Identification of Glacial Isostatic Adjustment in Eastern Canada Using S Transform Filtering of GPS Observations

Over the years, a number of different models and techniques have been proposed to both quantify and explain the glacial isostatic adjustment (GIA) process. There are serious challenges, however, to obtaining accurate results from measurements, due to noise in the data and the long periods of time necessary to identify the relatively small-magnitude signal in certain regions. The primary difficulty, in general, is that most of the geophysical signals that occur in addition to GIA are nonstationary in nature. These signals are also corrupted by random as well as correlated noise added during data acquisition. The nonstationary characteristic of the data makes it difficult for traditional frequency-domain denoising approaches to be effective. Time–frequency filters present a more robust and reliable alternative to deal with this problem. This paper proposes an extended S transform filtering approach to separate the various signals and isolate that associated with GIA. Continuous global positioning system (GPS) data from eastern Canada for the period from June 2001 to June 2006 are analyzed here, and the vertical velocities computed after filtering are consistent with the GIA models put forward by other researchers.     

Optimizing Ground Water Observation Networks in Irrigation Areas Using Principal Component Analysis

Ground water monitoring networks can provide vital information for sustainable water resources management. This involves the measurement of ground water level, solute concentration, or both. This article deals with the former. It optimizes network distribution of piezometer or data sampling wells to effectively monitor ground water levels under an irrigation region while retaining adequate overall measurement accuracy. This article presents a structured process for applying principal component analysis (PCA) in optimizing a ground water monitoring network in an irrigation area of Australia. The PCA functions, distributed with the MATLAB package, were used to determine relative contributions of individual piezometers in capturing the spatiotemporal variation of ground water levels. Kriging gridding interpolation algorithm was used to render the data surface presentations and determine spatial differences in piezometeric surfaces using different number of data sets. The results show that the overall difference of ground water level between the original piezometer network and the optimized networks after the PCA process was applied is less than 20%, while the total number of piezometers in the optimized network is reduced by 63%, which will save the time and cost to monitor ground water levels in the irrigation area.     

Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day’s AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000–2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.     

基于主成分分析的多能谱CT图像分析方法研究

基于光子计数探测器的能谱CT,可以同时采集多个能谱通道的投影数据,并获得相应能量范围内物质的吸收特征,可以有效应用于物质识别与材料分解。主成分分析是一种很好的多元数据分析技术,可以用于处理多能谱CT数据。本文分别在投影域和图像域对能谱CT数据进行主成分分析,并对分析结果做出系统比较。为了减少噪声的影响,提高能谱CT图像的彩色表征性能,提出双域滤波与像素值平方相结合的方法,用于含噪声的主成分图像去噪,然后将所选取的主成分图像映射到RGB颜色通道。实验结果表明,无论是在投影域还是图像域进行主成分分析,都可以获取清晰的CT图像,识别出物质的不同成分。相较于在图像域的主成分分析方法,在投影域进行主成分分析能够保留物质的更多细节,获取更清晰的彩色CT图像。      

滇西地区前兆震群参数的主成分分析

对1970年以来滇西地区中小地震资料进行了分析,给出了滇西地区震群及前兆震群的定义;应用主成分分析方法构建了前兆震群判别方程,对震群活动的异常特征进行了分析。结果表明,滇西地区前兆震群具有集中分布的区域特征,主要分布在洱源、剑川、宁蒗、龙陵、腾冲、梁河一带。滇西地区前兆震群综合预报方案以P≥0.42为阈值,前兆震群出现后,2年内发生M≥5地震的对应率达到88%。     

基于PCA的地震官方微博影响力评价研究

随着政务微博用户规模及影响力的不断提升,微博作为地震部门传播平台,在地震信息传播方面发挥着巨大的作用。本文在充分考虑地震部门行业特点的基础上,对30个地震官方微博数据进行收集,利用主成分分析法(PCA)归纳出3种地震微博的主要影响指标,即服务力主成分、交互力主成分和创作力主成分,并由此构建出地震官方微博影响力评估指标体系,在此基础上计算得出各地震官方微博的主成分指标得分和影响力综合得分,最后根据得分情况对地震官方微博影响力提出了具可行性的提升策略。     

利用主成分分析法研究东北地震活动异常

选择与地震强度有关的地震频次N(ML≥3.0)、b值、η值、Mf值、A(b)值和AC值共6个参量进行主成分分析,实现对上述参量的有效约简。这6个参量之间有一定的相关性,在不同时段各参量的变化各有所异。根据主成分分析可以得到反应地震强度特征的综合指标W。1970年以来东北14次5级以上浅源地震前W都出现明显的异常变化,震后恢复。这表明综合指标W可以较好地反映地震活动的异常特征。     

