K_(cv)值在河北及邻区地震预测中的应用研究

地震空间分布非均匀性指标Cv值作为一种有效的预测指标近几年来得到了广泛应用。然而受空间分布的事件样本数大小的影响,Cv值会有所改变。本文采用Cv值标准化后的Kcv值,统计了河北及邻区的地震事件空间非均匀性分布,并与地震时空概率增益综合预测模型相结合,进行研究区地震预测。依据河北及邻区40多年的地震观测资料进行了Kcv值扫描和震例统计,研究了该方法应用于河北地区的预测效能及最优时间尺度。对研究区2008—2012年M5及以上地震的概率增益、地震发生概率预测与实际地震发生情况的回溯性检验结果表明,此方法应用于河北地区地震发生概率的预测是可行的。依据上述方法,对2013—2017年河北及邻区M5及以上地震发生的概率增益和地震发生概率进行了预测。     

地震前兆群体空间非均匀性指标Cv值及其应用问题探讨

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。     

地震前兆时空非均匀性指标Cv值的实验检验

在双向加载条件下，对两种构造和介质不同的岩石标本进行变形实验，测量了标本表面不同部位的应变．在此基础上，利用王晓青和陈学忠等提出的描述地震前兆非均匀分布的参量————Cv值，分析了标本变形失稳过程中应变异常的分布，以期从实验角度检验Cv值方法并探讨其物理意义．研究表明，Cv值的变化与岩石变形特征的变化有关，是描述前兆分布非均匀性的一种有效指标；Cv值在失稳发生前先异常上升，然后下降，并在Cv值恢复过程中或恢复后伴随着失稳事件的发生．因此，Cv值是一种有效的预报指标．      

河北及邻区地震时空概率增益综合预测研究

结合地震空间分布非均匀性指标Kcv值预测方法和图像信息学算法对河北及邻区2013~2017年ML≥5.0地震发生概率增益进行预测。根据河北及邻区40多年的地震观测资料,对地震空间分布非均匀性指标Kcv值进行了空间扫描和震例统计,得到了此方法应用于河北地区的预测效能,并对河北及邻区2013~2017年ML≥5.0地震发生概率增益进行了预测;对图像信息学算法在研究区进行了地震危险性概率预测的回溯性检验,得到了此方法应用于研究区的预测效能R值及概率增益K值,并进行了5年尺度的地震危险性预测。在上述2个单项预测方法的基础上,基于概率增益综合预测模型,得到了5年尺度河北及邻区ML≥5.0地震发生概率增益的综合预测结果。     

地壳介质破裂的尺度理论在研究山丹-民乐 6.1级地震的地震学前兆特征中的应用

介绍了Allegre的地壳介质破裂的尺度理论和根据该理论建立一种预测地震发生时间方法的可能性. 作为震例研究了甘肃山丹——民乐6.1级地震的地震学前兆（地震空间相关长度和尾波Qc值）特征. 结果表明,地震空间相关长度和尾波Qc值在震前都呈增长趋势,用幂次率关系拟合了这两种参数的上升变化形态. 这为利用地震空间相关长度和尾波Q值的监测资料建立一种预测地震发生时间的方法和找到预测指标打下了基础.      

川滇地区强震活动前b值的时空分布特征

由于地震活动性参数b值能够反映地壳环境应力的大小,因此基于b值的地震活动性图像研究成为地震预测研究和实践最直观明了的方法之一。川滇地区地震活动频繁,是检验各种地震预测方法的理想实验场。为了研究川滇地区b值分布与强震活动的关系,利用过去40多年的小震资料分析了该区域1981年以来19次M6.5地震与b值的时空分布关系。结果表明:虽然川滇地区b值空间分布特征与强震活动之间关系并不显著,不到50%的强震发生在b值小于区域平均值的地区,但是震前b值的时间分布呈现系统地下降,58%的震源区的b值在强震前出现显著下降趋势。结合其他的地震、地质资料,较短时间内的地震活动性资料可以为特定地区的地震潜在危险性的判定提供一定的启示。     

