小波与奇异谱分析在地铁保护区监测数据处理中的应用

以南京市地铁沉降监测数据为例,将小波分析和奇异谱分析方法应用在地铁保护区监测数据的处理中。实验证明本方法在探测异常值、获取沉降变形趋势、变形周期等方面具有可行性。     

遗传小波神经网络在高铁沉降预测中的应用

利用遗传算法的全局寻优特性,与小波神经网络相结合,建立遗传小波神经网络模型,并将其运用于高铁沉降预测,通过与BP神经网络、小波神经网络在高铁沉降预测的实验对比分析,结果表明遗传小波神经网络在高铁沉降预测中的稳定性更好、精度更高。     

小波包分析在GPS振动观测中的应用

将小波包分析应用于GPS振动观测数据处理,研究小波包分析在异常值探测、信号特征提取、去噪中的应用。首先对信号分解和单支重构,验证小波包分析探测异常值的能力。针对小波包分析含有丰富的高频信息,采用奇异谱分析方法提取高频部分的振幅和频率。小波包分析的去噪能力优于小波分析。     

沉降数据序列分析方法研究

针对沉降数据序列的特征,研究了多项式回归方法、小波降噪方法、频谱分析法在沉降数据处理中的应用.多项式回归残差和小波降噪残差统计指标对比表明:在趋势项分离过程中,小波降噪方法优于多项式回归方法.小波降噪的残差频谱分析结果表明了沉降数据序列在2个月和4个月的周期性信息.     

沉降数据序列分析方法研究

针对沉降数据序列的特征，研究了多项式回归方法、小波降噪方法、频谱分析法在沉降数据处理中的应用。多项式回归残差和小波降噪残差统计指标对比表明：在趋势项分离过程中，小波降噪方法优于多项式回归方法。小波降噪的残差频谱分析结果表明了沉降数据序列在2个月和4个月的周期性信息。     

结合Kalman滤波与小波神经网络的沉降形变监测

高速铁路隧道工程监测点沉降曲线具有小沉降、大波动的特点,沉降变形数据中存在大量随机噪声,将影响沉降变形分析的准确性。将Kalman滤波应用于高铁隧道沉降变形数据预处理,对沉降变形数据进行去噪,再利用小波神经网络对去噪后的沉降变形数据进行预测分析,从而提高单一小波神经网络的预测精度。通过工程实例分析表明,结合Kalman滤波的小波神经网络预测精度优于单一小波神经网络,具有更好的应用价值。     

基于 MATLAB 与小波进行沉降数据处理与分析

结合小波分析的基本原理和MATLAB强大的数据处理能力与图形拟合能力,采用了小波时间序列预测模型与MATLAB中自带的拟合工具箱来进行沉降数据的拟合,并将该方法应用于黄冈市某大桥软土路基沉降监测的预测工作中。然后,对两种拟合方法进行了预测效果对比。从所得的结果表明：小波处理后的模型具有抗差干扰和粗差探测、剔除的优点,同时使用曲线拟合工具箱的拟合模型比常规模型更方便快捷且预测的效果也很高,两种模型结合使用会较大地增加其应用价值。     

基于MATLAB与小波进行沉降数据处理与分析

结合小波分析的基本原理和MATLAB强大的数据处理能力与图形拟合能力,采用了小波时间序列预测模型与MATLAB中自带的拟合工具箱来进行沉降数据的拟合,并将该方法应用于黄冈市某大桥软土路基沉降监测的预测工作中。然后,对两种拟合方法进行了预测效果对比。从所得的结果表明:小波处理后的模型具有抗差干扰和粗差探测、剔除的优点,同时使用曲线拟合工具箱的拟合模型比常规模型更方便快捷且预测的效果也很高,两种模型结合使用会较大地增加其应用价值。     

基于Kalman滤波的高耸建筑物沉降预测模型研究

为了提高高耸建筑物沉降监测数据的可信性与实时预测的准确性,利用小波分析与Kalman滤波在数据处理方面的优势,采用小波分析先对沉降数据进行去噪,并采用Kalman滤波理论进行预测。通过实验数据的对比分析,结合小波去噪分析与Kalman滤波理论对原始沉降变形量进行沉降预测,能克服只使用单一Kalman滤波方法进行沉降预测中噪声处理的不足,验证了小波分析及结合Kalman滤波理论进行沉降预测的可行性,且预测精度比较高。     

