基于信号子空间增强和端点检测的大地电磁噪声压制

为了进一步保留大地电磁低频段的有用信息、提高矿集区复杂噪声环境下大地电磁测深深部探测能力,在形态滤波的基础上结合信号子空间增强和端点检测做二次信噪分离处理.首先,针对形态滤波预提取的噪声轮廓运用信号子空间增强分离出信号子空间和噪声子空间.然后,将信号子空间和重构信号相结合并将噪声子空间置零.最后,借鉴端点检测做后处理,以识别波形突变的起止点.仿真结果表明,卡尼亚电阻率曲线在低频段的数据质量得到了明显改善、视电阻率值相对稳定;有效地补偿了形态滤波处理过程中损失的低频有用信号,其结果更加真实地反映了测点本身所固有的大地电磁深部构造信息.

Noise suppression for magnetotelluric sounding data based on signal subspace enhancement and endpoint detection

To retain useful information of magnetotelluric low frequency band and improve the capacity of magnetotelluric deep detection in ore concentration area with complex noises, the combined signal subspace enhancement with endpoint detection is proposed based on morphology filtering to secondary signal-to-noise separation processing. Firstly, aimed at noise contour extracted by morphology filtering, we use signal subspace enhancement to separate signal subspace and noise subspace for pretreatment. Secondly, the signal subspace is combined with reconstructed signal and the noise subspace is set to zero. Finally, endpoint detection for post-processing is carried out in order to identify the start and end points of the waveform mutation. Simulated results show that Cagniard resistivity curve in the low frequency band has been improved obviously, and the apparent resistivity value is relatively stable. The proposed method is better to offset the loss of low frequency useful information in the process of the morphological filtering, and the results can even more truly reflect the inherent deep structural information of low frequency components for the measured point itself.