共查询到10条相似文献，搜索用时 100 毫秒
1.
地面磁共振技术能够对地下水进行直接探测,具有定性定量的优点,是一种新兴的地球物理方法.然而,磁共振信号只有纳伏级,极其微弱,易受环境噪声干扰,尤其是具有拉莫尔频率的噪声,在时频域上均与信号重叠,难以有效去除,导致提取的信号参数准确度低、反演解释误差较大.本文针对同频噪声干扰问题,提出了相关建模检测(CMDT)方法,通过相关方法实现频谱迁移和低通滤波,结合信号和噪声特征建立数学模型,采用模型变换实现同频噪声的抑制,并利用最小二乘指数拟合方法提取高精度SNMR信号.为了对新方法进行定量分析,以验证其效果,对含有不同幅度的同频噪声和磁共振信号进行仿真实验,实验结果表明在信噪比为-31.17 dB的情况下,所有参数的最大提取误差不大于1.22%,验证了新方法能够在压制同频噪声的同时提取出高精度信号参数.为了模拟野外情况,在同频噪声和信号数据中加入随机噪声进行实验,结果表明当信噪比大于-10.12 dB时,CMDT方法仍可以获取有效的信号.因此,本文的研究为处理含有同频噪声干扰的实际SNMR信号数据提供了理论依据,为后期高精度反演提供了技术支撑. 相似文献
2.
Wiener optimal filtering of GRACE data 总被引：4，自引：0，他引：4
We present a spatial averaging method for Gravity Recovery and Climate Experiment (GRACE) gravity-field solutions based on
the Wiener optimal filtering.
The optimal filter is designed from the least-square minimization of the difference between the desired and filtered signals.
It requires information about the power spectra of the desired gravitational signal and the contaminating noise, which is
inferred from the average GRACE degree-power spectrum. We show that the signal decreases with increasing spherical harmonic
degree j with approximately j−b, where b = 1.5 for GRACE data investigations. This is termed the Second Kaula rule of thumb for temporal variations of the
Earth’s gravity field. The degree power of the noise increases, in the logarithmic scale, linearly with increasing j.
The Wiener optimal filter obtained for the signal model with b = 1.5 closely corresponds to a Gaussian filter with a spatial
half width of 4° (∼440 km). We find that the filtered GRACE gravity signal is relatively insensitive to the exponent b of
the signal model, which indicates the robustness of Wiener optimal filtering. This is demonstrated using the GFZ-GRACE gravity-field
solution for April 2004. 相似文献
3.
基于ARMA模型非因果空间预测滤波(英文) 总被引：3，自引：1，他引：2
常规频域预测滤波方法是建立在自回归(autoregressive,AR)模型基础上的,这导致滤波过程中前后假设的不一致,即首先利用源噪声的假设计算误差剖面,却又将其作为可加噪声而从原始剖面中减去来得到有效信号。本文通过建立自回归-滑动平均(autoregres sive/moving-average,ARMA)模型,首先求解非因果预测误差滤波算子,然后利用自反褶积形式投影滤波过程估计可加噪声,进而达到去除随机噪声目的。此过程有效避免了基于AR模型产生的不一致性。在此基础上,将一维ARMA模型扩展到二维空间域,实现了基于二维ARMA模型频域非因果空间预测滤波在三维地震资料随机噪声衰减中的应用。模型试验与实际资料处理表明该方法在很好保留反射信息同时,压制随机噪声更加彻底,明显优于常规频域预测去噪方法。 相似文献
4.
本文基于YH4DVAR业务系统构建了集合资料同化试验平台,利用10个集合样本统计得到的流依赖背景误差能显著改进业务应用中背景误差方差的结构和大小.但是受样本数的限制,背景误差方差的集合估计值中引入了大量的随机取样噪声.为了降低噪声对估计值的影响,本文采用谱滤波方法,根据信号和噪声尺度的统计特征构造一个低通滤波器来滤除背景误差方差估计值中的大部分随机取样噪声.在2013年第九号台风"飞燕"的集合方差滤波试验中,10个样本的滤波结果优于30个样本的集合估计值.谱滤波方法的成功应用有效降低了集合资料同化系统对集合样本数的要求,将是集合资料同化系统未来业务化运行的一项不可或缺的关键技术. 相似文献
5.
径向道变换(Radial Trace Transform)是将地震道集振幅值从偏移距一双程旅行时坐标系变换到视速度-双程旅行时坐标系,通过这种坐标系的变换,使相干噪声与有效信号在视速度和频率方面都有效分离.本文在介绍RT变换基本原理基础上,分析了RT变换中两种常用插值方法及其特点.并利用对模拟地震资料的处理,证明了RT域模拟-相减法较其带通滤波法在相干噪声压制与反射信号保持方面具有明显优势.最后,根据噪声特点,通过选择合理RT滤波参数,对实际地震资料进行处理试验,获得了较好的去噪效果,明显提高了资料信噪比,验证了研究方法的有效性. 相似文献
6.
Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance. 相似文献
7.
Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction. 相似文献
8.
Surface nuclear magnetic resonance signals recovery by integration of a non‐linear decomposition method with statistical analysis 
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下载免费PDF全文 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. 相似文献
9.
消减随机噪声是目前陆地地震勘探数据处理的关键问题之一,分析随机噪声的产生机制及特征是对其进行有效压制的先决条件.本文针对中国南方山地金属矿区的勘探环境,根据随机噪声中包含的自然噪声和人文噪声的发声机理分别确定其噪声源函数,以波动方程作为噪声传播模型对山地地区随机噪声进行建模,将随机噪声作为一个综合波场,并且与实际噪声记录进行比较.随机噪声记录作为时空域的二维随机过程,分别对模拟噪声和实际噪声记录的时间域波形(振动图)特征包括频谱、功率谱密度,相空间轨迹图,统计量特征(能量分布,累积分布,均值,方差,峰度,偏度),和空间域波形(波剖面)特征包括波数谱和统计量特征进行比较,对比结果显示在时空域模拟噪声和实际噪声都有基本相同的性质,证明了本文对随机噪声模拟方法的可行性,为进一步研究随机噪声时空域传播特性以及噪声消除奠定理论基础. 相似文献
10.
磁共振探测技术（Magnetic Resonance Sounding，MRS）以其无损、定量、直接等优势，被广泛应用于地下水调查、水文环境评价以及灾害水源预警等领域.在实际应用中，强工频谐波和随机噪声等严重影响MRS信号的质量，导致后续水文参数解释不准确.针对这一问题，提出谐波建模和自相关相结合的方法进行消噪以及信号特征参数提取.首先构建工频谐波模型，针对建模算法严重依赖工频基频精度的问题，采用自适应扫描方式搜索方案，大幅提高搜索准确度和速度；其次推导了MRS信号自相关表达式，提出了自相关参数提取的非线性拟合方法.仿真数据结果表明，建模消噪方法有效消除了工频谐波，信噪比平均提升了17.03 dB；自相关处理后，信噪比进一步提升了16.10 dB，初始振幅和弛豫时间参数提取结果的准确度比处理前分别提高了3.8倍和2.8倍.通过不同信噪比和弛豫时间的重复实验，得到当噪声水平小于200 nV和弛豫时间大于200 ms时，自相关参数提取具有较高的稳定性.最后，通过野外实测数据处理实验，进一步验证了联合消噪和参数提取方法的有效性. 相似文献