改进BP神经网络在岩质边坡稳定性分析中的应用

使用MATLAB软件中的NNtool工具箱建立了一个通过添加动量改进的BP神经网络模型,用以分析岩质边坡的稳定性。通过对天津某工程的边坡进行分析来检验这个模型的可靠度。根据现场调查的实际情况分析,该边坡岩体比较破碎,因此文中选择了几个有代表性的岩石完整性参数作为输入层参数,通过收集的一些类似边坡的参数对所建立的神经网络模型进行训练,以保证此模型适合被用来分析该工程。结果证明,所建立的模型是可靠的,此种方法在分析岩质边坡稳定性的的实践中具有广泛的使用性。     

基于地震反演方法的奥陶系顶部含隔水层探测

在山西阳泉泊里矿区,太原组K₂灰岩是15号煤层上部主要的含水层,查明其富水分布特征对上下组煤层安全开采至关重要。为了准确得到K₂灰岩的富水分布区域,首先,利用常规的波阻抗反演获取精确的K₂灰岩空间展布特征。然后,结合皮尔逊相关系数法与交叉验证−逐步回归法优选出9种地震属性,构成网络的训练数据。此外,引入适合于时序数据处理且能够捕捉测井曲线前后相关性的长短期记忆神经网络(LSTM),构建智能化、多变量LSTM视电阻率预测模型,以精确地预测研究区视电阻率进而得到地层富水性分布特征。同时,分别利用常规多属性回归算法与多变量LSTM模型在井点位置建立电阻率测井曲线与地震属性井旁道之间的映射关系。最后,将井点处训练好的网络模型推广至无井区得到全区视电阻率体,根据视电阻率值的高低、矿区地质构造与陷落柱发育情况圈定灰岩富水区。实际数据的测试结果表明：与常规多属性回归算法相比,多变量LSTM模型预测误差小,与测井相关系数高,说明多变量LSTM模型可以更加精确地预测出工区视电阻率,在含煤地层的富水性预测中有较好的应用价值。\t\t\t\t

     

浅析山西长治数字测震台网维护工作

文章概述了山西长治数字测震台网的系统构成,并对台网的维护工作从维修次数、故障类型等方面进行分析,认为,要保证观测质量必须做好五方面的维护工作。     

基于Householder矩阵和Butterworth滤波器组的反馈延迟网络人工混响

针对现有人工混响方法中反馈延迟网络方法存在延迟线的个数选择受限和无法设定子带混响时间的不足,本文设计了一种基于Householder反馈矩阵和Butterworth滤波器组的反馈延迟网络人工混响器.Householder反馈矩阵有助于最大化回声密度,延迟线个数的选择相对自由,Butterworth滤波器组便于设定子带混响时间.仿真实验中分别利用语谱图、语音质量感知评价和主观评价验证了本文方法的有效性.     

Generating hierarchical strokes from urban street networks based on spatial pattern recognition

Strokes are products of a higher-level aggregation of street segments that can reflect functional importance and perceptual significance that is associated with them in human spatial mental conceptualizations, which is of vital importance for network analysis, street selection, and map generalization. Street properties (e.g., street names) and angles between street segments are the two main elements used for generating street strokes according to the continuity principle of perceptual grouping into networks. However, it is difficult to automatically generate strokes with good continuity from street networks with multiple lanes such as dual carriageways or complex street junctions. This article proposes a method for generating street strokes that maintain good continuity across multiple lanes and complex street junctions. The proposed method first detects dual carriageways and complex junctions in street networks and then generates strokes according to the continuity principle of perceptual grouping. Finally, it groups the generated street strokes across the dual carriageways and complex street junctions to maintain good continuity. Moreover, the generated strokes are hierarchically ranked based on stroke length and centrality measurements. Experimental studies demonstrate the validity and effectiveness of the proposed method. The result shows that the generated street strokes maintain good continuity and reflect well the hierarchical structure of the street networks.     

面向野外地质调查工作的道路提取方法研究

为弥补传统的依靠管理组织、法律、经济、教育等进行对野外地质工作安全管理工作的不足,通过百度SDK采集GPS步行轨迹数据,基于单向GPS轨迹数据的特点,进行轨迹的分割分段处理.最后利用BP神经网络算法拟合道路,通过反复实验确定BP网络的参数配置信息.并以中国地质大学西区为研究区域,提取路网信息,验证算法的有效性和实用性....     

一种基于GA-BP-MC神经网络的高铁桥墩沉降预测模型

提出一种基于马尔科夫链修正的遗传BP神经网络预测模型（GA-BP-MC）,利用遗传算法的全局寻优能力初始化BP神经网络权值和阈值,初步建立GA-BP神经网络预测模型,结合马尔科夫链的无后效性修正模型预测值,形成高精度GA-BP-MC神经网络变形预测模型。结合高铁桥墩沉降数据,分别与BP神经网络、GA-BP神经网络预测模型进行对比,结果表明,该预测模型精度最高。     

自由测站边角交会网在工程中的应用

鉴于自由测站边角交会网具有网形规则、可靠性高、多余观测数多和观测时无需对中等优点,提出将自由测站边角交会网应用于长大隧道洞内平面控制、基坑水平位移监测和有砟轨道CPⅢ平面网建网中。研究结果表明,将自由测站边角交会网应用于上述工程中都能在一定程度上克服传统方法的不足,提高测量效率和精度,值得推广使用。     

美国长期生态研究计划：背景、进展和前景

由美国国家科学基金会支持的长期生态研究计划于1980年启动，至今在理论研究、开发技术和服务社会方面都取得了举世瞩目的成就。该计划的发展分为 3个阶段，每一阶段大约为10年。在其发展的第一个阶段，是以研究站所代表的生态系统为研究对象，主要开展了生态系统的过程与格局方面的研究并系统采集和存贮了有关数据；在1990 -2000年的第二个发展阶段中，其研究工作的重点是开展跨站的网络研究和人类活动对生态系统的影响，以揭示生态系统的过程与格局在较大空间尺度上的特征；按照规划，美国长期生态研究计划的发展进入到第三个阶段后，其工作的重点是开展综合研究、进行生态预测和更好地为社会发展服务，同时加强生物多样性的研究和信息学的发展。     

SAR image despeckling with a multilayer perceptron neural network

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.