20 August 2010 Application of filtering techniques in preprocessing magnetic data
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Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78201S (2010) https://doi.org/10.1117/12.866729
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China
Abstract
High precision magnetic exploration is a popular geophysical technique for its simplicity and its effectiveness. The explanation in high precision magnetic exploration is always a difficulty because of the existence of noise and disturbance factors, so it is necessary to find an effective preprocessing method to get rid of the affection of interference factors before further processing. The common way to do this work is by filtering. There are many kinds of filtering methods. In this paper we introduced in detail three popular kinds of filtering techniques including regularized filtering technique, sliding averages filtering technique, compensation smoothing filtering technique. Then we designed the work flow of filtering program based on these techniques and realized it with the help of DELPHI. To check it we applied it to preprocess magnetic data of a certain place in China. Comparing the initial contour map with the filtered contour map, we can see clearly the perfect effect our program. The contour map processed by our program is very smooth and the high frequency parts of data are disappeared. After filtering, we separated useful signals and noisy signals, minor anomaly and major anomaly, local anomaly and regional anomaly. It made us easily to focus on the useful information. Our program can be used to preprocess magnetic data. The results showed the effectiveness of our program.
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Haijun Liu, Haijun Liu, Yongping Yi, Yongping Yi, Hongxia Yang, Hongxia Yang, Guochuang Hu, Guochuang Hu, Guoming Liu, Guoming Liu, } "Application of filtering techniques in preprocessing magnetic data", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201S (20 August 2010); doi: 10.1117/12.866729; https://doi.org/10.1117/12.866729
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