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1.
Groundwater being an important component of the hydrological cycle as it sustains the streamflow during precipitation free periods and is a major source of water supply. The dependence on the groundwater has increased drastically over the years leading to over exploitation of the aquifers. Therefore, it is imperative to assess the extent of exploitation and analyse the groundwater level scenarios in the area of interest. The existence of a trend in a hydrological time series can be detected by statistical tests. The present study investigates the application of various methods for identification of trends in groundwater levels in few blocks of Sagar district, which faces severe water scarcity owing to the declining groundwater levels. The non-parametric Kendal rank correlation test as well as the parametric linear regression test has been used for trend detection based on the analysis of the seasonal groundwater levels. Kendal’s rank correlation test, has been applied to identify the trend persisting in the data and the linear regression test is used to identify the significance of the slope. The analysis indicates that the time series of groundwater levels are cyclical with characteristics of seasonal variation in all the blocks coupled with a declining trend at Sagar, Khurai and Bina.  相似文献   

2.
In studies that involve a finite sample size of spatial data it is often of interest to test (statistically) the assumption that the marginal (or univariate) distribution of the data is Gaussian (normal). This may be important per se because, for example, a data transformation may be desired if the normality hypothesis is rejected, or it may provide a way of testing other hypotheses, such as lognormality, by testing the normality of the logarithms of the observations. The most commonly used tests, such as the Kolmogorov–Smirnov (K–S), chi-square (2), and Shapiro–Wilks (S–W) tests, are designed on the assumption that the observations are independent and identically distributed (iid). In geostatistical applications, however, this is not usually the case unless the spatial covariance (semivariogram) function is a pure nugget variance. If the covariance structure has a (practical) range greater than the minimum distance between observations, the data are correlated and the standard tests cannot be applied to the probability density function (pdf) or cumulative probability function (cdf) estimated directly from the data. The problem with correlated data arises not from the correlation per se but from cases in which correlated data are clustered rather than being located on a regular grid. In these cases inferences requiring iid assumptions may be seriously biased because of the spatial correlation among the observations. If unbiased (i.e., de-clustered) estimates of the pdf or cdf are obtained, then normality tests, such as K-S, 2, or S–W, can be applied using the unbiased estimates and an effective number of samples equivalent to the iid case. There are three questions to be addressed in these cases: Is the distribution ergodic?  相似文献   

3.
In the linear model of coregionalization (LMC), when applicable to the experimental direct variograms and the experimental cross variogram computed for two random functions, the variability of and relationships between the random functions are modeled with the same basis functions. In particular, structural correlations can be defined from entries of sill matrices (coregionalization matrices) under second-order stationarity. In this article, modified t-tests are proposed for assessing the statistical significance of estimated structural correlations. Their specific aspects and fundamental differences, compared with an existing modified t-test for global correlation analysis with spatial data, are discussed via estimated effective sample sizes, in relation to the superimposition of random structural components, the range of autocorrelation, the presence of correlation at another structure, and the sampling scheme. Accordingly, simulation results are presented for one structure versus two structures (one without and the other with autocorrelation). The performance of tests is shown to be related to the uncertainty associated with the estimation of variogram model parameters (range, sill matrix entries), because these are involved in the test statistic and the degrees of freedom of the associated t-distribution through the estimated effective sample size. Under the second-order stationarity and LMC assumptions, the proposed tests are generally valid.  相似文献   

4.
精细化衡量地下水对外界因素的响应规律对分析滑坡稳定性具有重要意义。考虑库岸边坡的稳定性同时受库水位和降雨作用影响,提出了一种基于数据挖掘的滑坡地下水响应特征的研究方法。以三峡库区麻地湾滑坡为例,首先通过特征时段分析和Granger检验确定地下水响应滞后期,然后基于响应滞后期确定地下水水位的影响因素,结合Apriori数据挖掘算法揭示了麻地湾滑坡地下水的响应特征。研究结果表明:滑坡前缘地下水变化与库水位波动相关性较大,滑坡中后缘地下水变化与降雨相关性较大; 麻地湾滑坡地下水对于降雨和库水位的最佳响应滞后期为1 d;滑坡后缘的地下水水位对降雨响应较为强烈,而前缘的地下水水位对库水位响应更为强烈。  相似文献   

5.