2003年岷县5.5级地震地震学前兆特征及预报意义

研究了2003年甘肃岷县5.5级地震的地震学前兆的变化特征,显示尾波Qc值和地震空间相关长度在震前都呈增长趋势,用幂次率关系对这种变化进行了拟合。结果为利用尾波Q值和地震空间相关长度的监测资料建立预测地震发生时间的方法和指标打下了基础。     

地震力标度σ值及其在地震短期预测中的应用

基于地震波能量,利用数字地震波形资料,提出地震力标度σ值定量预测指标,详细介绍了σ值计算方法,研究了强震前σ值时间和空间变化规律。结果表明:①地震力标度σ值与震级ML存在lgσ=0.98ML+6.93标度关系,σ值是一个表征地震时震源处断层平均驱动力的物理量,其大小介于地震频度和能量之间;②云南地区σ值会出现类似于正弦波的高低起伏周期变化,发生于波峰附近的强震绝大多数以连发的形式发生,而波谷附近则以单个地震发生为主,以σ值曲线从谷底上升1.5个月为起报时间点,则3个月及其以内发生强震的可能性为78%;③σ值空间分布表明,主震前2～4个月σ值在较大范围内会出现高值异常,随着主震的临近,σ高值会向未来主震震中及附近迁移、集中和收缩,主震发生前很短时间(1个月内),σ值会迅速变小,临震前地震活动异常平静现象明显。     

长江下游—南黄海地震带地震活动性研究

长江下游—南黄海地震带位于华北地震区东南部, 带内地震主要受长江下游和南黄海海域内一系列断裂的控制, 以中强地震活动为主, 是地震活动较强的地区。 本文充分利用该带最新的区域地震台网资料, 历史地震复核资料以及地震构造等资料, 统计和计算了b值、 V₄和中小地震能量密度值, 探讨了带内b值空间分布与历史强震、 中小地震能量密度值空间分布的关系, 进一步研究了长江下游—南黄海地震带的地震活动性特征。 通过本文的研究, 获得了长江下游—南黄海地震带的地震活动性参数, 为概率危险性分析提供计算参数; 探讨了该带未来百年地震发展趋势, 初步判定了该带潜在地震危险区, 为地震活动中长期预测提供参考依据和方法; 研究结果对地震区划、 工程场地地震安全性评价、 地震活动中长期预测均有重要意义。     

前兆性地震活动的研究

近年来中国在前兆性地震现象的研究方面取得的主要进展可概述如下:1.地震空区和地震条带。在判定空区和条带时,除了震中分布以外,必须同时考虑区、带内外地震频度,能量释放率以及 b 值等的变化特征。2.地震的相关性。近期的研究表明,在中国某些特定地区间,地震的发生在时间上具有同步性,在空间距离上亦有优势分布,优势距离约为400—600公里。此外,还发现了一些地震“窗”或地震发生的“敏感”点。3.地震序列性质的判别。提出了描述地震序列能量分布均匀度的参数 U 值和 K 值,对华北地区地震序列应用的结果表明,高 U 值或高 K 值(归一化熵值),可作为判别前兆性地震序列的参考指标。如果将它们与 h 值联合使用,有可能提高判别地震序列的概率增益。4.介质参数。利用尾波和 P 波,都观测到大地震前后,震源及其周围地区介质 Q 值有不同程度的变化。文中还从统计的观点出发,对前兆性地震活动预测大地震的能力,作了估计,并提出了进一步研究的意见。     

Experimental examination on the heterogeneity parameter C_v of earthquake precursors

Introduction Many earthquake cases indicate that there exists obvious heterogeneity in temporal-spatial distribution of earthquake precursors (FENG, 1983; MEI, et al, 1993). Therefore, it is very crucial for the study of earthquake prediction to describe the heterogeneity in temporal-spatial distribution of earthquake precursors and to understand its physical mechanism. Up to now, a lot of useful researches have been done in this field. Recently, WANG, et al (1999) and CHEN, et al (2000)…     

强震前水氡异常台站时空分布非均匀性变化特征

研究了华北地区６级左右强震前水氡观测值异常的时空非均匀分布变化特征。结果表明,在强震前水氡观测异常台站的空间分布非均匀性发生显变化,Ｃｖ值明显的和蔼同过程。这说明,强震前短临地震前兆异常空间分布的非均匀性增强,变得更加不均匀,这可能邮地壳应力场的增强过程。     

