最小二乘配置在磁力异常数据网格化中的应用

doi:10.11720/wtyht.2025.1286

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Abstract

Traditional gridding methods struggle to balance computational accuracy and efficiency when processing irregularly distributed magnetic anomaly data. To address this issue, this study applied the classic least-squares collocation method from geodesy to the gridding of ground-based magnetic anomaly data. This application was verified through the test and analysis of the simulation data and the actual coalfield data. The results indicate that the computational accuracy of gridding based on least-squares collocation is dictated by the error estimation of discrete observational data and the selection and fitting of the covariance function. More accurate error estimation contributes to higher-accuracy interpolation. A polynomial function is a simple and effective empirical covariance function for processing magnetic anomaly data. The least-squares collocation method demonstrates more effective noise suppression compared to the Kriging, minimum curvature, and radial basis function methods. Overall, applying the least-squares collocation to the gridding of magnetic anomaly data can enhance the accuracy and efficiency of data processing.

Key words： least-squares collocation magnetic anomaly gridding interpolation covariance function

Received: 10 July 2024 Published: 22 April 2025

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	Xiao-Wei GAO
	Xiong-Wei LI
	Shao-Dong PANG
	Wen-Gang LI
	Wei-Hua YAO
	Jin-Song DU

Cite this article:

Xiao-Wei GAO,Xiong-Wei LI,Shao-Dong PANG, et al. Application of least-squares collocation to the gridding of magnetic anomaly data[J]. Geophysical and Geochemical Exploration, 2025, 49(2): 422-432.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.1286 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I2/422

Simulated theoretical data (a) and observation data (b)

Calculated covariance values and fitted covariance function

Predicted data (a) and interpolation errors (b)

Simulated theoretical data (a), noises (b) and practical observation data (c)
(black dots represent synthetic observation locations)

Statistic parameters of calculation accuracy in cases of setting different levels of data noisesnT

Calculating results by using different interpolation methods

Differences between calculating results and theoretical data by using different interpolation methods

Statistic parameters of calculating accuracy of four interpolation methodsnT

Columnar statistical charts of residual data by using different interpolation methods

Standard deviation values of calculating residuals in cases of different noise levels by using different interpolation methods

Elevation (a), original (b) and interference removed (c) ΔT magnetic anomaly data

Calculated covariance values and fitted covariance function curve by practical observation data

Calculated grid results by using different interpolation methods

Differences between calculated results and practical data by using different interpolation methods

Statistic parameters of calculating error of four gridding methodsnT

Columnar statistical charts of calculating errors by different gridding methods

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