Spatial data analytics provides new opportunities for automated detection of anomalous data for data quality control and subsurface segmentation to reduce uncertainty in spatial models. Solely data-driven anomaly detection methods do not fully integrate spatial concepts such as spatial continuity and data sparsity. Also, data-driven anomaly detection methods are challenged in integrating critical geoscience and engineering expertise knowledge. The proposed spatial anomaly detection method is based on the semivariogram spatial continuity model derived from sparsely sampled well data and geological interpretations. The method calculates the lag joint cumulative probability for each matched pair of spatial data, given their lag vector and the semivariogram under the assumption of bivariate Gaussian distribution. For each combination of paired spatial data, the associated head and tail Gaussian standardized values of a pair of spatial data are mapped to the joint probability density function informed from the lag vector and semivariogram. The paired data are classified as anomalous if the associated head and tail Gaussian standardized values fall within a low probability zone. The anomaly decision threshold can be decided based on a loss function quantifying the cost of overestimation or underestimation. The proposed spatial correlation anomaly detection method is able to integrate domain expertise knowledge through trend and correlogram models with sparse spatial data to identify anomalous samples, region, segmentation boundaries, or facies transition zones. This is a useful automation tool for identifying samples in big spatial data on which to focus professional attention.

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6.
降水集中程度是反映降水结构的重要指标。基于1960—2017年中国773个气象站日降水资料,运用降水集中程度（Q）,研究中国降水集中程度的时空特征,分析其与降水量、海洋状态之间的关系。结果表明:①中国年平均Q值为0.38,南北较低中间较高;冬季和秋季的降水集中程度相对较高,平均Q值分别为0.53和0.51,夏季和春季相对较低,分别为0.39和0.48。②年降水集中程度变化趋势较小,总体上略有上升,东南升高西北降低;在年和季尺度,Q和降水量均表现出较强的负相关性,年尺度相关系数为-0.71,秋季的相关性最强,相关系数为-0.89,春季的相关性最弱,相关系数为-0.70,降水集中程度和降水量共同影响水旱灾害受灾面积。③Q与NINO3.4指数间的相关性随着滞后时间的延长先增大后减小,当滞后时间为2个月时相关系数最大,平均为0.13,由北向南总体呈"-+-"分布;与PDO指数间的相关系数随着滞后时间的延长先减小后增大再减小,当滞后时间为4个月时相关系数最大,平均为0.12,以负相关为主。  相似文献   

7.
Transmissivity of an aquifer is determined from pumping test analysis, but due to the difficulty of performing such tests as well as the relatively high cost of these test, it is often estimated from specific capacity data. In this study an empirical relation is derived using 237 pairs of transmissivity and specific capacity values that are obtained from groundwater wells penetrating a fractured and karstified carbonate aquifer. Linear and logarithmic regression functions have been performed and it is found that the logarithmic relationship predicting transmissivity from specific capacity data has a better correlation (r=0.95) than linear one (r=0.84). This is logically true because both transmissivity and specific capacity are lognormally distributed. The spatial distribution of transmissivity is also affected by the presence of fracturing and karstification phenomena within the aquifer matrix.  相似文献   

