Nonlinear data processing method for the signal enhancement of GPR data

An alternative data processing procedure is proposed in this paper for the purpose of enhancing the signal/noise (S/N) ratio of ground penetrating radar (GPR) data. The processing methodology is achieved by performing the logarithmic transform in conjunction with the ensemble empirical mode decomposition (EEMD), a new nonlinear data analysis method in signal processing. The synthetic model study and field example indicate that the logarithmic transform is effective in alleviating the attenuation problem. Additionally, the spectrogram obtained from Hilbert-Huang transform (HHT) shows that the decomposition sensitivity of the EEMD method is greatly improved with the aid of the logarithmic transform. This new method allows us to extract the signal components from noisy GPR data efficiently. The success of this study suggests a possible nonlinear analysis application in future GPR investigation, particularly in the filter design and gain correction.     

基于探地雷达信号处理的小波基选取研究

针对探地雷达信号处理和分析时小波基选取存在的问题,本文在分析探地雷达信号特点的基础上,首先从理论上讨论小波基的选取准则,然后再从实验角度进行对比、判别,认为在进行小波分解和重构时应该分别选择不同的小波基函数进行处理,这样可以保证重构信号的精确度,增强对信号的处理能力,从而也突破了以往分解与重构时都采用同一个小波基进行处理的做法.最后通过实际资料的处理,指出bior2.6小波基在进行雷达信号处理时效果最佳,不仅去噪彻底,而且能够保留有效信号的高频部分,提高信号的分辨率和信噪比,为后续解释工作打好了基础.     

多分辨分析理论在井-震匹配中的应用

简单介绍了多分辨率逼近理论,详尽分析了地震与声测井资料在尺度上的差异,进而提出了一种解决井-震尺度差异的新方法--多分辨率逼近方法。通过理论模型及实际资料算例验证了该方法的有效性,并且与常用的抗混频滤波方法进行了比较。结果表明,多分辨率逼近方法更能保持信号的局部化性质,使地震与声波测井资料能更好地匹配。     

基于EMD小波阈值滤波的地磁数据去噪分析

采用小波阈值滤波、经验模态分解滤波和基于经验模态分解的小波阈值滤波3种方法,对磁通门磁力仪观测数据进行去噪,有效去除或抑制郫县地震台地磁数据的地电干扰.     

MT时间序列的小波去噪分析

从本质上说 ,MT时间序列中噪声的强度与类型是能否取得MT响应参数无偏估计的决定性因素。当MT时间序列中磁场和电场中都含有相关噪声时 ,传统的去噪方法已无能为力。结合小波分析与MT时间序列的特征 ,提出了一种基于小波分析的MT时间序列去噪方法 ,讨论了基于小波分析的噪声识别 ,分析了理论数据通过小波分解与重构实现的去噪处理 ,探讨了对实测时间序列的固定源和随机干扰的去噪处理     

提取非金属管GPR图像的非线性技术

Non-linear Technique for Abstracting the Non-metallic Pipeline Image from GPR Section     

GNMF小波谱分离在地震勘探噪声压制中的应用

地震勘探资料噪声压制及信噪比提高是整个地震勘探信号处理过程中的重要任务,随着地震勘探深度的增加及其复杂性,人们对地震数据质量的要求越来越高.勘探环境的复杂化使得采集到的地震资料中有效信号被大量噪声淹没,无法清晰辨识,严重影响后续的数据处理与解释.小波去噪是地震勘探中常用且发展较成熟的一种方法,但是其涉及到的阈值函数选取问题一直令人困扰,虽然已有多种阈值函数被提出,但仍存在各自的缺陷.本文利用小波分解在时域及频域良好的信号细节体现特性,引入模式识别中的非负矩阵分解(NMF)谱分离思想,针对小波系数阈值优化问题,提出了一种小波域图非负矩阵分解(GNMF)消噪算法.该方法首先在小波分解基础上,利用GNMF算法实现小波分解系数谱中信号分量与噪声分量的谱分离,然后通过反变换重构各分离子谱对应的子信号,最后利用K均值聚类算法将得到的多个子信号划分为信号类及噪声类,最终得到重构信号及分离噪声.合成记录和实际地震资料的消噪结果验证了新方法在提高信号与噪声分离准确性和精度方面的有效性,同时新方法避免了阈值选取造成的噪声压制不理想或有效成分损失问题.与小波消噪结果的对比及数值分析也说明了新方法在噪声压制及有效成分保持方面的优势.     

DETECTION OF THIN BEDS WITH THE PSEUDO-ACOUSTIC IMPEDANCE SECTION*

The conventional seismic response of a thin bed approximates the time derivative of the incident wavelet, whereas the pseudo-impedance response approximates the incident wavelet. Consequently the pseudo-impedance response of a geological sequence composed of thin beds is simpler and easier to interpret than the conventional response. By calibrating the sonic log data with check-shot data and performing zero-phase seismic processing, the fit of the sonic log and pseudo-velocity section is improved. Discrepancies in amplitude and phase, however, generally remain. A five-step processing and interpretation procedure, which benefits from multichannel interpretation along the model seismic section generated from the sonic logs, is described. The method has been tested with field data. In the test the detection of thin beds and the estimation of the natural gas content was more reliable with the proposed procedure than with the conventional method.     

