Scholarlink

<p>Kriging, renowned for its optimal linear unbiased estimation properties, has been extensively applied to address geophysical data interpolation problems. The variogram serves as a critical tool for characterizing the spatial decay of data covariance, fundamentally governing the precision of Kriging interpolation. However, existing variogram models exhibit inherent limitations when describing gravity data, specifically in the inability to adequately capture the authentic spatial correlation of gravity anomalies across varying distances. This deficiency consequently induces systematic biases or excessive smoothing in the inter-polated results. Therefore, it is imperative to construct variogram models by incorporating the characteristics of the gravity field. This paper systematically analyzes the decay characteristics of gravity anomaly covariance across varying spatial distances and proposes a novel variogram model specifically tailored for the interpolation and reconstruction of gravity background fields. The proposed model simultaneously satisfies the short-range smoothness constraint of second-order differentiability at the origin while effectively characterizing the long-range power-law decay behavior. Based on gravity anomaly grids from satellite altimetry, five areas with varying complexity and statistical characteristics were selected in the Pacific Ocean to conduct interpolation and reconstruction experiments. The interpolation accuracy of the proposed model was compared against conventional variogram models when reconstructing 1′×1′ grids from 2′×2′ and 5′×5′ grids respectively. The results demonstrate that the proposed model effectively reduces the root mean square error (RMSE) of interpolation reconstruction by 67.0% – 84.3% compared to conventional models, and in areas with abundant local detail features, the proposed model exhibits superior stability and precision advantages. The proposed model effectively extends the application of Kriging in gravity data processing, and significantly enhances the accuracy and reliability of gravity background field reconstruction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Variogram Model Based on Spatial Decay of Gravity Anomaly Covariance

  • 期刊论文

  • 发表日期：

    2026年7月4日

查看全文
  • Xiao-yu Xie,
  • Kai-xin Luo,
  • Ju-liang Cao,
  • Shao-kun Cai,
  • Rui-hang Yu,
  • Qi-qin Hu,
  • Yan Guo,
  • Bai-nan Yang

摘要

Kriging, renowned for its optimal linear unbiased estimation properties, has been extensively applied to address geophysical data interpolation problems. The variogram serves as a critical tool for characterizing the spatial decay of data covariance, fundamentally governing the precision of Kriging interpolation. However, existing variogram models exhibit inherent limitations when describing gravity data, specifically in the inability to adequately capture the authentic spatial correlation of gravity anomalies across varying distances. This deficiency consequently induces systematic biases or excessive smoothing in the inter-polated results. Therefore, it is imperative to construct variogram models by incorporating the characteristics of the gravity field. This paper systematically analyzes the decay characteristics of gravity anomaly covariance across varying spatial distances and proposes a novel variogram model specifically tailored for the interpolation and reconstruction of gravity background fields. The proposed model simultaneously satisfies the short-range smoothness constraint of second-order differentiability at the origin while effectively characterizing the long-range power-law decay behavior. Based on gravity anomaly grids from satellite altimetry, five areas with varying complexity and statistical characteristics were selected in the Pacific Ocean to conduct interpolation and reconstruction experiments. The interpolation accuracy of the proposed model was compared against conventional variogram models when reconstructing 1′×1′ grids from 2′×2′ and 5′×5′ grids respectively. The results demonstrate that the proposed model effectively reduces the root mean square error (RMSE) of interpolation reconstruction by 67.0% – 84.3% compared to conventional models, and in areas with abundant local detail features, the proposed model exhibits superior stability and precision advantages. The proposed model effectively extends the application of Kriging in gravity data processing, and significantly enhances the accuracy and reliability of gravity background field reconstruction.