基于Context模型的Shearlet变换地面微地震随机噪声压制

地面微地震监测采集到的微地震信号通常能量微弱,信噪比低,如何提高微震数据的信噪比是数据处理的难题.Shearlet变换是一种新型的多尺度几何分析方法,具有敏感的方向性和较强的稀疏表示特性,能起到很好的随机噪声压制效果.由于地面微震数据的有效信号大多被淹没在噪声中,基于传统阈值的Shearlet变换（the traditional threshold-based Shearlet transform TST）只考虑到尺度或方向的阈值,在去噪过程中会过度扼制有效信号系数,造成有效信号能量损失.因而,本文建立Context模型,得到基于Context模型的Shearlet变换（the Context-model-based Shearlet transform CMST）方法,改进传统Shearlet阈值方法的不足.我们通过所建立的Context模型将能量相近的各方向系数划分为同一组,并分组估计阈值,分别处理各部分系数,达到微弱同相轴有效恢复的目的.通过TST及CMST的模拟实验与实际地面微震记录处理结果对比可知,本文方法在低信噪比条件下比对比方法更加有效地恢复地面微震数据的微弱信号,随机噪声压制效果明显,在-10 dB条件下,提升信噪比18.3741 dB.

     

Surface nuclear magnetic resonance signals recovery by integration of a non‐linear decomposition method with statistical analysis

Presence of noise in the acquisition of surface nuclear magnetic resonance data is inevitable. There are various types of noise, including Gaussian noise, spiky events, and harmonic noise that affect the signal quality of surface nuclear magnetic resonance measurements. In this paper, we describe an application of a two‐step noise suppression approach based on a non‐linear adaptive decomposition technique called complete ensemble empirical mode decomposition in conjunction with a statistical optimization process for enhancing the signal‐to‐noise ratio of the surface nuclear magnetic resonance signal. The filtering procedure starts with applying the complete ensemble empirical mode decomposition method to decompose the noisy surface nuclear magnetic resonance signal into a finite number of intrinsic mode functions. Afterwards, a threshold region based on de‐trended fluctuation analysis is defined to identify the noisy intrinsic mode functions, and then the no‐noise intrinsic mode functions are used to recover the partially de‐noised signal. In the second stage, we applied a statistical method based on the variance criterion to the signal obtained from the initial phase to mitigate the remaining noise. To demonstrate the functionality of the proposed strategy, the method was evaluated on an added‐noise synthetic surface nuclear magnetic resonance signal and on field data. The results show that the proposed procedure allows us to improve the signal‐to‐noise ratio significantly and, consequently, extract the signal parameters (i.e., and V₀) from noisy surface nuclear magnetic resonance data efficiently.     

基于自适应多分辨率奇异值分解的大地电磁数据处理

针对强电磁干扰极易掩盖微弱的大地电磁有用信号,本文结合奇异值分解在去噪方面的优越性,提出基于自适应多分辨率奇异值分解（Adaptive Multi-Resolution Singular Value Decomposition,AMRSVD）的大地电磁数据处理方法.首先对大地电磁数据构建Hankel矩阵,利用MRSVD得到不同分辨率的近似信号和细节信号;然后选用近似信号和细节信号的标准差差值,对大地电磁数据进行信噪辨识;接着结合MRSVD和相邻细节信号的标准差差值,提出先验信息未知情况下的AMRSVD法;最后对辨识出的强干扰运用AMRSVD去除噪声,重构有用信号.实验结果表明,该方法的处理效率高,能有效分离出相关性较强的噪声,时间序列和视电阻率-相位曲线均得到有效改善.

     

基于信号子空间分解的三维地震资料高分辨率处理方法

提出了一种新的基于信号子空间分解技术的3 D地震资料高分辨率处理方法.利用信号子空间分解技术,不仅可以分离3 D地震资料中的信号和噪声,而且可以进一步根据地震同相轴的倾角不同,将混合信号进行分解得到单个同相轴信号.通过丢弃噪声子空间,只对不同信号子空间重构的信号利用谱白化技术进行高分辨率处理,然后累加所有处理结果,从而达到既提高地震资料的分辨率,又提高地震资料信噪比的目的.对合成资料和实际地震资料的处理结果表明,此法具有好的应用前景.     

Active sensing using impedance‐based ARX models and extreme value statistics for damage detection

In this paper, the applicability of an auto‐regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established. Damage sensitive features that explicitly consider non‐linear system input/output relationships are extracted from the ARX model. Furthermore, because of the non‐Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to non‐linear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three‐story building model. It is found that the vibration‐based method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance‐based active sensing method using piezoelectric (PZT) material as both an actuator and a sensor is then investigated as an alternative solution to the problem of damage localization. Copyright © 2005 John Wiley & Sons, Ltd.