Airborne Gamma-ray Spectrum Data Leveling Method Based on Image Eigenvalue Extraction
The extraction of image eigenvalue is the utilization of mathematical methods to solve the non-linear uncer-tainty problem of spatial distribution in image data. Traditional methods for leveling airborne gamma-ray spectrum data are highly susceptible to errors due to their reliance on the experiential judgment of technical personnel. Based on this problem, a leveling method for airborne gamma-ray spectrum data using image eigenvalue extraction is proposed in the paper. This method utilizes feature clustering to partition the data into strips and background, preserving data unaffected by strip noise contamination, thereby making the leveled data closer to the real Earth radiation field. While processing along the measure-ment line, neighborhood point search is conducted in both the measurement and cutting directions, avoiding errors intro-duced by interpolation and fully considering the spatial relationships in the data. Finally, combined with the improved sam-ple entropy, geological background information is preserved intact during the leveling process. The results indicate that air-borne gamma-ray spectrum data leveling method based on image eigenvalue extraction can effectively eliminate strip anoma-lies, with anomalous points accounting for only 30％ ～50％.
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