一种稳定的位场数据最小曲率网格化方法研究

The research to a stable minimum curvature gridding method in potential data processing

在进行位场数据处理和转换之前均要对离散数据进行网格化,网格化的精度直接影响位场处理和转换结果的精度、网格化方法的计算量大小直接影响位场处理和转换结果的效率.本文在已有最小曲率网格化算法的基础上提出了一种稳定的最小曲率网格化算法,该算法不但提高了网格化结果的精度,而且网格化结果受点(线)距大小的影响较小,通过实际资料验证了该方法的稳定性以及效果.

All discrete data should be processed with the gridding before the potential field data processing and the conversion. The precision of gridding directly influence on the accuracy of the potential field data processing and conversion results, and the computational of gridding directly affects the efficiency of the potential field data processing and conversion results. In this paper, a stable minimum curvature gridding algorithm has been proposed based on the existing ones, which greatly improved the accuracy of the results, and less affected by the gridding size of point and line spacing. The stability and effectiveness of the method has been verified on the actual data.