Scale effects of hydrostratigraphy and recharge zonation on base flow

Uncertainty regarding spatial variations of model parameters often results in the simplifying assumption that parameters are spatially uniform. However, spatial variability may be important in resource assessment and model calibration. In this paper, a methodology is presented for estimating a critical basin size, above which base flows appear to be relatively less sensitive to the spatial distribution of recharge and hydraulic conductivity, and below which base flows are relatively more sensitive to this spatial variability. Application of the method is illustrated for a watershed that exhibits distinct infiltration patterns and hydrostratigraphic layering. A ground water flow model (MODFLOW) and a parameter estimation code (UCODE) were used to evaluate the influence of recharge zonation and hydrostratigraphic layering on base flow distribution. Optimization after removing spatial recharge variability from the calibrated model altered base flow simulations up to 53% in watersheds smaller than 40 km(2). Merging six hydrostratigraphic units into one unit with average properties increased base flow residuals up to 83% in basins smaller than 50 km(2). Base flow residuals changed <5% in watersheds larger than 40 and 50 km(2) when recharge and hydrostratigraphy were simplified, respectively; thus, the critical basin size for the example area is approximately 40 to 50 km(2). Once identified for an area, a critical basin size could be used to guide the scale of future investigations. By ensuring that parameter discretization needed to capture base flow distribution is commensurate with the scope of the investigation, uncertainty caused by overextending uniform parameterization or by estimating extra parameter values is reduced.     

Spatial scale effect on the upper soil effective parameters of a distributed hydrological model

Nonlinear dynamics and spatial variability in hydrological systems make the formulation of scaling theories difficult. Therefore, the development of knowledge related to scale effects, scaling techniques, parameterization and linkages of parameters across scales is highly relevant. The main purpose of this work is to analyse the spatial effect of the static storage capacity parameter H_u and the saturated hydraulic conductivity parameter k_s from microscale (sub‐grid level) to mesoscale (grid level) and its implication to the definition of an optimum cell size. These two parameters describe the upper soil water characteristics in the infiltration process conceptualization of the TETIS hydrological model. At microscale, the spatial heterogeneity of H_u and k_s was obtained generating random parameter fields through probability distribution functions and a spatial dependence model with pre‐established correlation lengths. The effective parameters at mesoscale were calculated by solving the inverse problem for each parameter field. Results indicate that the adopted inverse formulation allows transferring the nonlinearity of the system from microscale to the mesoscale via non‐stationary effective parameters. Their values at each cell and time step are in the range of zero to the mean value of the parameter at microscale. The stochastic simulations showed that the variance of the estimated effective parameters decreases when the ratio between mesoscale cell size and correlation length at microscale increases. For a ratio greater than 1, we found cell sizes having the characteristics of a representative elementary area (REA); in such case, the microscale variability pattern did not affect the system response at mesoscale. Copyright © 2011 John Wiley & Sons, Ltd.     

Capturing the essential spatial variability in distributed hydrological modelling: Hydraulic roughness

The spatial variability of each parameter affecting storm runoff must be accounted for in distributed modelling. The objective of the work reported here is to assess the effects of using distributed versus lumped hydraulic roughness coefficients in the modelling of direct surface runoff. A spatially variable data set composed of Manning roughness coefficients is used to model direct surface runoff. To assess the information content (as measured by entropy) of spatially variable data and its significance in distributed modelling, various degrees of smoothing are applied. The error resulting from smoothing the hydraulic roughness coefficients is determined by modelling overland flow using a finite element solution. The Manning roughness coefficients were taken from field measurements of the Manning roughness coefficient at 0.6 m on a 14 m hillslope. These values were then used in a numerical simulation of outflow hydrographs to investigate the dependence of error on spatial variability. Our study focuses on the characteristics of spatial data used in distributed hydrological modelling. The field sites have fractal dimensions of ≈? 1.4, which is close to a Brownian variation. The sampling interval that captures the essential spatial variability of the Manning roughness coefficient does not seem to matter due to its Brownian variation in the field sites. Hence due to the nearly uniform random distribution, measurements at 0.6 m intervals are not necessary and larger intervals would yield results that are just as acceptable provided the mean value together with a uniformly random distribution is maintained for any size of finite element or sampling resolution. Because detailed measurements of hydraulic roughness are not practically available for deterministic catchment modelling, it is important to know that larger sampling resolutions may be used than 0.6 m.     

