低信噪比地震资料FDOC-seislet变换阈值消噪方法研究

地震数据本质上是时变的,不仅有效同相轴表现出确定性信号的时变特征,而且复杂地表和构造条件以及深部探测环境总是引入时变的非平稳随机噪声.标准的频率-空间域预测滤波只适合压制平面波信号假设下的平稳随机噪声,而处理非平稳地震随机噪声时,需要将数据体分割为小窗口进行分析,但效果不够理想,而传统非预测类随机噪声压制方法往往适应性不高,因此开发能够保护地震信号时变特征的随机噪声压制方法具有重要的工业价值.压缩感知是近年出现的一个新的采样理论,通过开发信号的稀疏特性,已经在地震数据处理中的数据插值以及噪声压制中得到了应用.本文系统地分析了压缩感知理论框架下的地震随机噪声压制问题,建立了阈值消噪的数学反演目标函数;针对时变有效信息具有的可压缩性,利用有限差分算法求解炮检距连续方程,构建有限差分炮检距连续预测算子(FDOC),在seislet变换框架下,提出一种新的快速稀疏变换域———FDOC-seislet变换,实现地震数据的高度稀疏表征;结合非平稳随机噪声不可压缩的特征,提出了一种整形迭代消噪方法,该方法是一种广义的迭代收缩阈值(IST)算法,在无法计算稀疏变换伴随算子的条件下,仍然能够对强噪声环境中的时变有效信息进行有效恢复.通过对模型数据和实际数据的处理,验证了FDOC-seislet稀疏变换域随机噪声迭代压制方法能够在保护复杂构造地震波信息的前提下,有效地衰减原始数据中的强振幅随机噪声干扰.     

3D surface‐wave estimation and separation using a closed‐loop approach

Surface waves in seismic data are often dominant in a land or shallow‐water environment. Separating them from primaries is of great importance either for removing them as noise for reservoir imaging and characterization or for extracting them as signal for near‐surface characterization. However, their complex properties make the surface‐wave separation significantly challenging in seismic processing. To address the challenges, we propose a method of three‐dimensional surface‐wave estimation and separation using an iterative closed‐loop approach. The closed loop contains a relatively simple forward model of surface waves and adaptive subtraction of the forward‐modelled surface waves from the observed surface waves, making it possible to evaluate the residual between them. In this approach, the surface‐wave model is parameterized by the frequency‐dependent slowness and source properties for each surface‐wave mode. The optimal parameters are estimated in such a way that the residual is minimized and, consequently, this approach solves the inverse problem. Through real data examples, we demonstrate that the proposed method successfully estimates the surface waves and separates them out from the seismic data. In addition, it is demonstrated that our method can also be applied to undersampled, irregularly sampled, and blended seismic data.     

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.     

The impact of surface‐wave attenuation on 3‐D seismic survey design

Three‐dimensional seismic survey design should provide an acquisition geometry that enables imaging and amplitude‐versus‐offset applications of target reflectors with sufficient data quality under given economical and operational constraints. However, in land or shallow‐water environments, surface waves are often dominant in the seismic data. The effectiveness of surface‐wave separation or attenuation significantly affects the quality of the final result. Therefore, the need for surface‐wave attenuation imposes additional constraints on the acquisition geometry. Recently, we have proposed a method for surface‐wave attenuation that can better deal with aliased seismic data than classic methods such as slowness/velocity‐based filtering. Here, we investigate how surface‐wave attenuation affects the selection of survey parameters and the resulting data quality. To quantify the latter, we introduce a measure that represents the estimated signal‐to‐noise ratio between the desired subsurface signal and the surface waves that are deemed to be noise. In a case study, we applied surface‐wave attenuation and signal‐to‐noise ratio estimation to several data sets with different survey parameters. The spatial sampling intervals of the basic subset are the survey parameters that affect the performance of surface‐wave attenuation methods the most. Finer spatial sampling will reduce aliasing and make surface‐wave attenuation easier, resulting in better data quality until no further improvement is obtained. We observed this behaviour as a main trend that levels off at increasingly denser sampling. With our method, this trend curve lies at a considerably higher signal‐to‐noise ratio than with a classic filtering method. This means that we can obtain a much better data quality for given survey effort or the same data quality as with a conventional method at a lower cost.     

