高维PP时间序列分析在地震预报中的应用

将投影寻踪(PP)与高维时间序列分析结合起来，建立了地震PP综合预测模型。并选取祁连山地区作研究区，做了未来三个月内最大震级的短期预测，经展望式检验，合格率(预测误差≤0．5或≤10％)≥80％。中根据实际需要提出建立一定震级门限(M≥4．0)的预测具有更好的效果。本预测模型还可增加自变量、维数和改变时间尺度，并运用到其他领域中。     

PPR（投影寻踪回归）地震综合预报模型研究

PP(投影寻踪)是处理和分析高维观测数据的新兴统计方法。其基本思想是把高维数据点阵投影到低维(1～3维)子空间上,寻找出能反映原高维数据结构特征的投影以达到研究分析高维数据的目的。我们研究的目的是将投影寻踪回归技术应用于地震综合预报之中。 1 PPR基本原理概述     

HPPPR模型及其在地震综合预测研究中的初步应用

在综合考虑经典投影寻踪算法特点的基础上,针对投影寻踪计算中存在的一些不利因素,给出相应的解决思路.利用数值仿真技术进行基于粒子群优化算法与厄密特多项式构建的投影寻踪回归模型建模能力与计算精度的检验,再将其应用于多维地震时间序列综合建模预测中.计算结果和进一步分析表明,基于粒子群优化算法与厄密特多项式构建的投影寻踪回归投影寻踪模型具有简单、快速、有效的特点,在实际地震综合预测建模中取得了满意的效果,可作为地震综合预测的一种回归分析方法.     

投影寻踪软件及其在地震预报中的应用简介

投影寻踪软件及其在地震预报中的应用简介投影寻踪（ＰｒｏｊｅｃｔｉｏｎＰｕｒｓｕｉｔ，ＰＰ）是一种新兴的统计方法。它通过研究高维数据在低维子空间上的投影发现高维数据的有意义的空间结构或特征。因此成为处理、分析高维数据，尤其是来自非正态总体的高维数据的强...     

基于粒子群优化算法与厄米多项式构建地震综合预测投影寻踪回归模型

在地震综合预测投影寻踪研究工作中,投影寻踪回归算法是其中应用最多的一种方法．但一般投影寻踪回归算法构造技术较为复杂,采用多次局部光滑回归,计算量较大,外推较为繁杂,容易陷于局部解．在综合考虑传统投影寻踪回归算法特点的基础上,针对投影寻踪回归计算中存在的一些不利因素,给出了一定的解决思路:采用粒子群优化算法代替高斯 牛顿算法优化投影方向;采用厄米多项式代替分段线性光滑回归来拟合岭函数,以简化优化过程;参数优化无需分组,获得全局优化的岭函数．利用数值仿真技术进行基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型建模能力与计算精度的检验,再将其应用于多维地震时间序列和一般多维无序地震样本回归综合建模预测中．通过计算和分析表明,基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型具有简单、快速、有效的特点,在实际地震综合预测建模中取得了满意的效果,可作为地震预测的一种综合分析方法．      

门限自回归模型在地震预报中的应用初探

本文简要介绍了应用门限自回归方法处理浑沌时间序列,建立了浑沌时间序列自回归预报模型,并用其进行外推预报的基本方法及步骤。作者运用该方法建立了云南及滇西、滇东和滇西南等三个片区的半年最大震级序列的震级预报模型。由此预测了1991年上半年模型相应地区可能发生的最大地震震级。     

时间序列的投影寻踪自回归在新疆地震预报中的应用

选取新疆各分区一年和半年最大震级时间序列资料 ,运用投影寻踪自回归方法 ,建立相应的中、短期预测模型。并把预测模型应用于新疆年中和年度地震趋势分析 ,其预测结果是可信的 ,有一定的现实意义。     

山西年度地震趋势的数值预测

采用"自激励门限自回归模型"(SETAR)对山西1970年以来年度最高震级序列进行了分析计算,给出了1998年以来年度最高预测震级,结果表明,该模型对年度地震趋势预测具有良好的预测效果,这种建模方法有效,模型可信.     

