average semi-variogram

简明释义

平均半方差图;

英英释义

例句

1.The average semi-variogram 平均半变异函数 helps in optimizing the placement of sampling points in environmental studies.

平均半变异函数有助于优化环境研究中采样点的布置。

2.The calculation of the average semi-variogram 平均半变异函数 is essential for understanding spatial correlation in geostatistics.

计算平均半变异函数在地质统计学中理解空间相关性至关重要。

3.By analyzing the average semi-variogram 平均半变异函数, we can determine the range of spatial dependence among the data points.

通过分析平均半变异函数，我们可以确定数据点之间空间依赖的范围。

4.We used the average semi-variogram 平均半变异函数 to assess the variability of mineral concentrations across the site.

我们使用平均半变异函数来评估场地内矿物浓度的变异性。

5.In our study, the average semi-variogram 平均半变异函数 revealed a strong spatial structure in the soil moisture levels.

在我们的研究中，平均半变异函数揭示了土壤湿度水平的强空间结构。

作文

In the field of geostatistics, understanding spatial dependence among data points is crucial for effective analysis and interpretation. One of the key tools used to measure this spatial correlation is the concept of the average semi-variogram. The average semi-variogram is a statistical function that describes how data values vary with distance. It provides insights into the degree of similarity between locations based on their respective values. By analyzing the average semi-variogram, researchers can determine the range, sill, and nugget effects, which are essential parameters in spatial modeling. The average semi-variogram is calculated by taking pairs of data points at various distances and computing the semi-variance for each pair. The semi-variance measures the average squared difference between the values of the paired observations. As the distance between these points increases, the semi-variance typically increases as well, indicating that points further apart are less likely to be similar. This relationship is often visualized in a graph where the x-axis represents the distance and the y-axis represents the semi-variance. One of the primary uses of the average semi-variogram is in kriging, a geostatistical interpolation technique. Kriging utilizes the information captured by the average semi-variogram to predict unknown values at unsampled locations based on the spatial structure of the sampled data. By incorporating the average semi-variogram into the kriging equations, analysts can create more accurate and reliable predictions, which is particularly valuable in fields such as environmental science, mining, and agriculture.Moreover, the average semi-variogram assists in identifying the scale of spatial correlation. For instance, if the range of the semi-variogram is short, it indicates that the spatial correlation diminishes quickly with distance, suggesting that the phenomenon being studied has a localized effect. Conversely, a long-range semi-variogram implies that the influence of a data point extends over larger areas, highlighting a more widespread spatial dependence.Understanding the average semi-variogram also helps in assessing the quality of spatial data. If the semi-variogram exhibits unexpected patterns or trends, it may indicate issues with data collection or the presence of outliers. Consequently, researchers can make informed decisions about data cleaning and preprocessing before conducting further analyses.In conclusion, the average semi-variogram is an indispensable tool in geostatistics that provides valuable insights into spatial relationships among data points. Its role in determining spatial correlation, aiding in interpolation techniques like kriging, and evaluating data quality makes it a fundamental concept for researchers and practitioners in various fields. By mastering the average semi-variogram, one can significantly enhance their ability to analyze and interpret spatial data effectively.

在地统计学领域，理解数据点之间的空间依赖性对于有效的分析和解释至关重要。用于测量这种空间相关性的关键工具之一是平均半变异函数的概念。平均半变异函数是一个统计函数，它描述了数据值随距离变化的情况。它提供了基于各自值的地点之间相似度的见解。通过分析平均半变异函数，研究人员可以确定范围、平稳性和噪声效应，这些都是空间建模中的重要参数。平均半变异函数是通过取不同距离的数据点对并计算每对的半方差来计算的。半方差度量了配对观测值之间的平均平方差。随着这些点之间的距离增加，半方差通常也会增加，表明距离较远的点不太可能相似。这个关系通常在图表中可视化，其中x轴表示距离，y轴表示半方差。平均半变异函数的主要用途之一是在克里金法中，这是一种地统计插值技术。克里金法利用平均半变异函数所捕获的信息，根据采样数据的空间结构预测未采样位置的未知值。通过将平均半变异函数纳入克里金方程，分析师可以创建更准确和可靠的预测，这在环境科学、采矿和农业等领域尤为重要。此外，平均半变异函数有助于识别空间相关性的尺度。例如，如果半变异函数的范围较短，则表示空间相关性随着距离迅速减弱，表明所研究现象具有局部效应。相反，较长范围的半变异函数则意味着数据点的影响扩展到更大区域，突出显示了更广泛的空间依赖性。理解平均半变异函数还有助于评估空间数据的质量。如果半变异函数表现出意外的模式或趋势，可能表明数据收集存在问题或存在异常值。因此，研究人员可以在进行进一步分析之前，对数据清理和预处理做出明智的决定。总之，平均半变异函数是地统计学中不可或缺的工具，为数据点之间的空间关系提供了宝贵的见解。它在确定空间相关性、辅助插值技术如克里金法以及评估数据质量方面的作用，使其成为各个领域研究人员和从业者的基础概念。通过掌握平均半变异函数，人们可以显著增强有效分析和解释空间数据的能力。