Unsaturated flows in soils with self-similar hydraulic conductivity distribution

In this article, we are concerned with the statistics of steady unsaturated flow in soils with a fractal hydraulic conductivity distribution. It is assumed that the spatial distribution of log hydraulic conductivity can be described as an isotropic stochastic fractal process. The impact of the fractal dimension of this process, the soil pore-size distribution parameter, and the characteristic length scale on the variances of tension head and the effective conductivity is investigated. Results are obtained for one-dimensional and three-dimensional flows. Our results indicate that the tension head variance is scale-dependent for fractal distribution of hydraulic conductivity. Both tension head variance and effective hydraulic conductivity depend strongly on the fractal dimension. The soil pore-size distribution parameter is important in reducing the variability of the unsaturated hydraulic conductivity and of the fluxes.     

Upper-truncated Power Laws in Natural Systems

—?When a cumulative number-size distribution of data follows a power law, the data set is often considered fractal since both power laws and fractals are scale invariant. Cumulative number-size distributions for data sets of many natural phenomena exhibit a “fall-off?” from a power law as the measured object size increases. We demonstrate that this fall-off is expected when a cumulative data set is truncated at large object size. We provide a generalized equation, herein called the General Fitting Function (GFF), that describes an upper-truncated cumulative number-size distribution based on a power law. Fitting the GFF to a cumulative number-size distribution yields the coefficient and exponent of the underlying power law and a parameter that characterizes the upper truncation. Possible causes of upper truncation include data sampling limitations (spatial or temporal) and changes in the physics controlling the object sizes. We use the GFF method to analyze four natural systems that have been studied by other approaches: forest fire area in the Australian Capital Territory; fault offsets in the Vernejoul coal field; hydrocarbon volumes in the Frio Strand Plain exploration play; and fault lengths on Venus. We demonstrate that a traditional approach of fitting a power law directly to the cumulative number-size distribution estimates too negative an exponent for the power law and overestimates the fractal dimension of the data set. The four systems we consider are well fit by the GFF method, suggesting they have properties characterized by upper-truncated power laws.     

一种通过地形指数提取流域自由水蓄水容量空间分布的方法

对于分布式水文模型而言,如何获得参数的空间分布是模型应用的重点和难点问题.本文将分水源参数中的敏感参数——自由水蓄水容量为研究对象.建立地形指数与自由水蓄水容量的函数关系,以此提取流域内的自由水蓄水容量空间分布.最后利用本方法提取了陕西省陈河流域的自由水蓄水容量空间分布,并将之作为栅格型新安江模型的参数进行洪水模拟演算.应用结果表明本文提出的方法得到了理想的模拟结果.该方法以物理规律为基础能较为准确地计算出流域内自由水蓄水容量的空间分布,为分布式模型的发展奠定了坚实的基础.     

Approximate copula-based estimation and prediction of discrete spatial data

The present paper reports on the use of copula functions to describe the distribution of discrete spatial data, e.g. count data from environmental mapping or areal data analysis. In particular, we consider approaches to parameter point estimation and propose a fast method to perform approximate spatial prediction in copula-based spatial models with discrete marginal distributions. We assess the goodness of the resulting parameter estimates and predictors under different spatial settings and guide the analyst on which approach to apply for the data at hand. Finally, we illustrate the methodology by analyzing the well-known Lansing Woods data set. Software that implements the methods proposed in this paper is freely available in Matlab language on the author’s website.     

Meshing scheme in the computation of spontaneous fault rupture process

The choice of spatial grid size has been being a crucial issue in all kinds of numerical algorithms. By using BIEM (Boundary Integral Equation Method) to calculate the rupture process of a planar fault embedded in an isotropic and homogeneous full space with simple discretization scheme,this paper focuses on what grid size should be applied to control the error as well as maintaining the computing efficiency for different parameter combinations of (Dc,Te),where Dc is the critical slip-weakening dis-tance and Te is the initial stress on the fault plane. We have preliminarily found the way of properly choosing the spatial grid size,which is of great significance in the computation of seismic source rup-ture process with BIEM.