基于核主成分分析的时间域航空电磁去噪方法

时间域航空电磁数据往往在测量过程中受到天然和人文噪声的干扰.如果不能很好滤除这些电磁噪声，那么将会降低资料质量、影响反演的精度，甚至获得错误的解释结果.本文提出了一种基于核主成分分析的去噪方法，通过核主成分分析提取叠加后衰减曲线的主成分，然后使用能量占比方法分离反映地下介质的有效信号和噪声，最后使用反映地下介质的特定成分进行重构.本文所推荐的去噪方法不仅能剔除天然噪声，例如天电产生的尖脉冲或者振荡，而且能有效地抑制人文噪声.分别使用基于核主成分分析的去噪方法，以及AeroTEM软件的处理方法对同样的吊舱式时间域直升机航空电磁勘查系统实测数据进行处理，并比较其结果.处理结果表明：所推荐的去噪方法要优于AeroTEM软件.

A denoising method based on kernel principal component analysis for airborne time domain electromagnetic data

Airborne time domain electromagnetic (ATEM) data are often contaminated by natural noise and culture noise during the flight survey. If an inappropriate filter is taken to denoise for airborne electromagnetic data, it will result in low quality of data, less accuracy of the inversion and even wrong interpretation. This paper presents a denoising method based on kernel principal component analysis. Firstly, kernel principal component analysis is applied to extract the principal component of the raw decay curves; then the components reflecting subsurface media distribution and noise may be separated using the energy ratio method; finally, these components reflecting subsurface media are employed to reconstruct the decay curves. The denoising method proposed not only can remove natural noise such as spikes or sferics, but also effectively suppress the culture and shaking noise. In the experiments, we use the proposed denoising method and AeroTEM software respectively, to process the same helicopter time domain electromagnetic data measured in site. The experimental results show that the denoising method based on the kernel principal component analysis (KPCA) proposed in this paper is superior to the filtering method of the AeroTEM.