Applications of Savitzky-Golay Filter for Seismic Random Noise Reduction

This article utilizes Savitzky–Golay (SG) filter to eliminate seismic random noise. This is a novel method for seismic random noise reduction in which SG filter adopts piecewise weighted polynomial via leastsquares estimation. Therefore, effective smoothing is achieved in extracting the original signal from noise environment while retaining the shape of the signal as close as possible to the original one. Although there are lots of classical methods such as Wiener filtering and wavelet denoising applied to eliminate seismic random noise, the SG filter outperforms them in approximating the true signal. SG filter will obtain a good tradeoff in waveform smoothing and valid signal preservation under suitable conditions. These are the appropriate window size and the polynomial degree. Through examples from synthetic seismic signals and field seismic data, we demonstrate the good performance of SG filter by comparing it with the Wiener filtering and wavelet denoising methods.     

一种低成本无缆地震仪采集站的研制

随着电子技术的发展,许多新技术被应用到地球物理仪器之中.其中,无缆地震仪采集站具有重量轻、易搬运的特点,适宜应用于如森林、沼泽、沙漠等地面状况复杂的区域,可以在复杂地质条件下方便地进行地震数据采集,是未来地震仪发展的方向.本研究研发了一种低成本无缆地震仪采集站,通过分析无缆地震仪的发展方向,确定了采集站的设计方案,该系统采用了微功耗设计,GPS授时同步与低成本晶振相结合的时钟机制,并设计了基于多项分解的软件滤波器提高24位Σ-ΔADC的动态范围,其低成本、小体积、低功耗的特点使其非常适用于复杂区域下的资源勘探,并可应用于高密度、宽方位等物探新技术当中.     

时频域相位滤波在远震接收函数噪声压制中的应用

宽频带地震观测数据中有效信号和干扰噪声经常发生混频效应,常规的频率域滤波方法很难将二者分离.地震波信号属于时变非平稳信号,时频分析方法能够同时得到地震波信号随着时间和频率变化的振幅和相位特征,S变换是其中较为高效的时频分析工具之一.本文以S变换为例,提出了基于相位叠加的时频域相位滤波方法.与传统叠加方法相比,相位叠加方法对强振幅不敏感,对波形一致性相当敏感,更加利于有效弱信号信息的检测.时频域相位滤波方法滤除与有效信号不相干的背景噪声,保留了相位一致的有效信号成分,显著提高了信噪比.运用理论合成的远震接收函数数据和实际的宽频带地震观测数据检验结果显示该方法较传统的带通滤波方法相比,即使在信噪较低且混频严重条件下,时频域相位滤波方法的滤波效果依然很明显,有助于识别能量较弱的有效信号.     

《群集放大、鉴频和锁笔电路》的研制

作者对地震遥测传输系统中收讯群放和鉴频器进行了改进，选用现代集成电路和现代技术，设计出了群放、鉴频、锁笔、滤波和限幅电路为一体的实用电路，在本文中介绍了该电路设计指标和工作过程，以供技术人员参考和交流     

基于SW统计量的自适应时频峰值滤波压制地震勘探随机噪声研究

由于金属矿区地震记录中随机噪声性质复杂且信噪比低,常规降噪方法难以达到预期的滤波效果.时频峰值滤波(TFPF)方法是实现低信噪比地震勘探记录中随机噪声压制的有效方法,但其在复杂地震勘探随机噪声下时窗参数优化问题仍难以解决.本文充分利用地震勘探噪声的统计特性,结合Shapiro-Wilk(SW)统计量辨识地震勘探记录中的微弱有效信号,提出基于SW统计量的自适应时频峰值滤波降噪方法(S-TFPF).在S-TFPF方案中,对于有效信号集中区,S-TFPF方法根据信号频率特征,选择有利于信号保持的较短时窗长度;对于噪声集中区,按噪声方差自适应增加时窗长度,增强随机噪声压制能力.S-TFPF应用于合成记录和共炮点记录的滤波结果表明,与传统时频峰值滤波方法相比,S-TFPF方法可以有效抑制低信噪比地震勘探记录中的随机噪声,更好地恢复出同相轴.     

