Visualization Of Spatial Data Technology

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 Visualization of Technology in background, application, present situation and Trend of development these four aspects. Secondly, it introduces Spatial Data, which mainly includes the basic knowledge of spatial data and structure model of the spatial data. Thirdly, introduces the principles of four kinds of simple gridding methods, which are linear interpolation, Curve Fitting, Trend surface stacking with residual error and Ordinary Kriging method. Fourthly, it researches the gridding methods of Equivalent linear data, in the reference of simple gridding method; it researches the N-P with orientation method and Motion fitting interpolation. Finally, it uses these gridding methods to Equivalent linear data, and compares and analyzes the gridding results.This dissertation compares with some gridding methods that we use frequently. Any of these methods is not best; we should choose the most appropriate method by the characteristic and spatial analysis of the data. N-P with orientation method is improved on Inverse distance weighted and averaged method. To a large extend, its explore scope reduced, so its Computation load is much more reduced than the simple method. Motion fitting interpolation is suitable for irregular continual surface data. According to the Distributed characteristic of scattered data points which is around gridding point, we can use Quadratic Surface Interpolation with Multiple Regression, it reliable and effective by testify.In summary, every method has its own characteristic and superiority, and they are widely used in geophysical irregular data gridding, at the same time, they also have its limitation and application condition. We should choose the most appropriate method by the distributed characteristic of data and the demand of precision.