Open AccessNoise suppression for magnetotelluric sounding data based on signal subspace enhancement and endpoint detection
Author(s) -
Li Jin,
Jingtian Tang,
Ling Wang,
Xiao Xiao,
Zhang Lincheng
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.019101
Subject(s) - signal subspace , subspace topology , noise (video) , signal (programming language) , waveform , acoustics , offset (computer science) , white noise , algorithm , computer science , pattern recognition (psychology) , artificial intelligence , speech recognition , mathematics , physics , telecommunications , image (mathematics) , programming language , radar
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.