利用数字图像处理技术提高地震剖面图像信噪比

提出了利用数字图像处理技术提高地震剖面信噪比的新方法，首先根据数字图像处理要求的格式，对地震剖面数据进行转换，得到地震剖面图像，分析了地震数据特点和初步地震图像的实验结果后，设计了新的预处理方法——“二维沿层滤波”，在此基础上，利用可以计算帧间运动速度及其变化都较大的改进的光流分析技术，计算出多幅地震剖面对应点的偏移量，然后应用图像积累技术对这多幅地震剖面进行积累，实现对三维地震数据体提高信噪比的处理，该方法充分利用了三维地震信息，不但可以提高整个数据体的信噪比，而且可以减少信号能量的损失，并保持原来的信号能量关系，使地震剖面的质量得到明显提高，为地震解释奠定良好的基础。     

Total-variation improved split Bregman method for ground penetrating radar image restoration

In this paper, an improved data processing procedure is proposed for the purpose of ground penetrating radar (GPR) image restoration. The image processing method is achieved by combining the isotropic and anisotropic total-variation (TV) model of split Bregman, suppressing the staircasing effect and circumventing the edges blurred. Numerical experiments indicate that the proposed approach can well recover edges and most of the details of a (GPR) image. Hence, the proposed method is efficient in (GPR) image restoration.     

基于ARMA模型非因果空间预测滤波(英文)

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。     

Bayesian frequency-domain blind deconvolution of ground-penetrating radar data

Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing.     

S变换谱分解技术在深反射地震弱信号提取中的应用

在深反射地震资料处理中,当来自深部的有效弱信号和噪声干扰频带差异较小且难以区分时,传统滤波方法的应用会受到限制.谱分解方法是一种使用离散傅里叶变换,基于信号的频率-振幅谱等信息生成高分辨率地震图像的方法,通常用来识别介质物性横向分布特征,处理复杂介质内频谱变化和局部相位的不稳定性等问题,包括定位复杂断层和小尺度断裂等.S变换作为一种新的时频分析方法,具有自动调节分辨率的能力,近些年来被广泛应用到勘探地震、大地电磁等数据处理中,逐渐成为地球物理方法中噪声压制的有效方法之一.与常规石油反射地震资料相比,深反射主动源地震为了探测深部结构信息,常采用大药量激发方式、长排列观测系统等,导致深部有效信号基本湮灭在噪声干扰之中.针对深反射数据特点,本文结合谱分解和S变换技术,首先设计了简单的脉冲函数实验数据,证实S变换方法的有效性,同时说明谱分解方法的效果受所用时频分析方法影响较大,而其中决定分辨能力的变换窗函数的选取尤为重要.在此基础上,分别应用到深反射地震资料的单道和叠加剖面实际数据上,对比分析了传统变换谱分解和S变换谱分解的应用效果,单道资料对比结果表明:相比传统谱分解,S变换谱分解方法具有自动调节分辨率的能力,能够精确的标定深反射地震资料中弱信号不同时刻的频率分量;叠加剖面资料应用结果表明:由S变换谱分解得到的剖面结果与其他谱分解方法结果整体上具有较高的一致性,同时清晰地刻画出原叠加剖面上被噪声湮灭的低频细节特征,提高了剖面的分辨率及同相轴连续性;对比结果明显看出,Gabor变换谱分解方法得到的结果同相轴较为破碎,分析原因认为这是由Gabor变换的时频分解方法的定长窗函数所致,窗口大小不会随着信号频率的变化来调节长度,只能在处理的过程中根据一定的记录长度范围选取窗函数参数,而S变换谱分解方法在窗函数的选取时,通过时变信号的局部频率特征自动调节窗口长度,能够更好的刻画各个频段的细节特征,在深反射剖面成像应用中效果尤为明显.本文结果表明S变换谱分解技术在深地震叠加剖面上的应用有效地提高了来自深部弱反射信号的信噪比和分辨率,并刻画出了叠加剖面上所不具有的低频细节特征,在实际深反射地震资料处理中能有效保护低频弱信号获得更好的成像效果.本文为深地震反射资料中弱信号的保护处理找到一种有效的方法.     

小波多分辨分析方法在波动方程正演模拟中的应用

初步探讨小波多分辨分析理论在地震波动方程正演模拟中的应用，给出一种基于地震波场局部变化性质而自适应调空间网格点的波动方程数值算法，目前求解波动方程所用的有限差分法、有限元法以及伪谱法都不能根据波场的局部变化性质而动态选择空间网格点的大小、小波基函数在空间域和频域中都具有局部性特征，它的性质优于有限差分法，有限元法和伪谱法中所用的基函数，通过阀值运算，地震波场失发辨表示变得非常稀疏，同时地下介质中的主要信息又不会受到损害，本文将声波场的多分辨表示变得非常稀疏，同时地下介质中的主要信息又不会受到损害，本文将声波方程的矩阵表示形式小波多分辨分析的框架下进行了展开，通过对算子长阵的地震波场矩阵进行多分辨分解和压缩，得到了小波域中地震波场正演模拟算法。     

多尺度形态学在地震去噪中的应用

In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing.     

