The oldest (~ 1.9 Ga) metadolerites of the southern Siberian craton: age,petrogenesis, and tectonic setting

Geological, geochronological, and isotope-geochemical studies of the metadolerites of the Angaul complex, widespread in the Urik-Iya graben of the southern Siberian craton, were carried out. The metadolerites forming separate conformal bodies (sills) among the metasandstones of the Ingash Formation were studied in detail. U-Pb zircon (SHRIMP) dating of metadolerites yielded an age of 1913 ± 24 Ma, and U-Pb baddeleyite (ID-TIMS) dating of these rocks yielded an age of 1914.0 ± 1.7 Ma. Thus, the date of 1914 ± 2 Ma can be taken as the most precise age estimate for the studied rocks. The metadolerites of the Angaul complex correspond in chemical composition to the normal-alkaline tholeiitic basalts. Metadolerites are differentiated rocks with mg# of 36 to 58. They show fractionated REE patterns: (La/Yb)_n = 1.2-3.5. All metadolerites, independently of their mg# value, have low contents of Nb (1.6-10.2 ppm) and show well-pronounced negative Nb-Ta anomalies in multielement patterns (Nb/Nb* = 0.19-0.54). The metadolerites are characterized by positive ε_Nd(T) values ranging from 0.4 to 5.2, which correlate well with their SiO₂ content and mg# value. The isotope-geochemical parameters of the metadolerites of the Angaul complex indicate that fractional crystallization, along with the assimilation of the host rocks (AFC), might have been the main process during the formation of the most differentiated metadolerites. The geochemical characteristics of metadolerites with the maximum mg# values of 57-58 and ε_Nd(T) = 5.2 suggest that the parental mantle source of the metadolerites resulted from mixing of predominant depleted mantle material with the subcontinental-lithosphere material. Intrusion of the dolerites of the Angaul complex, as well as the deposition of the sedimentary strata of the Ingash Group, took place at the Paleoproterozoic stage of intracontinental extension caused by the collapse of the orogen resulted from the collision of the Biryusa block with the Tunguska superterrane in the southern Siberian craton.     

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

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。     

面向矿产资源信息的空间关联性分析

关联性分析是综合多源、多类数据,发现和挖掘数据中潜在的相关关系,提取和挖掘数据之间的关联性。该方法对于充分利用地质大数据、发现地质要素之间的共生组合规律具有重要的意义。本文首先基于数据的空间位置,将不同类型的地质空间数据建立联系,形成空间属性数据库;其次应用统计方法,对不同来源数据中的属性特征进行分析,发现热液型金矿的形成与火山作用存在明显相关性;最后基于Apriori算法提取热液型金矿的伴生矿与侵入岩的频繁项集,发现伴生矿与侵入岩酸碱性二者密切相关。因此,在今后的工作中,有望应用空间关联性的方法对地球物理、地球化学、遥感等多源地质数据开展深入的研究分析。     

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

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.     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.     

国家数字地震台数据处理研究

为了突显被淹没在噪声背景下的有用信息,对国家数字地震台的数据处理方法进行了研究,并用Matlab程序给出了详细的设计过程。     

基于A*算法的路网数据组织

路网的数据组织是路径规划算法设计的基础。采用C++标准模板库实现基于A*算法的路网数据组织,着重从路网的拓扑结构、空间索引、A*算法数据结构、分层和分网格组织几方面来讨论。最后给出实例证明其有效性。     

利用地震资料反演地层的碳酸盐含量

基于碳酸盐含量与地层速度、密度之间的关系, 在井资料约束下, 使用人工神经网络方法反演高分辨率地震资料所反映的地层碳酸盐含量, 并应用于南海北部陆坡ODP184航次1146和1148孔区, 取得较好效果.方法的关键是从井旁地震道中提取多种属性, 利用逐步回归法, 确定6种与碳酸盐含量相关性最好的地震属性, 分别是平均频率、道积分绝对振幅、主频、时间、道微分瞬时振幅和瞬时频率, 然后进行地层碳酸盐含量反演.反演结果相对于岩心分析的碳酸盐含量的误差大多在±5%之内, 较为精确地揭示了地震地层剖面上碳酸盐含量的分布.      

A trajectory-oriented carriageway-based road network data model, part 1: Background

Introduction Withthedramaticdevelopmentofpositioning andtelecommunicationtechnologies,lotsoftraj ectorydataofmovingvehiclescanbecalculated,collected,andtransferred.Inthemeanwhile,thisdevelopmentandtheavailabilityoftrajectory datahavealsomotivatedresearche…     

三参数小波及其在地震资料分析中的应用

提出了一类含有三个可调参数的分析小波（称为三参数小波）,通过选择这三个参数,可使其最适合于所给定的问题．Morlet小波及改进的Morlet小波是其特例．通过对三参数小波中的参数加以约束,可以获得一类新的近似解析小波．论证了通过恰当的选择参数,三参数小波不仅适合于分析包含慢变频率和振幅分量的信号,而且也适合于包含快变分量的信号．以三参数小波为分析小波,提出了一种用于薄互层地震资料分析的方法．该方法能够刻画薄互层的沉积旋回,也能研究薄互层内部的局部结构,并采用模型资料验证了方法的有效性．