镇江地震台网理论监测能力评估

基于近震震级公式,对镇江地震台网理论监测能力进行评估,绘制地震监测能力图,以确定监测区不同地点发生地震时能被台网有效分析和定位的地震最小震级。镇江市句容县在该台网理论监测震级最小,为ML1.2—1.3;在镇江及相邻城市,发生M_L 1.8分析认为,以上地震均能被有效监测。     

Verringerung der Schwermetall‐ und Sulfatbelastung in sauren bergbaubelasteten Gewässern durch Aluminiumpräzipitate

Attenuation of Heavy Metals and Sulfate by Aluminium Precipitates in Acid Mine Drainage During the mixing of acid mine waters with nearly neutral tributaries, often precipitates are formed which are high in iron or aluminium. These precipitates cover the river bed for many kilometres. Near the town of Lehesten (Thuringian slate mining area), leachates of slate quarries and waste rock dumps contain high amounts of aluminium, sulfate, copper, nickel, zinc, manganese, and H⁺ ions as a result of the oxidation of incorporated pyrite. These leachates enter the brooks Loquitz, Kleine Sormitz, and Rehbach leading to the phenomenon named above. The contribution of the forming aluminium‐rich precipitates on the attenuation of sulfate and heavy metals by sorption or coprecipitation was studied by analysing the composition of water and sediment samples as well as samples of suspended matter. Sulfate is often considered as conservative tracer in acid mine drainage. However, sulfate does not behave conservatively in this system what might be explained by the adsorption of sulfate to the aluminium precipitates. Instead, conservative behaviour was found for calcium, potassium, chloride, zinc, manganese, and nickel. A formation of jurbanite can be excluded because of the low sulfate contents. The sulfate content of the sediment depends on the pH. At low pH values (4.8) the S/Al ratio corresponds to the theoretical ratio in basaluminite and decreases with rising pH. Sulfate is weakly bound to the solid phase and can easily be replaced by OH^– ions. A formation of basaluminite is possible at low pH values with a fluent transition to aluminium hydroxide. Therefore the precipitates are assumed to consist predominantly of aluminium hydroxide with sulfate being adsorbed to the surface.     

混沌理论在桥梁变形监测中的应用

针对桥梁的非线性下沉问题,引用了混沌理论,首先求取时间序列的两重构参数时间延迟τ和嵌入维数m进行相空间重构;随后进行混沌特性判别,确定该时间序列存在混沌迹象;最后根据所求参数建立加权零阶局域预计模型和RBF神经网络混沌预计模型对观测数据进行预计分析,并与系数为0.9的指数平滑预测模型进行比较,结果显示混沌预计模型值更接...     

基于分形理论的航空图像分类方法

提出一种基于分形理论和BP神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB格式转化为HSI格式，然后，根据亮度计算分数维、多重分形广义维数谱q-D(q)和“空隙”等基于分形的纹理特征，同时加入归一化的色度和饱和度作为光谱特征，采用BP神经网络作为分类器。通过对彩色航空图像的分类实验，结果证实该方法行之有效。     

Modelling road surface sediment production using a vector geographic information system

Field investigations indicate that unpaved roads are the largest sediment source on St John, US Virgin Islands. Cross-sectional measurements of eroded road surfaces were used to establish an empirical relationship to predict annual road surface erosion as a function of road gradient and contributing drainage area. A model (ROADMOD) for estimating and mapping average annual sediment production from a road network was developed by combining this empirical relationship with a series of network algorithms to analyse road data stored in a vector geographic information system. ROADMOD was used to estimate road surface erosion in two St John catchments with very different road densities but similar land cover, topography and soils. Unpaved roads were found to increase sediment production in the more densely roaded catchment by a factor of three to eight, and in the less-roaded catchment by a factor of 1·3–2·0. Turbidity measurements in the receiving bays of these two catchments are consistent with model predictions and observed sediment delivery processes. Although this model was developed specifically for St John, it can easily be adapted to other locations by substituting a locally derived predictive equation for road erosion. Model assumptions, limitations and potential improvements are discussed. © 1998 John Wiley & Sons, Ltd.     

基于小波变换与神经网络的石羊河流域夏季地温预测模型研究

地温变化在气候反馈效应中起着重要作用, 理解地温及其与影响因素之间的时空关系对预测全球温度变化至关重要。利用1998 - 2017年石羊河流域的逐日常规气象观测资料, 采用小波分析结合BP（Back Propagation）神经网络构建了石羊河流域夏季地温预报模型, 结果表明： 日平均地温预测效果在不同站点均为最佳, 其中预测值和观测值的相关系数均大于0.87, 3 ℃以内的预测概率均大于84%。其中, 民勤地区地温预测效果最好, 预测值和观测值的相关系数达到0.91, 3 ℃以内的预测概率达到86%。日最高地温的预测值与观测值的相关系数高于0.8, 但误差平方和、 标准差较大。永昌地区日最高地温的模拟效果最好, 3 ℃以内的预测概率达到83%。日最低地温的预测与观测值的平均相关系数高于0.66, 3 ℃以内的预报概率高于83%, 但预测值略低。其中, 武威地区日最低地温的预测效果最好, 预测值与观测值的相关系数为0.72, 3 ℃以内的预测概率达到94%。研究成果可为有效弥补干旱、 半干旱区地温观测资料缺失和探讨其与局地气候的关系提供一些参考。     

平坦地区GPS高程异常拟合方法研究

结合GPS测量和水准测量资料,用BP人工神经网络和RBF人工神经网络方法和二次多项式曲面拟合方法拟合高程异常,对平坦地区GPS高程异常拟合精度进行比较分析,得出有实用价值的结论。     

基于PSO-BP神经网络的地表下沉系数选取研究

针对BP神经网络自身收敛速度慢、容易陷入局部极小点的缺点,引入粒子群优化算法,建立地表下沉系数的PSO-BP选取模型。利用粒子群算法反复优化BP网络的权值和阈值,将其作为BP网络的初始值,并将上覆岩层岩性、开采深厚比、松散层厚度、覆岩中坚硬岩层所占比例、是否为重复采动和顶板管理方法等主要影响因素作为网络输入,进行BP算法,直至网络达到训练指标。利用实测资料数据,建立PSO-BP预计模型,并同普通BP神经网络预计结果对比。结果表明：PSO-BP神经网络不仅训练速度快,而且预测精度明显提高,该模型对地表下沉系数选取具有一定的应用价值。     

土壤含盐量反演的研究

运用Hyperion数据,以黑龙江省大庆市某一实验区为例,开展对土壤含盐量定量提取的研究,通过对图像预处理、特征提取、建立BP神经网络模型（Back Propagation Network）等研究工作,探讨反演土壤含盐量的方法。研究结果表明：神经网络模型具有极强的线性和非线性拟合能力,模拟遥感影像特征与土壤盐分之间比较复杂的关系上有很大优势。研究结果不但为利用Hyperion数据反演土壤含盐量提供理论依据,而且还为其它地表参数的反演提供参考。     

导航应用的按需下载区域网络模型研究

提出了一种导航应用的按需下载区域数据模型。对按需下载模式的技术要求进行了分析,提出了区域导航数据模型的定义及其算法,用UML定义了区域网络概念模型,并与ISO的网络模型进行了一致性检验。通过实际算例证明了方法的可用性。