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1.
提出一种用于震级序列预报的新方法，给出其数学模型，并导出实用公式和写出用此方法编程的程序框图。对大华北地区１９００年以来的每年最大震级序列建模，给出了１９８４－１９９４年的预测结果。 相似文献
2.
本文根据邢台，海城，唐山３大地震序列每年最大震级资料，展示了３者的起伏特点及其以给定时间计算新一年度最大震级的办法，同时也探讨了超限值与大区强震发生时间的随属关系及其对中期大形势预报的意义。 相似文献
3.
本文根据邢台、海城、唐山３大地震序列每年最大震级资料，展示了３者的起伏特点及其在给定时间计算新一年度最大震级的办法，同时也探讨了超限值与大区强震发生时间的随属关系及其对中期大形势预报的意义 相似文献
4.
通常用成功率和报警率来表示地震预报的效率，它们分别由成功预报数与总预报数和成功预报数与应被预报的事件数之比确定，这里，我们针对震级高于给定阈值的事件来设想预报系统下，且假定系统单一指标“是”或“否”。对于这样一个预报系统，用于确定系统预报率的事件震级的观测误差引起了识别预报目标数的增加和识别成功预报数的减少，事实上，当忽略事件震级的不确定性时，预报率可能被显著低估。这可能会导致舍弃有价值的预报系统或低估实际的地震风险。本文给出了估计预报率的一种方法，该方法考虑了独立震级分布的任何对称的观测震级的误差，目标识别的不确定性可以表示为从实际到观测震级概率转换的结果，给定预报效率参数，偶然连接率和转换概率，由预报和转换过程的分析求解这个预报率估计问题，假观测误差为正态分布，预报／转换过程的数值试验和蒙特卡罗模拟看来证实了所得的解是正确的。 相似文献
5.
只有预报震级和实际震级两个值都用相同尺度时，预报震级和实际震级的直接比较地是允许的。由事实来看，雅典国家观测台地震研究所公开发布ＭＥＱ＝ＭＬ＋０．５，ＶＡＮ很早地清楚地表示，预报值Ｍｐｒｅｄ是指ＭＬ＋０．５，因此，ＶＡＮ预报的本身一致性评估值应当包括Ｍｐｅｒｄ和实际的ＭＬ＋０．５直接比较。 相似文献
6.
以生物有机体演化的基本思想构造数学模型,对大华北地区1900年以来的每年最大震级序列建模,给出了1983—1992年的逐年追踪预报结果。 相似文献
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8.
时间-震级可预报模式在南北地震带分段危险性评估中的应用 总被引：9，自引：1，他引：8
为了定量评估南北地震带不同段落的长期地震危险性,引入了时间－震级可预报模式。在详细地震复发行为的基础上,沿南北地震带划分了３９个震源区。利用其中２７个震源的多轮回复发资料初步建立起时间－震级可预报统计模型。计算结果表明,不同震源的地震复发表现出较好的时间可预报行为以及相对较弱的震级可预报行为。以时间可预报模型为基础,对所有震源区未来地震的复发概率进行了估算,同时,用震级可预报模型对未来地震的震级作 相似文献
9.
本文把神经网络方法引进地震预报研究当中。使用地震频次，最大震级，平均震级，等价地震次数等多项地震活动性指标作为神经网络的输入，未来时段内的最大地震震级作为其输出，可以对某一固定地区的最大地震震级作出中近期预报。选用的神经网络模型为含两个中间层的前向模型，并采用ＢＰ算法。所得结果表明，用神经网络方法可以在一定精度范围内使震级预报的内检符合率达到１００％，在本文的例子中，外推预报准确率达到６０％以上。 相似文献
10.
本文应用模糊信息检索方法,选取频度变化率、平均震级、震级变化率、上一时段最大震级四项指标,对乌鲁木齐地区1972年以来地震活动进行了初步分析与研究,并探讨了预报指标对应地震的灵敏度和预报原则。 相似文献
11.
A univariate model for long-term streamflow forecasting 总被引：1，自引：0，他引：1
P. F. Krstanovic V. P. Singh 《Stochastic Environmental Research and Risk Assessment (SERRA)》1991,5(3):173-188
This paper, the first in a series of two, employs the principle of maximum entropy (POME) via maximum entropy spectral analysis (MESA) to develop a univariate model for long-term streamflow forecasting. Three cases of streamflow forecasting are investigated: forward forecasting, backward forecasting (or reconstruction) and intermittent forecasting (or filling in missing records). Application of the model is discussed in the second paper. 相似文献
12.