8.
Describing how soil properties vary spatially is of particular importance in stochastic analyses of geotechnical problems, because spatial variability has a significant influence on local material and global geotechnical response. In particular, the scale of fluctuation θ is a key parameter in the correlation model used to represent the spatial variability of a site through a random field. It is, therefore, of fundamental importance to accurately estimate θ in order to best model the actual soil heterogeneity. In this paper, two methodologies are investigated to assess their abilities to estimate the vertical and horizontal scales of fluctuation of a particular site using in situ cone penetration test (CPT) data. The first method belongs to the family of more traditional approaches, which are based on best fitting a theoretical correlation model to available CPT data. The second method involves a new strategy which combines information from conditional random fields with the traditional approach. Both methods are applied to a case study involving the estimation of θ at three two-dimensional sections across a site and the results obtained show general agreement between the two methods, suggesting a similar level of accuracy between the new and traditional approaches. However, in order to further assess the relative accuracy of estimates provided by each method, a second numerical analysis is proposed. The results confirm the general consistency observed in the case study calculations, particularly in the vertical direction where a large amount of data are available. Interestingly, for the horizontal direction, where data are typically scarce, some additional improvement in terms of relative error is obtained with the new approach.  相似文献   

9.
Natural processes generate spatial fields which reflect their specific properties. In this paper the effect of the direction of processes on the resulting spatial fields is investigated. This is done by extending the concept of reversibility used for time series to space. A novel copula based measure of asymmetry is defined which is an indicator of directional dependence. Contrary to traditional geostatistics where all points separated by a vector are considered irrespective of its sign, in this study the direction of the vector is also taken into consideration, leading to differences in the dependence corresponding to the vector \({\mathbf {h}}\) and \({-}{\mathbf {h}}\). The concept of directional dependence and the corresponding measure of asymmetry are defined using spatial copulas, and are thus independent of the scale of measurement. The result is a bivariate directional third-order moment based measure which can identify the direction in which the processes generating the spatial field acted. A statistical test to find the statistical significance of the asymmetry indicating directional dependence is presented. The methodology is tested on a number of synthetic and observed cases. Precipitation and groundwater quality parameters obtained using numerical models are first investigated. Regular dense grids obtained by numerical simulations show good correspondence between properties of the modeled processes and the new measure introduced. Measured variables observed on sparse irregular networks show similar behavior to the theoretical examples. Mean flow directions in groundwater and advection directions of precipitation fields can be detected from single snapshots. As a further example, dominant wind directions in the Sahara are found by investigating the digital terrain model.  相似文献   

10.
In many fields of the Earth Sciences, one is interested in the distribution of particle or void sizes within samples. Like many other geological attributes, size distributions exhibit spatial variability, and it is convenient to view them within a geostatistical framework, as regionalized functions or curves. Since they rarely conform to simple parametric models, size distributions are best characterized using their raw spectrum as determined experimentally in the form of a series of abundance measures corresponding to a series of discrete size classes. However, the number of classes may be large and the class abundances may be highly cross-correlated. In order to model the spatial variations of discretized size distributions using current geostatistical simulation methods, it is necessary to reduce the number of variables considered and to render them uncorrelated among one another. This is achieved using a principal components-based approach known as Min/Max Autocorrelation Factors (MAF). For a two-structure linear model of coregionalization, the approach has the attractive feature of producing orthogonal factors ranked in order of increasing spatial correlation. Factors consisting largely of noise and exhibiting pure nugget–effect correlation structures are isolated in the lower rankings, and these need not be simulated. The factors to be simulated are those capturing most of the spatial correlation in the data, and they are isolated in the highest rankings. Following a review of MAF theory, the approach is applied to the modeling of pore-size distributions in partially welded tuff. Results of the case study confirm the usefulness of the MAF approach for the simulation of large numbers of coregionalized variables.  相似文献   

11.
Stochastic simulation techniques which do not depend on a back transform step to reproduce a prior marginal cumulative distribution function (cdf)may lead to deviations from that distribution which are deemed unacceptable. This paper presents an algorithm to post process simulated realizations or any spatial distribution to reproduce the target cdfin the case of continuous variables or target proportions in the case of categorical variables, yet honoring the conditioning data. Validations conducted for both continuous and categorical cases show that. by adjusting the value of a correction level parameter , the target cdfor proportions can be well reproduced without significant modification of the spatial correlation patterns of the original simulated realizations.  相似文献   

