Geophysical Irregular Distribution Of The Data Grid

Geophysical observed data usually are irregular because of the irregular spread of the observed lines and observed points. The irregular data need to be gridded before conventional processing, because the imaging, processing and interpretation of geophysical data are always based on regular data. Therefore, the gridding of irregular data is the first and basic step of the analysis, processing and interpretation of geophysical data.This dissertation firstly introduces the principles of four kinds of simple gridding methods, which are"Linear interpolation based on triangulation","Inverse distance weighted and averaged method","Modified Shepard's method"and"Multiquardirc method". Secondly, it researches the Ordinary Kriging method for geophysical irregular data, which mainly includes the base knowledge of region change and variogram and the principle of Ordinary Kriging method. Thirdly, it researches the Inverse Interpolation for geophysical irregular data, which mainly includes the principle of inverse interpolation and its iterative algorithm, and the principle of Inverse Interpolation to realize the gridding of geophysical data. Finally, it uses these gridding methods to grid theoretical model data of potential field and raw aeromagnetic data, and compares and analyzes the gridding results.The research of both theories and practices of gridding in the dissertation shows that: Linear interpolation based on triangulation is simple computationally and fast, but its gridding results isn't smooth and its precision is low; Multiquardric method is also simple computationally with high precision and good effect, but its gridding is slow because it is based on global calculation; Minimum curvature method is fast with high precision and good effect, but its gridding quickly degrades over areas with sparse data control when grid spacing smaller than the actual grid spacing is used; Ordinary Kriging method is high-precision, but its gridding is slow because of large calculation; Inverse interpolation is a computationally fast, efficient and robust method with high accuracy and perfect effect, and its gridding results of different gridding orientations are basically identical. Inverse interpolation is suitable for thegridding of geophysical irregular data, especially for large-scale geophysical irregular data.