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1.
Earthquake occurrence is well-known to be associated with structural changes in underground dynamics, such as stress level and strength of electromagnetic signals. While the causation between earthquake occurrence and underground dynamics remains elusive, the modeling of changes in underground dynamics can provide insights on earthquake occurrence. However, underground dynamics are usually difficult to measure accurately or even unobservable. In order to model and examine the effect of the changes in unobservable underground dynamics on earthquake occurrence, we propose a novel model for earthquake prediction by introducing a latent Markov process to describe the underground dynamics. In particular, the model is capable of predicting the change-in-state of the hidden Markov chain, and thus can predict the time and magnitude of future earthquake occurrences simultaneously. Simulation studies and applications on a real earthquake dataset indicate that the proposed model successfully predicts future earthquake occurrences. Theoretical results, including the stationarity and ergodicity of the proposed model, as well as consistency and asymptotic normality of model parameter estimation, are provided. 相似文献
2.
This paper introduces an extension of the traditional stationary linear coregionalization model to handle the lack of stationarity. Under the proposed model, coregionalization matrices are spatially dependent, and basic univariate spatial dependence structures are non-stationary. A parameter estimation procedure of the proposed non-stationary linear coregionalization model is developed under the local stationarity framework. The proposed estimation procedure is based on the method of moments and involves a matrix-valued local stationary variogram kernel estimator, a weighted local least squares method in combination with a kernel smoothing technique. Local parameter estimates are knitted together for prediction and simulation purposes. The proposed non-stationary multivariate spatial modeling approach is illustrated using two real bivariate data examples. Prediction performance comparison is carried out with the classical stationary multivariate spatial modeling approach. According to several criteria, the prediction performance of the proposed non-stationary multivariate spatial modeling approach appears to be significantly better. 相似文献
3.
We present estimates of future earthquake rate density (probability per unit area, time, and magnitude) on a 0.1-degree grid for a region including California and Nevada, based only on data from past earthquakes. Our long-term forecast is not explicitly time-dependent, but it can be updated at any time to incorporate information from recent earthquakes. The present version, founded on several decades worth of data, is suitable for testing without updating over a five-year period as part of the experiment conducted by the Collaboratory for Study of Earthquake Predictability (CSEP). The short-term forecast is meant to be updated daily and tested against similar models by CSEP. The short-term forecast includes a fraction of our long-term one plus time-dependent contributions from all previous earthquakes. Those contributions decrease with time according to the Omori law: proportional to the reciprocal of the elapsed time. Both forecasts estimate rate density using a radially symmetric spatial smoothing kernel decreasing approximately as the reciprocal of the square of epicentral distance, weighted according to the magnitude of each past earthquake. We made two versions of both the long- and short-term forecasts, based on the Advanced National Seismic System (ANSS) and Preliminary Determinations of Epicenters (PDE) catalogs, respectively. The two versions are quite consistent, but for testing purposes we prefer those based on the ANSS catalog since it covers a longer time interval, is complete to a lower magnitude threshold and has more precise locations. Both forecasts apply to shallow earthquakes only (depth 25 km or less) and assume a tapered Gutenberg-Richter magnitude distribution extending to a lower threshold of 4.0. 相似文献
4.
Ergodicity is a behavior generally limited to equilibrium states and is here defined as the equivalence of ensemble and temporal
averages. In recent years, effective ergodicity is identified in simulated earthquakes generated by numerical fault models
and in real seismicity of natural fault networks by using the Thirumalai-Mountain metric. Although the effective ergodicity
is already reported for Taiwanese seismicity, an immediate doubt is the unrealistic gridded sizes for discretizing the seismic
data. In this study, we re-examined the effective ergodicity in Taiwanese seismicity by using reasonable gridded sizes which
corresponded with the location errors in the real earthquake catalogue. Initial time and magnitude cut-off were examined for
the validity of ergodic behavior. We found that several subsets extracted from Taiwanese seismicity possessed effectively
ergodic intervals and all terminations of these ergodic intervals temporally coincided with the occurrences of large earthquakes
(M
L
< 6.5). We thus confirm the ergodicity in the crustal seismicity by using the Thirumalai-Mountain metric. 相似文献
5.