12.
三峡库区典型堆积层滑坡变形滞后时间效应研究   总被引：1，自引：0，他引：1  
堆积层滑坡是三峡水库运行过程中的重要地质灾害,其变形演化往往滞后于库水位的变化,表现出时间滞后效应,给滑坡灾害精准预测和灾害警情准确发布造成极大困扰。采用集对分析法并结合层次分析法,构建了滑坡加权位移向量计算模型,在滑坡加权位移演化与库水位波动相互关系定性分析的基础上,寻找滑坡加权位移与库水位变化速率相关性达到最大时的平移步数,从而计算出滑坡变形滞后于库水位变化的时间。以三峡库区典型堆积层滑坡——树坪滑坡为例,在分析滑坡变形演化规律基础上,分别选取2012年、2013年、2014年汛雨期地表位移与库水位下降速率的监测数据开展滑坡变形滞后时间研究。研究发现:当库水位下降速率小于等于0.43 m·d-1时,树坪滑坡变形滞后时间大于等于5 d;当库水位下降速率在0.43 m·d-1到0.7 m·d-1之间时,树坪滑坡变形滞后时间在2 d到5 d之间;当库水位下降速率大于等于0.7 m·d-1时,树坪滑坡变形滞后时间小于等于2 d;随着库水位下降速率不断增大,树坪滑坡变形滞后时间不断缩短。通过分析滑坡不同空间位置监测点的滞后时间,发现越靠近滑坡体前缘变形滞后时间越短,当库水位下降速率在0.43 m·d-1到0.7 m·d-1之间时,滑坡前缘变形滞后时间在2.4 d到5.4 d之间,滑坡中部的变形滞后时间在3.4 d到5.6 d之间,滑坡前缘和中部的变形滞后时间差在0.2 d到1.4 d之间。研究成果可以为树坪滑坡的监测预警防治工作提供参考,对重大水利工程涉水滑坡监测预警具有一定借鉴意义。  相似文献   

13.
CFG桩(cement-fly ash-gravel pile)复合地基是一种重要的地基处理形式,在日益增加的大面积住宅和商业开发中作用越来越突出,然而该种桩型的加卸荷-沉降变形特性仍然需深入研究,尤其在概率评估方面。根据北京星光影视股份有限公司生产科研基地项目工地中的21根CFG桩单桩静载试验和32个复合地基静载试验的原位加卸载测试成果,采用两参数的双曲线或幂曲线回归拟合了每一条加荷-变形曲线。由于土体的内在各向异性和其强度的变异性,评估整个场地的加荷-变形曲线时,其回归参数表现出了较大的离散性。将一个场地的多组回归参数组成一个随机向量,其加载-位移曲线的不确定性可由简单的两变量随机向量体现,引入双变量联结函数（Copula）描述随机回归参数间的相依性。最后,考虑正常使用极限状态,采用基于Copula函数的模拟模型计算了CFG桩复合地基的可靠度指标。研究结果有助于改进CFG桩复合地基的概率设计与评估。  相似文献   

