Abstrait

Gross error detection method based on wavelet theory of mining spatial data

Chen Ling-Xia, Zhang Jun-Li

Some gross errors in mining spatial data may occur during the process of data collection owning to the natural or human factors. The existence of gross error will affect the result of measuring, so it is of great necessity to explore an effective detection method to find out and eliminate the errors. The paper aims to observe the detection of the gross error in mining spatial data by using the multi-resolution capability of wavelet analysis, and as well to make an analysis of the influence of different wavelet function and decomposition upon the gross error by the case study of mine drilling data, and therefore it finally confirms the use of db2 wavelet decomposition into four layers for gross error detection. Meanwhile, it accurately pinpoints the existing gross error which should be eliminated combining with the spatial distribution characteristics of the mine drilling data. It is proved to be practical by applying the way of wavelet analysis to detecting mining spatial data and which is of great value to solve the deficiency of traditional gross error detection