Filters for linear sea-wave prediction

Deterministic sea-wave prediction (DSWP) models are appearing in the literature designed for quiescent interval prediction in marine applications dominated by large swell seas. The approach has focused upon spectral methods which are straightforward and intuitively attractive. However, such methods have the disadvantage that while the sea is aperiodic in nature, the standard discrete spectral processing techniques force an absolutely periodic structure onto the resulting sea surface prediction models. As it is the shape of the sea surface that is important in such applications, particularly near the end of the domain which is important, the standard windowing techniques used in signal processing work to reduce leakage artifacts cannot be employed. This has necessitated the use of end matching methods that can be both inconvenient and may reduce the fraction of the time for which legitimate predictions are available. As a result, an investigation has been undertaken of the use of finite impulse response prediction filters to provide the necessary dispersive phase shifting required in DSWP systems. The present work examines the theoretical basis for such filters and explores their properties together with their application to both long and short crested swell seas. It is shown that wide band forms of such filters are only convergent in the sense of distributions having both infinite duration impulse responses and asymptotically divergent first derivatives. However, appropriate band limitation can produce useful finite impulse responses allowing implementation via standard discrete convolution methods. It is demonstrated that despite the prediction filters having a non-causal impulse response such filters can be used in practice due to a combination of the asymmetric nature of the impulse response and the fundamental nature of the prediction process. The findings are confirmed against actual sea-wave data.     

Testing earthquake prediction methods: «The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥ 5.8»

Analyzing the tables and probability maps posted by Yan Y. Kagan and David D. Jackson in April 2002–September 2004 at http://scec.ess.ucla.edu/~ykagan/predictions_index.html and the catalog of earthquakes for the same period, the conclusion is drawn that the underlying method could be used for prediction of aftershocks, while it does not outscore random guessing when main shocks are considered.     

影像匹配中几种相似性测度的分析

对基于灰度影像匹配中的几种相似性测度进行了研究，分析了每种测度的直观意义、限制条件以及改进措施，最后通过实验结果验证了上述结论。     

水库地震主震发生时间的预测

水利工程对水库地震的抗震设防要求事先对水为地震进行预测。预测内容包括蓄水后诱发地震的危险性（可能性、）最大震级、最大震的地占紧及不库地震对场址地震动参数的影响。除主震时间的预测外，本文第一作者都曾提出过初步的预测方法。本文认为，水库地震前震波速比异常、非线性灰色模型以及前震系列回归等方法，是预测主震时间的有前途的方法。     

雅江地块的震兆分析与强震危险性探讨

分析了雅江地块的构造环境、地震活动和震兆异常，探讨2001年雅江6．0级地震、2002年新龙5．3级地震发生后雅江地块的强震趋势。重点对理塘-德巫断裂带，新龙、白玉、德格及贡觉一带地区的异常地震活动图像进行分析，认为该区存在强震填空的潜在危险性。     

北京奥运会期间NO2浓度降低原因分析

2002～2008年,北京市城区和近郊8月的NO2月均浓度大体呈现逐年下降趋势,其中前5年二者均以每年约10%的降幅下降,2008年发生显著下降,降幅达40%左右。利用嵌套网格空气质量模式系统(NAQPM/IAP),采用敏感性试验方法,评估了气象条件与污染控制措施对北京奥运会期间大气NO2浓度降低的影响,评估不同污染控制措施对NO2浓度降低的作用。研究结果表明,污染控制措施是NO2浓度降低的主要影响因素,其中面源的污染控制措施对于NO2浓度降低的作用最明显。     

珠江三角洲城市群污染综合指数预报的模式方法

应用全二阶矩表示的商斯烟团模式,重点引入烟团的合并与分解及各种清除过程,并利用广州热带模式（GZTM）所提供的华南地区细网格的三维数字化流场,来计算污染物浓度的长期变化。具体先模拟计算SO2、NOx、TSP的地面浓度日变化,通过求平均得到每种污染物的日均浓度,并和对应的污染物的日均浓度二级标准比较,得到每种污染物的污染指数Pi,最后求和得出污染综合指数P,从而对珠江三角洲地区的污染综合指数作出预报。     

我国夏季雨型的前期异常特征及预报方法的初步研究

用１９５１～１９９５年资料研究了我国东部夏季降水各雨型的前期大气环流及我国地面气象要素场的异常特征．结果表明,在冬季１月份北太平洋地区、秋季中国南海地区的海平面气压场有预报我国夏季雨型的信号．夏季不同雨型的前期冬季特征不同,我国的降水、气温场也有差异,４月份我国大范围的温度异常也是值得注意的预测信号．这些特征可以作为我国夏季雨型的预报信号及预报工具．     

Simulation of quasi-linear mesoscale convective systems in northern China: Lightning activities and storm structure

Two intense quasi-linear mesoscale convective systems(QLMCSs) in northern China were simulated using the WRF(Weather Research and Forecasting) model and the 3D-Var(three-dimensional variational) analysis system of the ARPS(Advanced Regional Prediction System) model.A new method in which the lightning density is calculated using both the precipitation and non-precipitation ice mass was developed to reveal the relationship between the lightning activities and QLMCS structures.Results indicate that,compared with calculating the results using two previous methods,the lightning density calculated using the new method presented in this study is in better accordance with observations.Based on the calculated lightning densities using the new method,it was found that most lightning activity was initiated on the right side and at the front of the QLMCSs,where the surface wind field converged intensely.The CAPE was much stronger ahead of the southeastward progressing QLMCS than to the back it,and their lightning events mainly occurred in regions with a large gradient of CAPE.Comparisons between lightning and non-lightning regions indicated that lightning regions featured more intense ascending motion than non-lightning regions;the vertical ranges of maximum reflectivity between lightning and non-lightning regions were very different;and the ice mixing ratio featured no significant differences between the lightning and non-lightning regions.     

基于迁移学习的地球物理测井储层参数预测方法研究

随着大数据和机器学习的成熟和推广应用,人工神经网络在地球物理测井预测储层参数中得到重视.本文引入迁移学习进行测井储层参数预测,以孔隙度预测神经网络模型和孔隙度含水饱和度联合预测神经网络模型为基础模型,分别以渗透率及含水饱和度预测作为目标任务进行迁移学习,以提升储层参数预测效果和效率.文中详细阐述了基于迁移学习的测井储层参数预测方法,并使用64口井的测井数据进行储层参数预测效果分析.结果表明,使用迁移学习后,渗透率模型预测效果最高可以提升58.3%;含水饱和度模型预测效果最高可以提升近40%,且最大可以节省60%的计算资源;以孔隙度预测模型为基础模型时更适合使用参数冻结的训练方式,以孔隙度含水饱和度联合预测模型为基础模型时更适合使用参数微调的训练方式.