14.
土工格栅与土界面作用特性试验研究   总被引：9，自引：4，他引：5  
刘文白  周健 《岩土力学》2009,30(4):965-970
土工格栅与土的界面摩擦特性指标是加筋土工程设计的关键。通过分析土工格栅与土的界面摩擦作用和进行了直剪摩擦试验和拉拔摩擦试验,测试了两种试验条件的界面摩擦特性。在两种试验条件下,土工格栅加筋土复合体的抗剪强度均有界面摩擦角φsq和界面凝聚力csq,且土工格栅与土相对位移量的不同,其复合体的强度机理有区别。在拉拔摩擦试验中,剪应力峰值强度对应的剪切变形值高于直剪摩擦试验中剪应力峰值强度的剪切变形值5～10倍以上。两种试验均有其适用性,而土与土工格栅的相对位移较小时直剪摩擦试验较能反映实际;土与土工格栅相对位移较大时土与格栅双面均发生相对位移,拉拔摩擦试验更为合适。随法向应力的增大,直剪摩擦和拉拔摩擦试验的剪应力峰值以及剪应力峰值对应的位移均提高。直剪摩擦的剪切速度小,剪应力峰值强度高,且达到峰值强度的剪切位移大;增加剪切速度,剪应力峰值强度降低,且对应的位移也减少,其原因是界面上的孔隙水压力消散和筋材的应力松弛。应根据具体工程的需要选择直剪摩擦试验和拉拔摩擦试验确定设计参数。  相似文献   

15.
抽水试验中,动水位数据采集记录及处理分析对水文地质参数计算具有重要意义。近年来自动水位计被广泛用于抽水试验,通过传感器压强水头变化值获取水位降深。因井管内水流动会产生水头损失,自动水位计安放位置不同会导致获取的井水位降深不同,不同于传统方法测得的井水面降深,对水文地质参数计算将产生一定影响,因此如何合理放置自动水位计以及在参数计算中如何应用其获取的水位降深都亟待开展试验研究。在黑河流域第四系大厚度含水层地区,选择典型单层试验孔和利用分层封隔技术实现的一孔同径多层抽水孔开展试验研究,在动水位以下抽水试验层段上部、中部、下部以及潜水泵上部和下部分别放置自动水位计进行了系统的数据采集分析。结果表明:抽水试验中因井管内水流沿程水头损失及速度水头差异导致不同位置自动水位计获得压强水头变化值不同,本次试验实测到井筒内不同部位井损值;井损值在潜水泵进水口处最大,随距潜水泵距离的增大而减小,为避开井筒内较大水头损失对参数计算的影响,自动水位计宜优选安放在潜水泵上部接近动水位位置;在单孔抽水试验中利用稳定流公式计算水文地质参数时,自动水位计获取水位降深含井损不可忽略,需通过多落程抽水试验数据分析扣除后使用。同时,抽水试验中自动水位计不同位置获取数据的处理分析方法为更好地理解井中水头损失提供了依据。  相似文献   

16.
The simulated annealing algorithm has been applied successfully to conditional simulation of categorical variables (e.g., rock or facies units) with the objective of improving the match between measured and modeled spatial variability. In some implementation schemes, however, spurious features termed “artifact discontinuities” may occur near conditioning data, especially during the “zero- temperature” case referred to as simulated quenching. This paper shows that artifact discontinuities can be avoided by considering the anisotropy of the spatial variability model, reducing the number of lag vectors used in the objective function, and providing a rudimentary initial configuration. Results from several test cases suggest that the artifact discontinuities might be caused by overly precise fitting of measured to modeled spatial variability.  相似文献   

17.
This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha−1 ya−1 in 2005, and the area of 47.5 km2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.  相似文献   

18.
This study discusses the scaling properties of the spatial distribution of the December 26, 2004, Sumatra aftershocks. We estimate the spatial correlation dimension D 2 of the epicentral distribution of aftershocks recorded by a local network operated by Geological Survey of India. We estimate the value of D 2 for five blocks in the source area by using generalized correlation integral approach. We assess its bias due to finite data points, scaling range, effects of location errors, and boundary effects theoretically and apply it to real data sets. The correlation dimension was computed both for real as well as synthetic data sets that include randomly generated point sets obtained using uniform distributions and mimicking the number of events and outlines of the effective areas filled with epicenters. On comparing the results from the real data and random point sets from simulations, we found the lower limit of bias in D 2 estimates from limited data sets to be 0.26. Thus, the spatial variation in correlation dimensions among different blocks using local data sets cannot be directly compared unless the influence of bias in the real aftershock data set is taken into account. They cannot also be used to infer the geometry of the faults. We also discuss the results in order to add constraints on the use of synthetic data and of different approaches for uncertainty analysis on spatial variation of D 2. A difference in D 2 values, rather than their absolute values, among small blocks is of interest to local data sets, which are correlated with their seismic b values. Taking into account the possible errors and biases, the average D 2 values vary from 1.05 to 1.57 in the Andaman–Nicobar region. The relative change in D 2 values can be interpreted in terms of clustering and diffuse seismic activity associated with the low and high D 2 values, respectively. Overall, a relatively high D 2 and low b value is consistent with high-magnitude, diffuse activity in space in the source region of the 2004 Sumatra earthquake.  相似文献   