地震资料去噪方法、技术综合评述

地震资料去噪，无论是叠前还是叠后，都是处理中非常重要的内容.随着勘探技术的进步，地球物理界积累和开发的去噪软件已越来越多.对各种去噪方法进行分门别类，阐述其基本原理、物理意义、适用条件、发展前景，既有理论价值又有实际指导意义.本文从噪声的特征出发，首先对地震资料噪声进行了分类；然后综合评述了目前实际生产中常用的几种去噪方法，包括频率域滤波、频率波数域滤波、频率空间域滤波、Radon变换、聚束滤波、基于小波分解和重建的去噪方法等；最后还简述了去噪技术的应用及发展情况.     

地震数据正交投影分解方法的研究与应用

地震记录正交分解是值得研究的重要问题.本文将人工神经网络自组织学习算法引入到地震数据处理中,提出地震数据神经网络正交分解方法.在分解后的特征子空间中进行特征提取来消除相干和随机噪声,提高地震记录的信噪比.研究结果表明,这种实现方法是可行的,因特征滤波或数据压缩一般只需为数不多的主分量,本文的方法可根据滤波需要自适应提取所需主分量,可免去大量无用的计算,因此,本方法可降低滤波的计算成本,参与分解的道数越多效果越明显.最后,给出了对实际资料进行特征滤波的一个应用实例.     

地震资料去噪方法技术综合评述

地震资料去噪,无论是叠前还是叠后,都是处理中非常重要的内容.随着勘探技术的进步,地球物理界积累和开发的去噪软件已越来越多.对各种去噪方法进行分门别类,阐述其基本原理、物理意义、适用条件、发展前景,既有理论价值又有实际指导意义.本文从噪声的特征出发,首先对地震资料噪声进行了分类;然后综合评述了目前实际生产中常用的几种去噪方法,包括频率域滤波、频率波数域滤波、频率空间域滤波、Radon变换、聚束滤波、基于小波分解和重建的去噪方法等;最后还简述了去噪技术的应用及发展情况.     

Wavelet‐based cepstrum decomposition of seismic data and its application in hydrocarbon detection

How to use cepstrum analysis for reservoir characterization and hydrocarbon detection is an initial question of great interest to exploration seismologists. In this paper, wavelet‐based cepstrum decomposition is proposed as a valid technology for enhancing geophysical responses in specific frequency bands, in the same way as traditional spectrum decomposition methods do. The calculation of wavelet‐based cepstrum decomposition, which decomposes the original seismic volume into a series of common quefrency volumes, employs a sliding window to move over each seismic trace sample by sample. The key factor in wavelet‐based cepstrum decomposition is the selection of the sliding‐window length as it limits the frequency ranges of the common quefrency section. Comparison of the wavelet‐based cepstrum decomposition with traditional spectrum decomposition methods, such as short‐time Fourier transform and wavelet transform, is conducted to demonstrate the effectiveness of the wavelet‐based cepstrum decomposition and the relation between these two technologies. In hydrocarbon detection, seismic amplitude anomalies are detected using wavelet‐based cepstrum decomposition by utilizing the first and second common quefrency sections. This reduces the burden of needing dozens of seismic volumes to represent the response to different mono‐frequency sections in the interpretation of spectrum decomposition in conventional spectrum decomposition methods. The model test and the application of real data acquired from the Sulige gas field in the Ordos Basin, China, confirm the effectiveness of the seismic amplitude anomaly section using wavelet‐based cepstrum decomposition for discerning the strong amplitude anomalies at a particular quefrency buried in the broadband seismic response. Wavelet‐based cepstrum decomposition provides a new method for measuring the instantaneous cepstrum properties of a reservoir and offers a new field of processing and interpretation of seismic reflection data.     

基于经验模态分解的分数维地震随机噪声衰减方法

经验模态分解算法(EMD)是一种基于有效波和噪声尺度差异进行波场分离的随机噪声压制方法,但由于实际地震数据波场复杂,导致模态混叠较严重,仅凭该方法进行去噪很难达到理想效果.本文基于EMD算法对信号多尺度的分解特性,结合Hausdorff维数约束条件,提出一种用于地震随机噪声衰减的新方法.首先对地震数据进行EMD自适应分解,得到一系列具有不同尺度的、分形自相似性的固有模态分量(IMF);在此基础上,基于有效信号和随机噪声的Hausdorff维数差异,识别混有随机噪声的IMF分量,对该分量进行相关的阈值滤波处理,从而实现有效信号和随机噪声的有效分离.文中从仿真信号试验出发,到模型地震数据和实际地震数据的测试处理,同时与传统的EMD处理结果相对比.结果表明,本文方法对地震随机噪声的衰减有更佳的压制效果.