小波时间序列在地铁沉降监测中的应用

本文结合小波理论和时间序列的基本原理,提出了小波时间序列模型,并应用于某市地铁沉降的预测中,将其结果与常规时间序列模型对比,表明该模型具有在粗差探测、剔除等方面的优点,同时比常规模型预测更具应用价值。     

A comparative study of soil erosion modelling by MMF,USLE and RUSLE

The quantitative assessment of spatial soil erosion is valuable information to control the erosion. The study area in a part of Narmada river in central India is selected. The main objective is to assess and compare the results obtained from three soil erosion models using GIS platform. Variation in the rate of erosion of the three models is compared considering varying slope, soil and land use of the area. Three models selected are Morgan–Morgan–Finney (MMF), Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE). The best fit or the most reliable model for the study area is selected after validation with the observed sedimentation data. The results give –39.45%, –9.60% and 4.80% difference in the values of sedimentation by MMF, USLE and RUSLE, respectively, from the observed data. Finally, RUSLE model has been found to be most reliable for the study area.     

ERS-1 modulation transfer function impact on shoreline change model

The impact of wave spectra modulation transfer function (MTF) in shoreline change model accuracy has been presented. The MTF consisted of real aperture radar (RAR) and velocity-bunching which is utilized to map the wave spectra observed from ERS-1 into the observed real ocean wave spectra. Based on this information, the shoreline change model have developed. Two hypotheses were concerned with the shoreline change model based on ERS-1 wave spectra. First, there is a significant difference between RAR and velocity-bunching modulations for ERS-1 wave spectra modeling. Second, this significant difference is induced a different spatial variation for shoreline change pattern.This study shows that there was the significant difference between velocity-bunching and quasi-linear models. The study shows that velocity-bunching model produces wave spectra pattern approximately close to the real ocean wave compared to the quasi-linear model. The error percentage occurred with velocity-bunching and quasi-linear models were 33.5 and 46.7%, respectively. The highest rate of erosion occurred to the shore south of Chendering with −5 m per year and the highest rate of sedimentation occurred to north of Chendering headland with 3 m per year. It can be concluded that ERS-1 data could be used to model shoreline change and identify the locations of erosion and sedimentation. The sedimentation was occurred due to the effect of lowest wave spectra energy captured along the range direction while the erosion was occurred due to highest spectra energy captured near azimuth direction.     

Reservoir Sedimentation Assessment Through Remote Sensing and Hydrological Modelling

Reservoir sedimentation is the gradual accumulation of incoming sediments from upstream catchment leading to the reduction in useful storage capacity of the reservoir. Quantifying the reservoir sedimentation rate is essential for better water resources management. Conventional techniques such as hydrographic survey have limitations including time-consuming, cumbersome and costly. On the contrary, the availability of high resolution (both spatial and temporal) in public domain overcomes all these constraints. This study assessed Jayakwadi reservoir sedimentation using Landsat 8 OLI satellite data combined with ancillary data. Multi-date remotely sensed data were used to produce the water spread area of the reservoir, which was applied to compute the sedimentation rate. The revised live storage capacity of the reservoir between maximum and minimum levels observed under the period of analysis (2015–2017) was assessed utilizing the trapezoidal formula. The revised live storage capacity is assessed as 1942.258 against the designed capacity of 2170.935 Mm³ at full reservoir level. The total loss of reservoir capacity due to the sediment deposition during the period of 41 years (1975–2017) was estimated as 228.677 Mm³ (10.53%) which provided the average sedimentation rate of 5.58 Mm³ year¹. As this technique also provides the capacity of the reservoir at the different elevation on the date of the satellite pass, the revised elevation–capacity curve was also developed. The sedimentation analysis usually provides the volume of sediment deposited and rate of the deposition. However, the interest of the reservoir authorities and water resources planner’s lies in sub-watershed-wise sediment yield, and the critical sub-watersheds upstream reservoir requires conservation, etc. Therefore, in the present study, Soil and Water Assessment Tool (SWAT) was used for the estimation of sediment yield of the reservoir. The average annual sediment yield obtained from the SWAT model using 36 years of data (1979–2014) was 13.144 Mm³ year^?1 with the density of the soil (loamy and clay) of 1.44 ton m^?3. The findings revealed that the rate of sedimentation obtained from the remote sensing-based methods is in agreement with the results of the hydrographic survey.     

神经网络在建筑物沉降分析中的应用

建筑物沉降的诱因与沉降量之间有一个复杂的非线性相关性,应用回归法对这种复杂的相关性进行分析有较大的局限性.人工神经网络是由许多神经元组成的大规模非线性系统,具有较强的动态处理能力,能对简单的非线性函数进行多次复合,来实现一个复杂的非线性函数.神经网络这些特性满足建筑物沉降分析的需求.实例表明,应用神经网络BP算法可以对建筑物沉降原因进行更客观的分析,对沉降趋势预测效果也较好.     

