SeaWinds散射计海面风场神经网络建模研究

根据Sea Winds散射计只有两个入射角和两种极化方式的特点,利用其L2A数据和F291海上浮标数据,针对传统建模方法的不足和限制,借助神经网络建立了一个两种极化方式下统一的神经网络地球物理模型函数。该模型的主要特点是建模风矢量全部取自海上浮标测量数据,因而所用风矢量更加客观准确。通过与Qscat-1模型的比较和L2B与浮标风速之间的偏差统计分析,证明了该神经网络模型的有效性,并发现Qscat-1模型存在一定的系统性偏差。     

Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network

A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.     

基于互联网数据的城市社区租金评估及 空间格局制图——以深圳市为例

准确刻画精细化尺度下的城市房租空间格局,对于研究城市居住行为、提高城市规划合理性十分重要。文章提出了一种基于互联网房租数据作为可靠数据源的城市房租空间格局制图方法。以深圳市作为研究区,通过广泛采集开放平台中用户发布的租房信息,绘制社区行政区尺度的房租空间分布图。房租空间制图涉及到对于没有样本数据区域平均房租的评估,因此,选取一系列与房租相关的房产属性、房屋区位及配套设施的评价指标,采用前馈神经网络技术构建评估模型。以2015年深圳市的住宅租赁市场作为研究对象,通过对结果的分析,以及与权威部门发布的统计数据进行比较,表明此方法能够有效地绘制社区尺度下城市房租的空间分布,模型预测结果的误差（%RMSE）为13.87%。所使用的互联网房租数据、POIs数据及前馈神经网络的建模工具均是开源的,而且所提出的方法论具有普适性,能够应用于其他研究区的房租空间格局制图,具有实践意义。