基于主成分分析方法的海量地震数据属性降维优化

针对传统的地震数据属性降维优化方法所选取的地震数据属性特征贡献率低导致降维过程计算量大、CPU占用率高等问题,提出一种基于主成分分析的海量地震数据属性降维优化方法。首先根据地震样本特征建立地震数据特征矩阵,把矩阵中的特征进行聚类,运用降序法排列聚类结果,选取前几项数据作为地震数据属性特征选取结果,对其结果评估分类信息量;通过特征积分准则(FSC)修正分类信息量,获取海量地震数据属性特征节点;运用主成分分析方法对地震数据属性特征节点主成分添加标签,确定Fisher判别分析与PCA可变动选择不确定关系,建立半监督降维的全局最优化形式,运用特征值分解计算降维结果,克服海量地震数据属性降维过程中的过拟合问题,融合主成分分析算法与Fisher判别分析算法实现海量地震数据属性降维优化。实验结果证明,所提方法选取的属性特征精度及贡献率较高,降维过程中CPU占用率较低。     

Principal Component Analysis for Water Quality Assessment of the Ganga River in Uttar Pradesh,India

Water Resources - The present study is an attempt to apply principal component analysis (PCA) for spatial assessment of water quality parameters that are responsible for water quality deterioration...     

基于主成分分析及BP神经网络分析的地震人员伤亡预测模型研究

影响地震人员伤亡的因素众多,且彼此之间存在着复杂的交互作用。本文在综合考虑各种因素后选取地震发生时刻、人口密度、地震预报与否、建筑物破坏率、设防水准、震级、烈度等因素作为评价指标,首先运用主成分分析算出其主成分,然后运用神经网络分析方法建立预测模型。实例验证预测效果良好。     

GPS垂直分量与温度、气压的相关性分析及影响机制探讨

选取新疆9个GPS观测站数据和所在地区的气温、 气压数据, 运用相关和回归分析方法进行研究, 并探讨了温度、 气压对GPS垂直分量影响的物理机制。 研究结果表明: ① GPS垂直分量周期近似为1 a的变化为气温的影响; ② 气压对GPS垂直分量的影响基本是同步的, 主要表现为短周期的震荡; ③ GPS周年振幅随着纬度的升高而增大的原因是年温差随纬度的升高而增大。     

基于主成分分析的遥感震害变化检测方法与应用

本文详细介绍了基于主成分分析的变化检测方法, 根据地震应急工作的实际要求设计合理的变化检测流程, 以阿尔及利亚地震QuickBird震前震后影像为试验数据, 使用该方法对地震引起的建筑物倒塌进行变化检测应用。 结果证明设计的变化检测方法流程具有一定的实用性, 能较为准确地标示出震后损毁区域, 是对变化检测技术应用于地震应急工作的有益尝试。     

主成分分析法和遗传算法优化的支持向量机模型在地震伤亡人数预测中的应用

为有效解决地震伤亡人数预测所需影响因子多、运算量大、模型训练烦琐等问题,构建了主成分分析法(PCA)和遗传算法(GA)优化的支持向量机(SVM)模型,采用PCA对地震伤亡人数影响因子进行降维以去除贡献率较低的主成分,将贡献率较大的主成分作为支持向量机的输入变量,以地震伤亡人数作为输出变量,利用GA对SVM模型性能参数进行优化,建立基于PCA-GA-SVM的地震伤亡人数预测模型,并对测试样本进行预测,结果表明:与SVM模型、 GA-SVM模型和PCA-GA-BP模型相比, PCA-GA-SVM模型的预测准确率和运行效率分别提高4.73%、 1.14%、 9.99%和47.05%、 36.76%、 44.55%。结果显示, PCA-GA-SVM模型预测精度高,泛化能力强,能够科学合理地对地震伤亡人数作出预测。     

Interpretation of Magnetotelluric Results Using Laboratory Measurements

Magnetotelluric (MT) surveying is a remote sensing technique of the crust and mantle based on electrical conductivity that provides constraints to our knowledge of the structure and composition of the Earth’s interior. This paper presents a review of electrical measurements in the laboratory applied to the understanding of MT profiles. In particular, the purpose of such a review is to make the laboratory technique accessible to geophysicists by pointing out the main caveats regarding a careful use of laboratory data to interpret electromagnetic profiles. First, this paper addresses the main issues of cross-spatial-scale comparisons. For brevity, these issues are restricted to reproducing in the laboratory the texture, structure of the sample as well as conditions prevailing in the Earth’s interior (pressure, temperature, redox conditions, time). Second, some critical scientific questions that have motivated laboratory-based interpretation of electromagnetic profiles are presented. This section will focus on the characterization of the presence and distribution of hydrogen in the Earth’s crust and mantle, the investigation of electrical anisotropy in the asthenosphere and the interpretation of highly conductive field anomalies. In a last section, the current and future challenges to improve quantitative interpretation of MT profiles are discussed. These challenges correspond to technical improvements in the laboratory and the field as well as the integration of other disciplines, such as petrology, rheology and seismology.     

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34′10.92″N, 88°22′10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.     

华北地区大范围精密水准和GPS垂直运动分析

利用华北地区2005-2007年大面积水准观测数据和1999年以来364个GPS测站(包括流动站和连续站)观测数据,分别获取其速度场。结果表明,该地区大面积水准和GPS重叠地区的垂直运动趋势基本一致,大致以太行山构造带为界,西部地区以隆升运动为主,东部地区以下沉运动为主,个别地区大面积水准和GPS的差异性运动可能是由于观测资料的时间跨度不一致和测点分布不尽相同所致。