Cv值是描述前兆分布非均匀性的一个有效指标。不同观测指标获取的震前Cv值异常证明了该方法预测地震的有效性。然而Cv值与观测指标的时间(或空间)分布形式(如周期分布、准周期分布、泊松分布、成丛分布等)并不是简单的对应关系。中在回顾Cv值研究与应用进展的基础上，对Cv值与其分布特征的关系进行了深入分析，指出了应用中存在的问题，从而加深对Cv值物理意义的理解。并给出了Cv值识别前兆群体异常的判别法则。 相似文献
6.
7.
The conventional spectral analysis method for interpretation of magnetic data assumes stationary spatial series and a white‐noise source distribution. However, long magnetic profiles may not be stationary in nature and source distributions are not white. Long non‐stationary magnetic profiles can be divided into stationary subprofiles following Wiener filter theory. A least‐squares inverse method is used to calculate the scaling exponents and depth values of magnetic interfaces from the power spectrum. The applicability of this approach is demonstrated on non‐stationary synthetic and field magnetic data collected along the Nagaur–Jhalawar transect, western India. The stationarity of the whole profile and the subprofiles of the synthetic and field data is tested. The variation of the mean and standard deviations of the subprofiles is significantly reduced compared with the whole profile. The depth values found from the synthetic model are in close agreement with the assumed depth values, whereas for the field data these are in close agreement with estimates from seismic, magnetotelluric and gravity data. 相似文献
8.
基于川滇地区2011—2014年的重力复测资料,系统分析了区域重力场时-空动态变化及其与2012年云南彝良MS5.7、2013年四川芦山MS7.0、2014年云南鲁甸MS6.5和四川康定MS6.3地震发生的关系.结合GPS、水准观测成果和区域地质构造动力环境,进一步研究了区域重力场变化的时空分布特征及其机理,讨论了近期区域重力场动态变化的强震危险含义.结果表明:1重力变化与川滇地区断裂构造活动存在密切空间联系,重力变化较好地反映了伴随活动断层的物质迁移和构造变形引起的地表重力变化效应.2重力资料对测区内2012年以来发生的4次MS5.7以上强震均有较好反映,地震前震中区及其附近观测到明显的区域性重力异常及重力变化高梯度带,可能是地震孕育过程中观测到的重力前兆信息.3区域重力场动态演化大体反映了青藏高原物质东流的动态效应,龙门山断裂带地壳受挤压隆起、面压缩率和重力上升变化的特征最为显著.4重力场的空间分布及其随时间变化与地壳垂直与水平运动及地质构造活动等观测结果有一定的对应关系,强震易发生在重力变化四象限分布中心地带或正、负异常区过渡的高梯度带上,研究区的一些重力异常部位仍存在中-长期大震危险背景. 相似文献
9.
在地震勘探随机噪声压制领域,噪声通常被假设为平稳、高斯随机过程的信息.然而,在某些情况下,这样的假设并不准确.本文应用现代统计检验方法对地震勘探随机噪声的平稳性、高斯性和线性进行了研究.结果表明地震勘探随机噪声并不是传统意义上认为的平稳随机过程,其平稳性受到噪声时长和采集环境复杂程度的影响.发现噪声时间越长,采集环境越复杂,随机噪声的平稳性越差,但是对于短时长随机噪声而言,其可以近似认为是平稳的.同时,采集环境的复杂程度也影响着随机噪声的高斯性和线性特性,环境条件越复杂,随机噪声高斯性越好,线性特性越差,但总的来说随机噪声可以归为线性非高斯随机过程. 相似文献
10.