利用ALOS数据获取汶川MS8.0地震的震前形变

利用合成孔径雷达干涉测量的短基线技术对汶川震前的地表形变进行面状监测。获取的是日本卫星L波段的ALOS PALSAR传感器拍摄的卫星数据。在Gamma软件处理下,将2007年6月至2008年5月的时隔320 d的6景数据,利用stacking技术得到汶川震中地区在震前一年内的变形速率。变形特征表明,沿着汶茂断裂带呈“凸”字形分布特征,断裂带处最大隆升值达24 cm,断层两侧出现下降的趋势,在绵虒镇附近达到最大沉降值22 cm。与前人用D-InSAR所做出的研究结果相比,二者具有很好的一致性。     

煤矿工业广场地表沉降监测及GM(1,1)建模预测分析

介绍了沉降监测的基本理论和方法,以钱营孜煤矿为例,建立了沉降监测系统,采用水准测量对监测系统进行定期监测,数据处理和分析。建立了能反映监测动态变化的灰色GM(1,1)等维新息模型,得到:地表沉降与水位变化呈现显著的相关关系,井架基础最大沉降量138mm,最大沉降差为3mm,并对监测点下一期的沉降值进行了预测。研究成果对于工业广场沉降治理具有指导意义。     

基于神经网络的建筑物沉降原因分析

在我们应用回归方法对建筑物沉降原因分析及沉降趋势的预测中，由于实际情况的复杂性及主观认识的局限性，这样所得的结果含有较多的人为因素，可能会与实际情况有所差异。针对这种情况，讨论了应用神经网络方法来发现和验证引起建筑物沉降的因素及对沉降趋势的预测。实例表明，该方法能取得较好的效果。     

建筑物沉降观测成果检查标准的探讨

建筑工程项目在施工和运行阶段对其质量稳定性需要进行沉降观测,而沉降观测结果的质量直接影响到建筑工程质量的判断,本文对沉降观测成果的检查内容、方法和标准进行探讨.     

基于SBAS-InSAR的矿区地表沉降监测与分析

针对雷达差分干涉测量技术（differential interferometry synthetic aperture radar,DInSAR）易受时空失相关、大气相位延迟等因素的影响,采用小基线集技术（small baseline subset,SBAS）对13景TerraSAR-X数据进行时序处理,估算并去除了残余数字高程模型误差、大气延迟误差和轨道误差,分析了研究区域2012-2013年沉降速率,发现2310和1301两工作面最大下沉速率分别为40 mm/a和50 mm/a;分析时序累计沉降值发现,2306、2308、2310这3个工作面在2012年11月15日之前地面沉降并不明显;在2310和1301工作面分别提取3个失相干现象表现较为缓慢的候选点进行时序分析,发现沉降值和时间成线性变化关系,且开采时间越早,其沉降特性越符合线性变化;将SBAS和DInSAR两种方法获得的累计沉降值进行差值分析,发现两种方法的差值在5 mm以内;在2310工作面走向方向和倾向方向选取若干观测点,并提取各观测点的时序沉降值对工作面时序沉降进行量化分析,实验表明SBAS-InSAR技术在矿区地表沉降监测与分析方面具有良好的应用前景。     

Assessment of floodplain landuse and channel morphology within meandering reach of the Talar River in Iran using GIS and aerial photographs

Abstract

The main objective of this study is to investigate and monitor the landuse and morphological changes in the floodplain part of the Talar River, northern parts of Iran. In the present study, the aerial photographs have been used to produce landuse maps of the floodplain for three periods including 1968, 1994, and 2013. The quantitative analysis of the produced landuse maps showed that the floodplain has undergone substantial landuse changes. Moreover, the sediment bar and the beach area have been decreased about 97 and 90%, respectively, during the 45-year period. Substantial increases of 192 and 622% have been observed for orchards and residential areas, respectively. On the other hand, not only the forest and riparian vegetation were decreased but also the average width of river was decreased about 25.5 m. In addition, flow length of the study reach increased about 8 m. The RNCI was about ?0.7 m per year indicating sedimentation process. During the period of 1968–2013, Caspian Sea has retreated about 150 m and the delta of Talar River was changed. This study showed that morphological actions during first 26 years (1968–1994) were the stable and last 19 years had the change period, especially sedimentation (bar).