大地电磁的小波变换—独立分量分析去噪

将小波变换与独立分量分析相结合,提出基于小波分析的独立分量分析对大地电磁数据进行去噪处理的方法。模拟信号仿真实验结果表明,本文方法的去噪稳定性优于传统小波阈值去噪方法;实际大地电磁观测资料去噪结果表明,该方法能较有效地去除噪声,特别是在非极低频区域去噪效果尤其明显,保证了后期数据的质量。

基于频域约束独立成分分析的经验模态分解去噪方法

噪声污染是煤岩动力灾害电磁监测应用中需要解决的重要问题,去噪效果的好坏直接影响灾害预测的准确性。经验模态分解(Empirical Mode Decomposition,EMD)是目前电磁信号去噪中应用最多的一种方法,但当信号与噪声时频特征相近时,该算法存在严重的内蕴模态函数(Intrinsic Mode Function,IMF)混叠现象(即部分模态函数仍为信号与噪声的组合)。针对该问题,提出一种基于经验模态分解和频域约束独立成分分析的去噪方法,首先利用EMD将电磁信号分解为多个IMF分量,通过计算各分量与原信号间的互相关系数判断存在模态混叠现象过渡IMF,再以过渡IMF后续分量的频域为约束条件,对过渡IMF进行独立成分分析,去除过渡分量中的噪声;最后将去噪后的过渡分量与其后续分量进行重构,得到去噪后的信号。分别以含噪Ricker子波和现场电磁信号为例,利用信噪比定量验证了上述方法对处理现场电磁信号模态混叠问题的有效性,同时频域约束条件下的独立成分分析去噪收敛快、效率高,适合海量实时监测信号快速去噪使用。

参考道方法在大地电磁测深数据中的应用研究

通过选取不同的参考距离、不同质量的大地电磁实测数据作为互参考点,研究在分别采用本地电参考,本地磁参考,互参考电场,互参考磁场处理后的大地电磁测深数据的卡尼亚电阻率曲线及相位曲线,及分析了参考道方法在大地电磁测深数据处理中的应用效果。结果表明,采用互参考方法优于采用本地参考方法,且采用磁场作为参考信号要明显好于采用电场作为参考信号,所以多用磁场作为参考信号,也表明了互参考方法是有效的,可行的。

Power-line interference suppression of MT data based on frequency domain sparse decomposition

Power-line interference is one of the most common noises in magnetotelluric(MT)data.It usually causes distortion at the fundamental frequency and its odd harmonics,and may also affect other frequency bands.Although trap circuits are designed to suppress such noise in most of the modern acquisition devices,strong interferences are still found in MT data,and the power-line interference will fluctuate with the changing of load current.The fixed trap circuits often fail to deal with it.This paper proposes an alternative scheme for power-line interference removal based on frequency-domain sparse decomposition.Firstly,the fast Fourier transform of the acquired MT signal is performed.Subsequently,a redundant dictionary is designed to match with the power-line interference which is insensitive to the useful signal.Power-line interference is separated by using the dictionary and a signal reconstruction algorithm of compressive sensing called improved orthogonal matching pursuit(IOMP).Finally,the frequency domain data are switched back to the time domain by the inverse fast Fourier transform.Simulation experiments and real data examples from Lu-Zong ore district illustrate that this scheme can effectively suppress the power-line interference and significantly improve data quality.Compared with time domain sparse decomposition,this scheme takes less time consumption and acquires better results.

Time-domain denoising of time–frequency electromagnetic data

Time–frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains.The observation data contains three types of noise:the harmonics interference at 50 Hz,high-frequency random noise,and low-frequency noise.We use frequency-domain bandstop filtering to remove the harmonics interference noise,segmentation and extension median filtering,and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise,respectively;furthermore,we base the selection of median filtering window size on the variance and skewness coefficient of the data.We first remove the harmonics interference at 50 Hz,then the high-frequency noise,and finally the low-frequency noise.We test the proposed methodology by using theory and experiments,and we find that the three types of noises are removed,the phase and amplitude information of the signal are maintained,and high-quality waveforms are obtained in the time domain.

Airborne electromagnetic data denoising based on dictionary learning

Time-domain airborne electromagnetic(AEM)data are frequently subject to interference from various types of noise,which can reduce the data quality and affect data inversion and interpretation.Traditional denoising methods primarily deal with data directly,without analyzing the data in detail;thus,the results are not always satisfactory.In this paper,we propose a method based on dictionary learning for EM data denoising.This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal.In the process of dictionary learning,the random noise is fi ltered out as residuals.To verify the eff ectiveness of this dictionary learning approach for denoising,we use a fi xed overcomplete discrete cosine transform(ODCT)dictionary algorithm,the method-of-optimal-directions(MOD)dictionary learning algorithm,and the K-singular value decomposition(K-SVD)dictionary learning algorithm to denoise decay curves at single points and to denoise profi le data for diff erent time channels in time-domain AEM.The results show obvious diff erences among the three dictionaries for denoising AEM data,with the K-SVD dictionary achieving the best performance.

A Zernike-moment-based non-local denoising filter for cryo-EM images

Cryo-electron microscopy (cryo-EM) plays an important role in determining the structure of proteins, viruses, and even the whole cell. It can capture dynamic structural changes of large protein complexes, which other methods such as X-ray crystallography and nuclear magnetic resonance analysis find difficult. The signal-to-noise ratio of cryo-EM images is low and the contrast is very weak, and therefore, the images are very noisy and require filtering. In this paper, a filtering method based on non-local means and Zernike moments is proposed. The method takes into account the rotational symmetry of some biological molecules to enhance the signal-to-noise ratio of cryo-EM images. The method may be useful in cryo-EM image processing such as the automatic selection of particles, orientation determination, and the building of initial models.