地震序列类型早期判断的可能性

详细讨论利用地震现场提供的１天、３天和７天观测资料，预测地震序列类型和最大强余震震级的可能性和现实性。４７个Ｍ≥６．０地震的地震序列实例检验表明，利用７天内的观测资料，准确率可达到８５．１％。因此，使用ｂ值横截距法有可能在早期预测最大强余震的震级并判断地震序列的类型。     

半年度地震趋势数值预测

采用"自激励门限自回归模型"(SETAR)对山西、河北平原带及郯庐带1970年以来半年度最高震级序列进行了分析计算,给出了1998年以来山西、河北平原带及郯庐带半年度最高预测震级。结果表明,该模型对半年度地震趋势预测具有良好的预测效果,这种建模方法有效,模型可信。     

新疆天山地区 PP回归综合预报模型研究及预报效能评价

运用基于 PP回归理论的数值型地震综合预报软件系统, 对新疆天山地区进行了 PP回归建模研究。 通过回顾性 PP回归动态建模预测检验,对其预报效能进行了评价。 结果认为,该模型作为时序性数值预报模型具有较好的中短期预报效能。 尤其对地震样本量大、震级分布完备的地区,其预报效能更好。     

Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin

ABSTRACT

The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.     

Seismic comprehensive forecast based on modified project pursuit regression

In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression (PPR) is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation: to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast.     

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.     

Study on medium-short term earthquake forecast in Yunnan Province by precursory events

Introduction Yunnan is a very active region of earthquake in China. Since Yunnan Regional Seismological Network established in 1965, 37 years have past and thousands of seismic events have been recorded. Among them, 9 are great earthquakes of M7.0, more than 150 are moderately strong earthquakes of M =5.0~6.9 and about 6 000 are earthquakes of M3.0. Figure 1 shows the epicenter distribution of M3.5 earthquakes occurred in 1965~2002 in Yunnan region and Figure 2 the magnitude-frequency dis…     

余震的序列参数稳定性和余震短期发生率预测效能的连续评估——以2014年云南鲁甸6.5级地震为例

为考察目前国际上广泛使用、对真实地震序列描述最好的"传染型余震序列模型"(ETAS)在主震后的序列参数拟合、余震短期发生率预测的效能,本研究以2014年云南鲁甸MS6.5地震序列为例,采用滑动连续拟合与预测的方式,考察了ETAS模型参数的动态变化和余震短期发生率预测的实际效能.连续滑动拟合结果表明,在主震发生后的早期阶段,α值有明显的不稳定变化,在震后5.10天稳定在1.6~2.0;p值在震后25.00天内由1.07逐渐下降至0.78左右,其后稳定在0.72~0.85;b值在震后35.00天内逐渐由0.80增加至0.95,其后稳定在0.93~0.97.对连续滑动预测结果的N-test检验表明,余震发生率预测会出现部分失效现象,1天预测时间窗失效比例约为12%、3天预测时间窗失效比例为6%.建议可在震后早期采用1天的较短预测时间窗,而在序列参数较为稳定时段采用较长的3天预测时间窗.     

基于ETAS模型对青海地区余震预测效能评估的研究

为系统评估青海地区余震短期发生率的预测效能,以及构建适合地震活动特点的余震早期预测策略和预测指标体系,利用国际上当前较为前沿的时间序列ETAS模型和"瘦化算法"对青海地区2009年以来的8个地震的早期余震序列参数进行拟合,并利用N-test检验方法对预测结果进行回溯性的效能评估。研究表明:ETAS模型和"瘦化算法"对青海地区的余震发生率具有很好的预测能力,建议采用3天的预测时间窗,且对序列早期阶段进行应用,或可取得"最佳"的预测效果。     

基于小波变换和支持向量机的中国大陆强震预测

将小波变换和支持向量机用于中国大陆年度最大地震震级预测。 先用小波变换把中国大陆年度最大地震序列分解成几个不同尺度水平(频率)的子序列, 然后使用支持向量机对分解后的子序列分别进行预测, 最后通过重构几个子序列的支持向量机预测结果得到最终预测结果, 预测次年中国大陆最大地震震级。 与支持向量机和神经网络方法对比, 结果表明小波变换和支持向量机相结合方法具有更高的预测精度, 预测效果很好, 说明此方法可用于地震时间序列预测。