基于结构自适应中值滤波器的随机噪声衰减方法

本文提出一种保护断层、裂缝等地层边缘特征的结构自适应中值滤波器,用于衰减地震资料中的随机噪声.基于地震反射同相轴局部呈线型结构的假设,采用梯度结构张量估计地层倾向,分析地层结构的规则程度,在此基础上引入地震剖面中线型和横向不连续性两种结构特征的置信度量.结构自适应中值滤波器根据这两种置信度量调整滤波器窗函数的尺度和形状,根据地层倾角调整滤波器窗函数的方向,从而使得滤波操作窗能够最佳匹配信号的局部结构特征.将本文方法用于合成和实际数据的处理,并与两种常用中值滤波方法进行对比,结果表明,该方法能够更好地解决地震剖面的随机噪声衰减和有效信号保真的问题,在增强反射同相轴的横向一致性的同时有效保持了剖面内的地层边缘和细节特征,显著改善了地震资料的品质.     

Enhancement of Deep Seismic Reflections in Pre-stack Data by Adaptive Filtering

—Adaptive filters offer advantages over Wiener filters for time-varying processes. They are used for deconvolution of seismic data which exhibit non-stationary behavior, and seldom for noise reduction. Different algorithms for adaptive filtering exist. The least-mean-squares (LMS) algorithm, because of its simplicity, has been widely applied to data from different fields that fall outside geophysics. The application of the LMS algorithm to improve the signal-to-noise ratio in deep reflection seismic pre-stack data is studied in this paper. Synthetic data models and field data from the DEKORP project are used to this end.¶Three adaptive filter techniques, one-trace technique, two-trace technique and time-slice technique, are examined closely to establish the merits and demerits of each technique. The one-trace technique does not improve the signal-to-noise ratio in deep reflection seismic data where signal and noise cover the same frequency range. With the two-trace technique, the strongest noise reduction is achieved for small noise on the data. The filter efficiency decreases rapidly with increasing noise. Furthermore, the filter performance is poor upon application to common-midpoint (CMP) gathers with no normal-moveout (NMO) corrections. Application of the two-trace method to seismic traces before dynamic correction results in gaps in the signal along the reflection hyperbolas. The time-slice technique, introduced in this paper, offers the best answer. In this case, the one-trace technique is applied to the NMO-corrected gathers across all traces in each gather at each time to separate the low-wavenumber component of the signal in offset direction from the high-wavenumber noise component. The stacking velocities used for the dynamic correction do not need to be known very accurately because in deep reflection seismics, residual moveouts are small and have only a minor influence on the results of the adaptive time-slice technique. Noise reduction is more significant with the time-slice technique than with the two-trace technique. The superiority of the adaptive time-slice technique is demonstrated with the DEKORP data.     

Dispersion calculation method based on S-transform and coordinate rotation for Love channel waves with two components

Dispersion analysis is an important part of in-seam seismic data processing, and the calculation accuracy of the dispersion curve directly influences pickup errors of channel wave travel time. To extract an accurate channel wave dispersion curve from in-seam seismic two-component signals, we proposed a time–frequency analysis method based on single-trace signal processing; in addition, we formulated a dispersion calculation equation, based on S-transform, with a freely adjusted filter window width. To unify the azimuth of seismic wave propagation received by a two-component geophone, the original in-seam seismic data undergoes coordinate rotation. The rotation angle can be calculated based on P-wave characteristics, with high energy in the wave propagation direction and weak energy in the vertical direction. With this angle acquisition, a two-component signal can be converted to horizontal and vertical directions. Because Love channel waves have a particle vibration track perpendicular to the wave propagation direction, the signal in the horizontal and vertical directions is mainly Love channel waves. More accurate dispersion characters of Love channel waves can be extracted after the coordinate rotation of two-component signals.     

L1 norm optimal solution match processing in the wavelet domain

Greater attention has been paid to vintage-merge processing of seismic data and extracting more valuable information by the geophysicist. A match filter is used within many important areas such as splicing seismic data, matching seismic data with different ages and sources, 4-D seismic monitoring, and so on. The traditional match filtering method is subject to many restrictions and is usually difficult to overcome the impact of noise. Based on the traditional match filter, we propose the wavelet domain L1 norm optimal matching filter. In this paper, two different types of seismic data are decomposed to the wavelet domain, different detailed effective information is extracted for L1-norm optimal matching, and ideal results are achieved. Based on the model test, we find that the L1 norm optimal matching filter attenuates the noise and the waveform, amplitude, and phase coherence of result signals are better than the conventional method. The field data test shows that, with our method, the seismic events in the filter results have better continuity which achieves the high precision seismic match requirements.     

Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time‐delayed arrival from the event, we propose an autocorrelation‐based stacking method that designs a denoising filter from all the traces, as well as a multi‐channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace's autocorrelation is centred at zero in the lag domain. The effect of white noise is concentrated near zero lag; thus, the filter design requires a predictable adjustment of the zero‐lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with coloured noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.     

