填充物油气运移分布区,二者耦合建立了一套识别油源断裂内油气运移优势通道的方法,并将该方法应用于渤海湾盆地冀中坳陷廊固凹陷大柳泉地区沙三中亚段F3油源断裂内油气运移优势通道的识别中,结果表明：F3油源断裂内可识别出5条油气运移优势通道,其中4条油气运移优势通道分布在断裂南部,1条油气运移优势通道分布在断裂北部,与目前F3油源断裂附近沙三中亚段已发现油气分布在中南部和北部相吻合,表明该方法用于识别油源断裂内油气运移优势通道是可行的。     

区域地质调查文本中文命名实体识别

作为我国地质调查领域最重要的数据源之一,地质调查报告中蕴含着丰富的地学知识及地质体描述等关键信息,准确高质量地抽取地质命名实体为地学知识图谱构建、知识推理及知识演化提供基础。笔者等在阐述地质命名实体识别任务基础上,分析地质实体不仅包含大量专业术语,还存在实体嵌套、大量长实体等领域特性,进一步增加了地质命名实体识别难度。笔者等提出一种基于轻量级预训练模型(ALBERT)—双向长短时记忆网络(BiLSTM)—条件随机场(CRF)模型的地质命名实体识别方法。首先利用ALBERT对输入字符上下文特征进行建模,并采用BiLSTM对其进行进一步上下文特征表征,最后采用CRF实现标注序列预测。实验结果表明,在构建的地质命名实体识别数据集上,相比于主流的命名实体识别模型算法,本文所提出的方法具有更好的抽取性能,提出的命名实体识别模型能为领域实体识别提供借鉴,同时为地学领域实体关系抽取和地学知识图谱构建提供有力方法支撑。     

国道213汶川—松潘段滑坡隐患遥感识别与易发性评价

国道213汶川—松潘段位于强震山区,滑坡灾害频发,每年都会因滑坡灾害导致交通中断,迫切需要查明沿线滑坡隐患的空间分布,并对其易发性进行评价。利用光学遥感和InSAR综合遥感技术对沿线滑坡隐患进行识别,共识别滑坡隐患288处,其中InSAR探测出有形变的滑坡隐患点27处。以识别出的滑坡隐患为评价样本,选取高程、坡度、坡向、地表曲率、工程地质岩组、归一化植被指数(normalizied difference vegetation index,NDVI)、距道路距离、距河流距离、距断层距离等9个影响因素作为评价因子,采用Logistic回归模型评价该沿线滑坡隐患易发性,评价结果可为国道213汶川—松潘段滑坡灾害防治提供参考。     

基于模式识别理论的卡钻类型判别分析

将模式识别理论中的支持向量机、Bayes判别分析以及多元回归分析应用于卡钻类型判别分析,建立了基于模式识别理论的卡钻判别模型。以近几年川东北卡钻数据为例进行了算例分析,结果表明：采用支持向量机、Bayes判别法及多元回归法对卡钻类型判别的结果与实际结果的误判率分别为1.92%,11.52%,61.54%。支持向量机（SVM）判别结果精度最高,但其判别方程式较为复杂,不能直观看出各分量对结果的影响程度;多元回归分析判别方程形式简单,可以直观表达各参量与卡钻之间的密切程度,但其判别精度较低;Bayes判别法计算精度介于二者之间,但其判别精度与判别式的个数密切相关。