作者: CHEN Bin , LU Cong‐De , LIU Guang‐Ding
DOI: 10.1002/CJG2.20087
关键词: Kernel principal component analysis 、 Pattern recognition 、 Data quality 、 Artificial intelligence 、 Inversion (meteorology) 、 Principal component analysis 、 Radio atmospheric 、 Software 、 Statistics 、 Time domain 、 Computer science 、 Noise reduction
摘要: Airborne time-domain electromagnetic (ATEM) data usually contain natural and cultural noise, which can lower quality, influence inversion accuracy or even lead to incorrect interpretation if it is not removed from using an appropriate filter. To solve this problem, work suggests a denosing method based on kernel principal component analysis. Firstly, extracts the stacked decay curves. Then useful signals, are associated with subsurface media, noise separated energy ratio. Finally, these signals used perform reconstruction. This only remove such as spikes oscillation caused by sferies, but also effectively suppress noise. Using AeroTEM software, real ATEM helicopter survey processed separately. Comparison of results shows that denoising effect suggested paper superior software.