用多通道AD分级采集扩展地震数据采集器的动态范围

地震数据采集是地震信号数字化必不可少的环节,动态范围是其一个重要的性能指标.实际地震信号的动态范围在160dB以上,而目前普遍使用的24位地震数据采集器动态范围相对较小且在50 Hz采样率时最大只达到135dB,致使24位地震数据采集器在实际使用中对小信号分辨率不够,不能有效提取地震信息;在大地震时又容易使数据采集器出现饱和限幅失真的现象而失去地震监测记录功能.本文针对在地震监测和地震研究中需要具有高分辨率和高动态范围的地震数据采集器这个亟待解决的问题,提出一种采用多通道AD转换器并行分级采集的方法,讨论了通道间失配及其标定.对研制实验样机的测试表明,其动态范围在50Hz采样时可以达到157dB以上,线性度优于0.005%.     

S变换在面波去噪中的应用r

S变换是一种用于分析非平稳信号的时频变换方法, 可以很好地刻画地震信号的时频特性. 本文将S变换用于地震面波数据的噪声去除中, 首先介绍了S变换的理论基础, 然后设计了时频滤波和阈值滤波两种方法, 分别对天然地震面波数据和背景噪声数据进行去噪处理, 并与相位匹配滤波进行了比较． 结果表明, 面波数据经S变换去噪后, 群速度频散曲线的短周期部分得到改善, 能够连续追踪至6 s左右, 但长周期部分出现了缺失; S变换去噪的效果优于相位匹配滤波, 两者相结合会得到更加理想的结果．      

Inversion-based data-driven time-space domain random noise attenuation method

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.     

Measuring accelerometer dynamic range from seismic data using Allan deviation

We propose a new approach for calculating the dynamic range of an accelerometer based on an Allan deviation analysis of production seismic data. This test is intended as a field audit technique and does not require an unconditioned dataset from a low‐noise environment. We first show that Allan deviation can measure white noise levels using two commercial accelerometers. The analysis accurately captures the manufacturing noise density specifications and known relationships between white noise, preamplifier gain, and group forming. We then show that a production seismic dataset is suitable for an Allan deviation analysis because the results are not critically affected by a recording filter. Finally, we illustrate the proposed technique by calculating the dynamic range of an accelerometer channel in a seismic streamer using a production dataset.     

背景噪声提取体波方法研究进展

地球深部结构探测是地球物理学的核心领域,而地震体波可以深入地球内部且分辨率较高,是研究地球内部结构不可或缺的技术手段。基于背景噪声提取高信噪比体波信号技术的迅速发展,极大地促进了地震学的发展和应用范围,使其在地球深部结构成像、城市浅层空间探测等领域日益发挥出重要作用。本文详细综述了如何利用地震干涉法及台阵处理技术提取出用于研究不同探测尺度（局部、区域、全球）的各类体波信号。其中,地震干涉法通过对地震台站记录到的波形信号进行互相关,抵消掉重合的射线路径,最后得到台站对之间的地震记录;而台阵处理方法是基于接收器台阵发展起来的数据处理手段,该技术不仅能够进一步提高信噪比（SNR）,而且能够获得方位信息。一般来讲,背景噪声中包含的体波信号能量远低于面波信号能量,提取难度大。本文着重介绍了Bin-叠加法、双波束方法（DBF）以及相位加权叠加法（PWS）,并对3种方法的适用条件进行了总结。      

地震记录的广义分维及其应用

根据分形理论,对不同信噪比地震记录的分维特征进行了分析,指出地震记录中噪声背景与信号部分具有不同的分维尺度,地震道时间序列的分维数值与计算时所用的测量尺度有关,因此,可利用广义分维的概念计算地震记录的分数维.地震记录广义分维大大提高了分形算法在计算机自动识别地震波震相时的抗噪声能力.最后用本文方法对实际地震记录进行了有效的初至波自动拾取.     