本文讨论的是没有强震资料的地区在做地震动模拟时遇到的一些问题。模拟的地震动时程曲线基于统计的衰减数据,如地面加速度峰值或速度反应谱。由于这些数据并不包含有关地震频率成分的信息,要使用相同的统计反应谱对地震动可以随机模拟为稳态或准稳态的设想加以证明。 相似文献
11.
Phase folding algorithms are conventionally used in periodicity analyses using X-ray astronomy pulsar. These allow for accurate identification of the cycle and phase characteristics of the physical parameters of the periodic variation. Although periodic variations in earthquake activity have long been studied, this paper is the first to apply the phase folding algorithm to the analysis of shallow (<70 km) seismic data for the period 1973–2010. The goal is to study the phase distribution characteristics of earthquake frequencies and we see a connection between earthquake occurrence and solar and lunar cycles. First, the rotation of the Sun may play a significant role in impacting on the occurrence time of earthquakes with magnitudes of less than 6.0. This may be especially pertinent for earthquakes with magnitudes between 5.0 and 6.0, when the modulation ratio reaches 12 %. The Moon’s gravity, which is generally thought to have the greatest influence on the global environment, may actually play less of a role on earthquake timing than the rotation of the Sun. Second, when we consider the world to be divided into 72 local regions based on latitude and longitude, we can see that there are more than a dozen regions with significant non-uniform distributions of earthquake occurrence time. In these regions, the ratio of χ 2 to the number of degrees of freedom far exceeds five. As a result, we posit that some factors associated with the Sun–Earth–Moon relationship may trigger earthquake activity under certain temporal and spatial conditions. 相似文献
12.
A short-term predictive system for surface currents from a rapidly deployed coastal HF radar network
Donald Barrick Vicente Fernandez Maria I. Ferrer Chad Whelan Øyvind Breivik 《Ocean Dynamics》2012,62(5):725-740
In order to address the need for surface trajectory forecasts following deployment of coastal HF radar systems during emergency-response situations (e.g., search and rescue, oil spill), a short-term predictive system (STPS) based on only a few hours data background is presented. First, open-modal analysis (OMA) coefficients are fitted to 1-D surface currents from all available radar stations at each time interval. OMA has the effect of applying a spatial low-pass filter to the data, fills gaps, and can extend coverage to areas where radial vectors are available from a single radar only. Then, a set of temporal modes is fitted to the time series of OMA coefficients, typically over a short 12-h trailing period. These modes include tidal and inertial harmonics, as well as constant and linear trends. This temporal model is the STPS basis for producing up to a 12-h current vector forecast from which a trajectory forecast can be derived. We show results of this method applied to data gathered during the September 2010 rapid-response demonstration in northern Norway. Forecasted coefficients, currents, and trajectories are compared with the same measured quantities, and statistics of skill are assessed employing 16 24-h data sets. Forecasted and measured kinetic variances of the OMA coefficients typically agreed to within 10–15%. In one case where errors were larger, strong wind changes are suspected and examined as the cause. Sudden wind variability is not included properly within the STPS attack we presently employ and will be a subject for future improvement. 相似文献
13.
地震预测模型优化方法研究 总被引：2，自引：0，他引：2
在分析现有基于观测指标“异常-正常”的“二态”地震前兆模式不足的基础上, 提出了异常可表现为多种状态的“多态”前兆模式, 给出“多态”前兆模式下预测效能判定指标, 进一步提出了基于预测效能最优的单项预测模型参数的选择方法。 在广义(时间、 空间、 时-空联合的预测指标)“多态”前兆模式下, 将所建立的模型应用于我国华北和南北带地区, 分别确定了两地区活断层预测地震(MS≥6)空间分布的最优模型(分别为断层周围20 km、 30 km)及其预测效能(分别为0.42、 0.34)。 相似文献
14.