Qian Wang Xinxin Yin Changsheng Jiang Cong Jiang Yan Zhang Hongyu Zhai Yanbao Zhang Guijuan Lai Fengling Yin 《地震科学（英文版）》2021,34(3):286-298
In the new types of industrial activities including unconventional energy extraction associated with shale gas and hot dry rock, gas reservoir operations, CO2 geological storage, undergoing research on induced earthquake forecasting has become one of the forward positions of current seismology. As for the intense actual demand, the immature research on induced earthquake forecasting has already been applied in pre-assessment of site safety and seismic hazard and risk management. This work will review systematically recent advances in earthquake forecasting induced by hydraulic fracturing during industrial production from four aspects: earthquake occurrence probability, maximum expected magnitude forecasting, seismic risk analysis for engineering and social applications and key scientific problems. In terms of earthquake occurrence probability, we introduce statistical forecasting models such as an improved ETAS and non-stationary ETAS and physical forecasting models such as Seismogenic Index (SI) and hydro-mechanism nucleation. Research on maximum expected magnitude forecasting has experienced four stages of linear relationship with net injection volume of fluid, power exponential relationship and physical forecasting regarding fault parameters. For seismic risk analysis, we focus on probabilistic seismic hazard assessment and quantitative geological susceptibility model. Furthermore, this review is extended to key scientific problems that contain obtaining accurate fault scale and environmental stress state of reservoir, critical physical process of runaway rupture, complex mechanism of fault activation as well as physical mechanism and modeling of trailing effect. This work in understanding induced earthquake forecasting may contribute to unconventional energy development and production, seismic hazard mitigation, emergency management and scientific research as a reference. 相似文献
13.
随着新观测技术和理论的进一步发展,同位素地球化学方法在地震监测预测研究中发挥了越来越重要的作用。通过同位素地球化学方法确定地下流体来源,研究地下流体循环特征,分析地震前兆异常的成因,评估地质构造活动的程度,开展地震预测研究。同位素示踪技术还可以结合深源流体监测和地球物理方法,揭示地震孕育、流体与震源之间的关系。此外,同位素地球化学还可以构建断裂带流体地球化学背景特征,用于地震监测点映震效能的评估,提高地震监测预测的准确性,为地震新监测点的布设和震情跟踪提供技术支撑。通过对现今同位素在地震监测预测中所使用的方法、技术及国内外应用情况的总结分析,力图全面认识同位素地球化学在地震监测预测应用中的现状及发展趋势。 相似文献
14.
Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis 总被引：3，自引：0，他引：3
In this study, a new method of stand-alone short-term spring snowmelt river flood forecasting was developed based on wavelet and cross-wavelet analysis. Wavelet and cross-wavelet analysis were used to decompose flow and meteorological time series data and to develop wavelet based constituent components which were then used to forecast floods 1, 2, and 6 days ahead. The newly developed wavelet forecasting method (WT) was compared to multiple linear regression analysis (MLR), autoregressive integrated moving average analysis (ARIMA), and artificial neural network analysis (ANN) for forecasting daily stream flows with lead-times equal to 1, 2, and 6 days. This comparison was done using data from the Rideau River watershed in Ontario, Canada. Numerical analysis was performed on daily maximum stream flow data from the Rideau River station and on meteorological data (rainfall, snowfall, and snow on ground) from the Ottawa Airport weather station. Data from 1970 to 1997 were used to train the models while data from 1998 to 2001 were used to test the models. The most significant finding of this research was that it was demonstrated that the proposed wavelet based forecasting method can be used with great accuracy as a stand-alone forecasting method for 1 and 2 days lead-time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year-to-year, and that there is a relatively stable phase shift between the flow and meteorological time series. The best forecasting model for 1 day lead-time was a wavelet analysis model. In testing, it had the lowest RMSE value (13.8229), the highest R2 value (0.9753), and the highest EI value (0.9744). The best forecasting model for 2 days lead-time was also a wavelet analysis model. In testing, it had the lowest RMSE value (31.7985), the highest R2 value (0.8461), and the second highest EI value (0.8410). It was also shown that the proposed wavelet based forecasting method is not particularly accurate for longer lead-time forecasting such as 6 days, with the ANN method providing more accurate results. The best forecasting model for 6 days lead-time was an ANN model, with the wavelet model not performing as well. In testing, the wavelet model had an RMSE of 57.6917, an R2 of 0.4835, and an EI of 0.4366. 相似文献
15.