19.
In karst areas, accurately measuring and managing the spatial variability of soil water content (SWC) is very critical in settling numerous issues such as karst rocky desertification, ecosystem reconstruction, etc. In these areas, SWC exhibits strong spatial dependence, and it is a time and labor consuming procedure to measure its spatial variability. Therefore, estimation of this kind of soil property at an acceptable level of accuracy is of great significance. This study was conducted to evaluate and compare the spatial estimation of SWC by using ordinary kriging (OK) and cokriging (COK) methods with prime terrain variables, tending to predict SWC using limited available sample data for a 2,363.7 km2 study area in Mashan County, Guangxi Zhuang Autonomous Region, Southwest China. The measured SWC ranged from 3.36 to 26.69 %, with a mean of 17.34 %. The correlation analysis between SWC and prime terrain variables indicated that SWC showed significantly positive correlation with elevation (r is 0.46, P < 0.01), and significantly negative correlation with slope (r is ?0.30, P < 0.01); however, SWC was not significantly correlated with aspect in the study area. Therefore, elevation and slope were used as auxiliary data together for SWC prediction using COK method, and mean error (ME) and root mean square error were adopted to validate the prediction of SWC by these methods. Results indicated that COK with prime terrain variables data was superior to OK with relative improvement of 28.52 % in the case of limited available data, and also revealed that such elevation and slope data have the potential to improve the precision and reliability of SWC prediction as useful auxiliary variables.  相似文献   

20.
Padhy  Simanchal  Mishra  O. P.  Subhadra  N.  Dimri  V. P.  Singh  O. P.  Chakrabortty  G. K. 《Natural Hazards》2013,77(1):75-96

This study discusses the scaling properties of the spatial distribution of the December 26, 2004, Sumatra aftershocks. We estimate the spatial correlation dimension D 2 of the epicentral distribution of aftershocks recorded by a local network operated by Geological Survey of India. We estimate the value of D 2 for five blocks in the source area by using generalized correlation integral approach. We assess its bias due to finite data points, scaling range, effects of location errors, and boundary effects theoretically and apply it to real data sets. The correlation dimension was computed both for real as well as synthetic data sets that include randomly generated point sets obtained using uniform distributions and mimicking the number of events and outlines of the effective areas filled with epicenters. On comparing the results from the real data and random point sets from simulations, we found the lower limit of bias in D 2 estimates from limited data sets to be 0.26. Thus, the spatial variation in correlation dimensions among different blocks using local data sets cannot be directly compared unless the influence of bias in the real aftershock data set is taken into account. They cannot also be used to infer the geometry of the faults. We also discuss the results in order to add constraints on the use of synthetic data and of different approaches for uncertainty analysis on spatial variation of D 2. A difference in D 2 values, rather than their absolute values, among small blocks is of interest to local data sets, which are correlated with their seismic b values. Taking into account the possible errors and biases, the average D 2 values vary from 1.05 to 1.57 in the Andaman–Nicobar region. The relative change in D 2 values can be interpreted in terms of clustering and diffuse seismic activity associated with the low and high D 2 values, respectively. Overall, a relatively high D 2 and low b value is consistent with high-magnitude, diffuse activity in space in the source region of the 2004 Sumatra earthquake.

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