基于匹配追踪和遗传算法的大地电磁噪声压制
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摘要
针对匹配追踪计算量大、大地电磁数据处理效率低的问题,提出基于匹配追踪和遗传算法的大地电磁噪声压制方法.首先,利用Gabor原子构建过完备原子库,并对过完备原子库集合进行划分.然后,借助遗传算法的自适应性,快速搜寻最优匹配原子及所在位置.最后,运用最优匹配原子对待处理信号进行稀疏分解,重构有用信号.通过对计算机模拟的典型强干扰和矿集区实测大地电磁数据进行分析处理,实验结果表明,相对于匹配追踪和正交匹配追踪,文中所提方法能从过完备原子库中快速、自适应地选取最优匹配原子与不同噪声干扰类型高精度的匹配,极大地提升了计算效率;大地电磁时间域序列中的大尺度强干扰被有效剔除,视电阻率曲线更为光滑、连续,低频段的数据质量得到明显改善.
In order to solve the problem that matching pursuit has large amount of calculation and magnetotelluric data processing is low efficiency,a new method for magnetotelluric noise suppression based on matching pursuit and genetic algorithm is proposed in this paper.First of all,we use Gabor atom to build an over-complete dictionary,and divide the over-complete dictionary collection.Then,we use the adaptability of genetic algorithm to search the optimal matching atom and the location.Finally,we use the optimal matching atom to sparse decompose the signal to be processed and reconstruct the useful signal.Through analysis and processing for the typical strong interference simulated by computer and the measured magnetotelluric data of ore concentration area,we get some important conclusions as follows.Compared with matching pursuit and orthogonal matching pursuit,the proposed method can select the optimal matching atoms from the over-completedictionary quickly and adaptively.At the same time,the method ensures that the optimal matching atoms with different kinds of noise interference have high precision matching,which greatly improves the calculation efficiency.After processing by the proposed method,the large-scale strong interference of magnetotelluric data in time domain sequence is effectively removed and the apparent resistivity curve is more smooth and continuous.Moreover,the quality of magnetotelluric data for the low frequency band has been improved significantly.
In order to solve the problem that matching pursuit has large amount of calculation and magnetotelluric data processing is low efficiency,a new method for magnetotelluric noise suppression based on matching pursuit and genetic algorithm is proposed in this paper.First of all,we use Gabor atom to build an over-complete dictionary,and divide the over-complete dictionary collection.Then,we use the adaptability of genetic algorithm to search the optimal matching atom and the location.Finally,we use the optimal matching atom to sparse decompose the signal to be processed and reconstruct the useful signal.Through analysis and processing for the typical strong interference simulated by computer and the measured magnetotelluric data of ore concentration area,we get some important conclusions as follows.Compared with matching pursuit and orthogonal matching pursuit,the proposed method can select the optimal matching atoms from the over-completedictionary quickly and adaptively.At the same time,the method ensures that the optimal matching atoms with different kinds of noise interference have high precision matching,which greatly improves the calculation efficiency.After processing by the proposed method,the large-scale strong interference of magnetotelluric data in time domain sequence is effectively removed and the apparent resistivity curve is more smooth and continuous.Moreover,the quality of magnetotelluric data for the low frequency band has been improved significantly.
引文
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Cai T T,Wang L.2011.Orthogonal matching pursuit for sparse signal recovery with noise.IEEE Transactions on Information Theory,57(7):4680-4688.
Chang H W,Ma H,Zhang J Q,et al.2014.Optimization of metamaterial based weighted real-coded genetic algorithm.Acta Physica Sinica(in Chinese),63(8):087804.
Chen J,Heincke B,Jegen M,et al.2012.Using empirical mode decomposition to process marine magnetotelluric data.Geophysical Journal International,190(1):293-309.
Chen S S,Donoho D L,Saunders M A.1998.Atomic decomposition by basis pursuit.SIAM Journal on Scientific Computing,20(1):33-61.
Dong S W,Li T D,Chen X H,et al.2012.Progress of deep exploration in mainland China:A review.Chinese Journal of Geophysics(in Chinese),55(12):3884-3901,doi:10.6038/j.issn.0001-5733.2012.12.002.
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Fan C S,Li T L,Wang D Y.2008.Treatment of wavelet transform for square wave noise in MT date.Journal of Jilin University(Earth Science Edition)(in Chinese),38(S1):61-63.
Gamble T D,Goubau W M,Clarke J.1979.Magnetotellurics with a remote magnetic reference.Geophysics,44(1):53-68.