EFFICIENT MULTICHANNEL FILTERING OF SEISMIC DATA1

Multichannel filters are used to eliminate coherent noise from surface seismic data, for wavefield separation from VSP stacks, and for signal enhancement. Their success generally depends on the choice of the filter parameters and the domain of application. Multichannel filters can be applied to shots (monitors), common-receiver traces, CDP traces and stacked sections. Cascaded applications in these domains are currently performed in the seismic industry for better noise suppression and for signal enhancement. One-step shot-domain filtering is adequate for some applications. However, in practice, cascaded applications in shot-and common-receiver domains usually give better results when the S/N ratio is low. Multichannel filtering after stacking (especially after repeated applications in shot and/or receiver domains) may create undesirable results such as artificial continuations, or smearing and smoothing of small features such as small throw faults and fine stratigraphic details. Consequently, multichannel filtering after stacking must be undertaken with the utmost care and occasionally only as a last resort. Multichannel filters with fan-shaped responses (linear moveout filters) should be applied after NMO correction. These are the filters commonly used in the seismic industry where they have such names as velocity filters, moveout filters, f-k filters and coherency filters. Filtering before NMO correction may result in break-up and flattening especially of those shallow reflection events with relatively higher curvatures and diffractions. NMO correction is needed prior to wavefield separation from VSP stacks for the same practical reasons outlined above whenever source-receiver offsets are involved. Creation of artificial lineup and smearing at the outputs of multichannel filters is presently the common practical concern. Optimum multichannel filters with well-defined pass, reject and transition bands overcome the latter problems when applied before stacking and after NMO correction. The trace dimension of these filters must be kept small to avoid such lineups and the smoothing of small structures. Good results can be obtained with only five traces, but seven traces seems to be a better compromise both in surface and well seismic applications. The so-called f-k filtering and τ-p domain filtering are no exceptions to the above practical considerations. Residual static computations after multichannel filtering also need special consideration. Since multichannel filtering improves spatial continuity, residual static algorithms using local correlation, i.e. nonsurface-consistent algorithms, may be impractical especially after multichannel filtering.     

基于f-x域流式预测滤波器的地震随机噪声衰减方法

随机噪声的影响在地震勘探中是不可避免的,常规的随机噪声压制方法在处理中往往会破坏具有时空变化特征的非平稳有效地震信号,影响地震数据的准确成像.当前油气勘探的目标已经转变为“两宽一高”,随着数据量的增大,对去噪方法的处理效率也提出了更高的要求.因此,开发高效的非平稳地震数据随机噪声压制方法具有重要意义.预测滤波技术广泛用于地震随机噪声的衰减,本文基于流式处理框架提出一种新的f-x域流式预测滤波方法,通过在频率域建立预测自回归方程,运用直接复数矩阵逆运算代替迭代算法求解非平稳滤波器系数,实现时空变地震同相轴预测,提高自适应预测滤波的计算效率.通过与工业标准的FXDECON方法和f-x域正则化非平稳自回归(RNA)方法进行对比,理论模型和实际数据的测试结果表明,提出的f-x域流式预测滤波方法能更好地平衡时空变有效信号保护、随机噪声压制和高效计算三者之间的关系,获得合理的处理效果.     

基于高通滤波的频率-空间域经验模态分解压制高频噪声

高频噪声压制是高分辨率地震数据处理中提高信噪比的关键性问题.本文针对f-x(频率-空间)反褶积空间预测滤波器无法处理非平稳、非线性信号的缺点,提出了一种基于高通滤波的频率-空间域经验模态分解(Empirical Mode Decomposition in the frequency-space domain,f-xEMD)压制地震剖面中高频噪声的方法.该方法采用全域高通滤波从原始数据中分离出含有部分有效信号的高频数据,将其变换到f-x域,然后在滑动的短窗口内提取每一个频率的空变数据序列进行EMD分解得到高频复本征模态函数(Intrinsic Mode Function,IMF)IMF1,将所有频率的IMF1序列反Fourier变换到时间域得到噪声剖面,将其与原始数据相减,达到高频噪声压制的目的.该方法可克服传统EMD分解方法中的模态混叠现象,保护陡倾角反射同相轴;压制后的噪声剖面中不包含有效信号能量,地震剖面的信噪比得到了提高.模拟数据和实际数据处理结果充分证明了该方法的有效性.     

Reducing wind noise in seismic data using Non-negative Matrix Factorization: an application to Villarrica volcano,Chile

Single channel source separation of seismic signals is an appealing but difficult problem. In this paper, we introduce a semi-blind single-channel seismic source separation method to enhance the components of volcanic origin. In this method, the source decomposition scheme is addressed as a Sparse Non-negative Matrix Factorization (NMF) of the time-frequency representation of the single vertical seismic channel. As a case study we present an application using seismic data recorded at Villarrica volcano, Chile, one of the most active in the southern Andes. The analysed dataset is strongly contaminated by wind noise and the procedure is used to separate a component of volcanic origin from another of meteorological origin.