I. Cordery R. Mehrotra M. J. Nazemosadat 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(6):765-771
In modern hydrological practice large confidence is placed on modelling results that are used for planning and design. This
is especially the case where the modelling results have been carefully verified against independent data. An underlying assumption
of the calibration/verification process is that the whole data series is stationarity. Standard parametric and non-parametric
tests are available for examining the stationary of hydrologic time series but it has been shown here that these may be inadequate
for that purpose unless applied with care. Annual, seasonal, monthly and daily time series of precipitation and climate data
were examined considering parts of the series formed using sequential windows. Seven standard parametric and non-parametric
tests were applied to these relatively long series and while it was shown that some tests suggested that all series were stationary,
most series were shown to be non-stationary in more than one of the tests, some of them at very high levels of significance.
This apparently hidden non-stationarity could have very large effects on water resources modelling. These effects would have
considerable influence in calibration and verification of models and in simulation of long series of water resources characteristics
and could be especially important as the effects of climate change become more pervasive. 相似文献
15.
通过对黑龙江亚板块及其临近区域的不同时段地震活动图像研究分析,发现了中强震前地震活动图像的一些特点,再回归到地震动力学环境中去,建立了理论解释模型和两条判据假说。以此为基础并结合近年来地震活动图像综合分析后,我们在2005年下半年黑龙江省地震趋势会商意见中提出:松嫩盆地是5级以上中强震的主要孕震区。2005年7月25日林甸Ms 5．1地震对此给予了验证。本文对此过程阐述并作进一步分析,希望这种综合预报方法对地震预报工作有所帮助。 相似文献
16.
IntroductionThemodelofspatialandtemporalsynthesizedgainforastrongearthquakeoccurrenceisdevelopedasthebasisofTheDynamicSoftwareSystemforMiddle-andLong-termEarthquakeForecast.Themodelisestablishedaccordingbothtothestatisticalprincipleandtotheobservatio... 相似文献
17.
我国的震害预测与防御对策研究已开展多年,以前主要是以文件格式存储和管理震害预测数据。由于震害预测数据量多面广,那这种数据存储和管理方式不利于数据的管理与维护,也不利于GIS二次开发。建立一套较为完整的震害预测数据库,不仅有助于防震减灾信息管理与地震应急辅助决策系统后台的数据维护和管理,也便于更有效地应用所有数据资源。介绍的广州市部分城区震害预测基础(属性)数据库,采用SQL Server 2000,基于C/S结构及数据字典、ArcSDE管理基本上可满足上述要求。 相似文献
18.
Earthquake Forecasting Using Hidden Markov Models 总被引：1，自引：0，他引：1
Daniel W. Chambers Jenny A. Baglivo John E. Ebel Alan L. Kafka 《Pure and Applied Geophysics》2012,169(4):625-639
This paper develops a novel method, based on hidden Markov models, to forecast earthquakes and applies the method to mainshock
seismic activity in southern California and western Nevada. The forecasts are of the probability of a mainshock within 1,
5, and 10 days in the entire study region or in specific subregions and are based on the observations available at the forecast
time, namely the interevent times and locations of the previous mainshocks and the elapsed time since the most recent one.
Hidden Markov models have been applied to many problems, including earthquake classification; this is the first application
to earthquake forecasting. 相似文献
19.
钻孔应变,应力测量区域特征初探 总被引：1，自引：0，他引：1
我国钻孔应变、应力测量依不同时段区域变化形态有明显差异;短期(一月内)、中期(一年内)区域变化特征不显著。正常形态显示平稳、应变测量显示固体潮形态、应力测量显示年变形态。震前异常多呈现突升、突降式的应变阶跃、固体潮畸变、年变形态改变。长期(10年内)与其构造部位、地震活动性及区域构造运动,矢量速率有一定的相关性,因而存在着明显的区域变化特征。识别不同区域的曲线变化特征,对判定震兆信息有一定的指导作用,从而提高了应变、应力测量资料的应用价值。 相似文献