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首页> 外文期刊>Hydrology and Earth System Sciences >Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information
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Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information

机译:通过使用水平距离估计未计算的沉积物覆盖厚度作为二级信息的基岩露头

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Unconsolidated sediment cover thickness (D) above bedrock was estimated by using a publicly available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias. However, if information about the horizontal distance to the nearest bedrock outcrop (L) is included, does the spatial estimation of D improve? This idea was tested by comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis showed only minor differences between OK and CK with respect to differences between estimation and true values. However, the CK results gave in general less estimation variance compared to the OK results. All observations were declustered and transformed to standard normal probability density functions before estimation and back-transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50% of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50% of the total variance, which gave an accuracy of less than 60% for most of the cross-validation cases. Despite the noisy character of the observations, the analysis demonstrated that L can be used as secondary information to reduce the estimation variance of D.
机译:利用挪威格拉纳达的公开钻井数据库,估算了基岩上方的松散沉积物覆盖层厚度(D)。与此类数据库相关的一般挑战通常涉及聚类和偏见。然而,如果包括到最近基岩露头(L)的水平距离信息,D的空间估计是否有所改善?通过比较两个交叉验证结果来验证这个想法:忽略L的普通克里格法(OK);协克立格法(CK),其中包括D和L之间的互协方差。分析表明,在估计值和真实值之间的差异方面,OK和CK之间只有微小的差异。然而,与OK结果相比,CK结果给出的估计方差通常较小。在估计之前,所有观察结果都被去聚类并转换为标准正态概率密度函数,然后再进行反向转换以进行交叉验证分析。半变异函数分析得出D和L的相关长度分别约为10和6 km。这些相关性降低了交叉验证分析中的估计方差,因为超过50%的数据材料在5 km半径范围内有两个或两个以上的观测值。然而,D的小范围方差约为总方差的50%,这使得大多数交叉验证案例的准确度不到60%。尽管观测值具有噪声特性,但分析表明,L可以作为二次信息来减少D的估计方差。

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