杨阳, 李飒, 何福耀, 施展玲. 2019: 半变异函数及取样间距对克里金法在海洋地层分析中的影响研究. 工程地质学报, 27(4): 794-802. DOI: 10.13544/j.cnki.jeg.yt2019198
    引用本文: 杨阳, 李飒, 何福耀, 施展玲. 2019: 半变异函数及取样间距对克里金法在海洋地层分析中的影响研究. 工程地质学报, 27(4): 794-802. DOI: 10.13544/j.cnki.jeg.yt2019198
    YANG Yang, LI Sa, HE Fuyao, SHI Zhanling. 2019: A STUDY ON EFFECTS OF VARIOGRAM AND SAMPLING INTERVAL IN KRIGING ON ANALYSIS OF SUBMARINE STRATUM. JOURNAL OF ENGINEERING GEOLOGY, 27(4): 794-802. DOI: 10.13544/j.cnki.jeg.yt2019198
    Citation: YANG Yang, LI Sa, HE Fuyao, SHI Zhanling. 2019: A STUDY ON EFFECTS OF VARIOGRAM AND SAMPLING INTERVAL IN KRIGING ON ANALYSIS OF SUBMARINE STRATUM. JOURNAL OF ENGINEERING GEOLOGY, 27(4): 794-802. DOI: 10.13544/j.cnki.jeg.yt2019198

    半变异函数及取样间距对克里金法在海洋地层分析中的影响研究

    A STUDY ON EFFECTS OF VARIOGRAM AND SAMPLING INTERVAL IN KRIGING ON ANALYSIS OF SUBMARINE STRATUM

    • 摘要: 以浅剖数据为源数据,钻孔实测数据为验证数据,利用普通克里金法对海底地层厚度进行空间插值得到地层分布特征,采用3种半变异函数模型和不同取样间距对某井场3组地层厚度进行普通克里金插值并验证其插值效果。结果表明:普通克里金是一种有效的海底地层厚度预测方法;结构分析最佳的模型不一定是误差最小的模型,应对不同模型下的插值结果进行综合分析来选择最合适的模型,并提出球状模型在该井场厚度估计中最优,高斯模型次之;对于球状模型,增大取样间距对地层厚度变化剧烈的地层回归效果影响较小,对地层厚度变化不大的地层回归效果影响较大;同时,SE预测值变化率分析表明对于地层厚度变化剧烈的地层,减小取样间距可以大幅度地减少插值误差,而对于地层厚度变化不大的地层,减小取样间距对插值精度提高的意义不大。

       

      Abstract: Ordinary kriging interpolation methods can be used in predicting submarine stratum thickness and obtaining its spatial distribution characteristics, using profile data as source data and borehole data as validation data. In this paper, three variogram models and different sampling interval are chosen to estimating submarine stratums thickness. The results show that ordinary kriging is an effective method of predicting submarine stratum thickness. The variogram model with the best structural analysis is not the model with minimal error. The choice of variogram model should be figured out through comprehensive analysis of interpolation output. The most suitable model for estimation of submarine stratums thickness in this well field is spherical model. Gauss model comes the second. For spherical model, the influence to regression effect in undulating stratum is greater than gentle stratum. An analysis of the variation ratio of the SE prediction show that reducing the sampling interval can greatly reduce the interpolation error for undulating stratum, and has little effect on improving the interpolation accuracy for gentle stratum.

       

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