Mid‐ to long‐term runoff forecasting is important to China. Forecasting based on physical causes has become the trend of this field, and recognition of key factors is central to recent development. Here, global sensitivity analysis based on back‐propagation arithmetic was used to calculate the sensitivity of up to 24 factors that affect runoff in the Nenjiang River Basin. The following five indices were found to be key factors for mid‐ to long‐term runoff forecasting during flood season: Tibetan Plateau B, index of the strength of the East Asian trough, index of the area of the northern hemisphere polar vortex, zonal circulation index over the Eurasian continent and index of the strength of the subtropical high over the western Pacific. The hydrological climate of the study area and the rainfall–runoff laws were then analysed in conjunction with its geographical position and topographic condition. The rationality of the results can be demonstrated from the positive analysis point of view. The results of this study provide a general method for selection of mid‐ to long‐term runoff forecasting factors based on physical causes. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
16.
Jan F. Adamowski 《水文研究》2008,22(25):4877-4891
In this study, short‐term river flood forecasting models based on wavelet and cross‐wavelet constituent components were developed and evaluated for forecasting daily stream flows with lead times equal to 1, 3, and 7 days. These wavelet and cross‐wavelet models were compared with artificial neural network models and simple perseverance models. This was done using data from the Skrwa Prawa River watershed in Poland. Numerical analysis was performed on daily maximum stream flow data from the Parzen station and on meteorological data from the Plock weather station in Poland. Data from 1951 to 1979 was used to train the models while data from 1980 to 1983 was used to test the models. The study showed that forecasting models based on wavelet and cross‐wavelet constituent components can be used with great accuracy as a stand‐alone forecasting method for 1 and 3 days lead time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year‐to‐year, and that there is a relatively stable phase shift between the flow and meteorological time series. It was also shown that forecasting models based on wavelet and cross‐wavelet constituent components for forecasting river floods are not accurate for longer lead time forecasting such as 7 days, with the artificial neural network models providing more accurate results. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
17.
选取华北地区１９９０￣１９９８年８月较完整的水氡观测资料,笔者采用ｘ＾２统计检验法识别前兆异常,利用笔者建立的Ｂａｙｅｓ判别分析方法,对该地区水氡异常与中强震活动性的关系进行了内符检验和外推预测。在风险代价比Ｋｄｎ取４的情况下,内符检验的有震报准率ｃ为０．７１,预报占时率ｂ为０．３３,Ｒ值可达０．３８;外推有震报效率ｃ为０．５,时空占有率０．０５,Ｒ值为０．４５,能够正确预测１９９８年１月１０日张 相似文献
18.
Antecedent anomalies of sea surface temperature and atmospheric circulation are important signals for making long-term streamflow forecasts. In this study, four groups of ocean-atmospheric indices, i.e, El Niño Southern Oscillation (ENSO), the Northern Hemisphere atmospheric circulation, the Southern Hemisphere atmospheric circulation (SAC), and the Western Pacific and Indian Ocean SST (WPI), are evaluated for forecasting summer streamflow of the Yangtze River. The gradient boosting regression tree (GBRT) is used to forecast streamflow based on each group of indices. The score based on receiver operating characteristics (ROC) curves, i.e., area under the ROC curve (AUC), is used to evaluate skills of models for identifying the high category and the low category of summer streamflow. It is found that the ENSO group and the SAC group show higher AUC values. Furthermore, both AUC values of GBRT models and individual indices show that the low flow years are easier to be identified than the high flow years. The result of this study highlights the skill from the Southern Hemisphere circulation systems for forecasting summer streamflow of the Yangtze River. Results of relative influences of predictors in GBRT models and AUC of individual indices indicate some key ocean-atmospheric indices, such as the Multivariate ENSO Index and the 500-hPa height of the east of Australia. 相似文献
19.
Hideo Wada 《Surveys in Geophysics》1975,2(1):73-115
This article describes long-range forecasting, examples in Japan being mainly quoted. The article stresses the approach with which the author is most familiar, namely that used in the Japan Meteorological Agency; it employs statistical, synoptic, and physical techniques which contain implicitly many of the considerations used in long-range forecasting in many countries. After World War II, with an increase in quantity of upper-air data over the Northern Hemisphere, many studies of long-range forecasting were made in relation to the general circulation of the atmosphere. For the past several years the studies of long-range forecasting in Japan have been especially concentrated on the development of the techniques of seasonal weather forecasting. The relevant approaches to long-range forecasting are now being developed from synoptic and dynamical viewpoints in many countries, by the use of data in both the troposphere and the stratosphere over the globe and electronic computers. 相似文献
20.
Abstract The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7–9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall. Editor Z.W. Kundzewicz Citation Singhrattna, N., Babel, M.S. and Perret, S.R., 2012. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables. Hydrological Sciences Journal, 57 (1), 26–41. 相似文献