Gao R,Xu H N,Hu G.2008.MP algorithm for signal sparse decomposition based on over-complete dictionary partitioning and GA.Science Technology and Engineering(in Chinese),8(4):914-916,920.
Holland J H.1975.Adaptation in Natural and Artificial Systems.Michigan:University of Michigan Press.
Jing J E,Wei W B,Chen H Y,et al.2012.Magnetotelluric sounding data processing based on generalized S transformation.Chinese Journal of Geophysics(in Chinese),55(12):4015-4022,doi:10.6038/j.issn.0001-5733.2012.12.013.
Kapple K N.2012.A data variance technique for automated despiking of magnetotelluric data with a remote reference.Geophysical Prospecting,60(1):179-191.
LüQ T,Dong S W,Tang J T,et al.2015.Multi-scale and integrated geophysical data revealing mineral systems and exploring for mineral deposits at depth:A synthesis from SinoProbe-03.Chinese Journal of Geophysics(in Chinese),55(12):4319-4343,doi:10.6038/cjg20151201.
Li G,Tang J T,Xiao X,et al.2017.Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering.Applied Geophysics,14(4):581-589.
Li J,Tang J T,Xiao X,et al.2014a.Magnetotelluric data processing based on combined generalized morphological filter.Journal of Central South University(Science and Technology Edition)(in Chinese),45(1):173-185.
Li J,Tang J T,Wang L,et al.2014b.Noise suppression for magnetotelluric sounding data based on signal subspace enhancement and endpoint detection.Acta Physica Sinica(in Chinese),63(1):019101.
Li J,Tang J T,Cai J H,et al.2016.Separation and identification of magnetotelluric weak data using multi-scale morphology and recurrence plot.Journal of Central South University(Science and Technology Edition)(in Chinese),47(11):3890-3898.
Li J,Tang J T,Xu Z M,et al.2017.Magnetotelluric noise suppression base on signal-to-noise identification in ore concentration area.Chinese Journal of Geophysics(in Chinese),60(2):722-737,doi:10.6038/cjg20170224.
Li J,Zhang X,Gong J Z,et al.2018.Signal-noise identification of magnetotelluric signals using fractal-entropy and clustering algorithm for targeted de-noising.Fractals,26(2):1840011.
Ling Z B,Wang P Y,Wan Y X,et al.2016.A combined wavelet transform algorithm used for de-noising magnetotellurics data in the strong human noise.Chinese Journal of Geophysics(in Chinese),59(9):3436-3447,doi:10.6038/cjg20160926.
Mallat S G,Zhang Z F.1993.Matching pursuits with time-frequency dictionaries.IEEE Transactions on Signal Processing,41(12):3397-3415.
Pan H X,Zhang X.2016.Rolling bearing fault diagnosis based on GAMP and EEMD.Journal of Vibration and Shock(in Chinese),35(20):190-196.
Shao J,Yin Z K,Wang J Y,et al.2006.Set partitioning of the Over-complete dictionary in signal sparse decomposition.Journal of the China Railway Society(in Chinese),28(1):68-71.
Sutamo D,Vozoff K.1991.Phase-smoothed robust M-estimation of magnetotelluric impedance function.Geophysics,56(12):1999-2007.
Tang J T,Hua X R,Cao Z M,et al.2008.Hilbert-Huang transformation and noise suppression of magnetotelluric sounding data.Chinese Journal of Geophysics(in Chinese),51(2):603-610,doi:10.3321/j.issn:0001-5733.2008.02.034.
Tang J T,Li J,Xiao X,et al.2012a.Mathematical morphology filtering and noise suppression of magnetotelluric sounding data.Chinese Journal of Geophysics(in Chinese),55(5):1784-1793,doi:10.6038/j.issn.0001-5733.2012.05.36.
Tang J T,Xu Z M,Xiao X,et al.2012b.Effect rules of strong noise on magnetotelluric(MT)sounding in the Luzong ore cluster area.Chinese Journal of Geophysics(in Chinese),55(12):4147-4159,doi:10.6038/j.issn.0001-5733.2012.12.027.
Tang J T,Liu Z J,Liu F Y,et al.2015.The denoising of the audio magnetotelluric data set with strong interferences.Chinese Journal of Geophysics(in Chinese),58(12):4636-4647,doi:10.6038/cjg20151225.
Tang J T,Li G,Xiao X,et al.2017.Strong noise separation for magnetotelluric data based on a signal reconstruction algorithm of compressive sensing.Chinese J.Geophys.(in Chinese),60(9):3642-3654,doi:10.6038/cjg20170928.
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