A field experiment of walkaway distributed acoustic sensing vertical seismic profile in a deep and deviated onshore well in Japan using a fibre optic cable deployed inside coiled tubing

A two-dimensional walkaway vertical seismic profiling survey using distributed acoustic sensing was conducted at an onshore site in Japan. The maximum depth and the deviation of the observation well were more than 4,000 m and 81 degrees, respectively. Among the several methods for installing fibre optic cables, we adopted the inside coiled tubing method, in which coiled tubing containing a fibre optic cable is deployed. The signal-to-noise ratio of the raw shot gather was low, possibly due to poor coupling between the fibre optic cable and the subsurface formation resulting from the fibre optic cable deployment method and the existence of considerable tubewave noise. Nevertheless, direct P-wave arrivals, P–P reflections and P–S converted waves exhibited acceptable signal-to-noise ratios after careful optimization of gauge length for distributed acoustic sensing optical processing and the application of carefully parameterized tubewave noise suppression. One of the challenges in current distributed acoustic sensing vertical seismic profile data processing is the separation of P- and S-waves using only one-component measurements. Hence, we applied moveout correction using two-dimensional ray tracing. This process effectively highlights only reflected P-waves, which are used in subsequent subsurface imaging. Comparison with synthetic well seismograms and two-dimensional surface seismic data confirms that the final imaging result has a sufficiently high quality for subsurface monitoring. We acquired distributed acoustic sensing vertical seismic profile data under both flowing conditions and closed conditions, in which the well was shut off and no fluid flow was allowed. The two imaging results are comparable and suggest the possibility of subsurface imaging and time-lapse monitoring using data acquired under flowing conditions. The results of this study suggest that, by adopting the inside coiled tubing method without drilling a new observation well, more affordable distributed acoustic sensing vertical seismic profile monitoring can be achieved in fields such as CO₂ capture and storage and unconventional shale projects, where monitoring costs have to be minimized.     

Retrieval of local surface wave velocities from traffic noise – an example from the La Barge basin (Wyoming)

In regions where active source seismic exploration is constrained by limitations of energy penetration and recovery, cost and logistical concerns, or regulatory restrictions, analysis of natural source seismic data may provide an alternative. In this study, we investigate the feasibility of using locally‐generated seismic noise in the 2–6 Hz band to obtain a subsurface model via interferometric analysis. We apply this technique to three‐component data recorded during the La Barge Passive Seismic Experiment, a local deployment in south‐western Wyoming that recorded continuous seismic data between November 2008 and June 2009. We find traffic noise from a nearby state road to be the dominant source of surface waves recorded on the array and observe surface wave arrivals associated with this source up to distances of 5 kms. The orientation of the road with respect to the deployment ensures a large number of stationary points, leading to clear observations on both in‐line and cross‐line virtual source‐receiver pairs. This results in a large number of usable interferograms, which in turn enables the application of standard active source processing methods like signal processing, common offset stacking and traveltime inversion. We investigate the dependency of the interferograms on the amount of data, on a range of processing parameters and on the choice of the interferometry algorithm. The obtained interferograms exhibit a high signal‐to‐noise ratio on all three components. Rotation of the horizontal components to the radial/transverse direction facilitates the separation of Rayleigh and Love waves. Though the narrow frequency spectrum of the surface waves prevents the inversion for depth‐dependent shear‐wave velocities, we are able to map the arrival times of the surface waves to laterally varying group and phase velocities for both Rayleigh and Love waves. Our results correlate well with the known geological structure. We outline a scheme for obtaining localized surface wave velocities from local noise sources and show how the processing of passive data benefits from a combination with well‐established exploration seismology methods. We highlight the differences with interferometry applied to crustal scale data and conclude with recommendations for similar deployments.     

SVD与EMD联合去噪方法在地震勘探数据处理中的研究与应用

将基于倾角扫描的奇异值分解与经验模式分解法相结合应用到地震资料随机噪声压制中。首先利用经验模式分解法消除部分噪声,增强地震道有效信号的相关性,再利用奇异值分解对地震信号进行同相轴自动追踪,截取小时窗数据体,并进行同相轴拉平处理,经SVD计算小时窗数据中心点的值来代替计算样点的值,最终实现随机噪声的压制。理论模型试算和实际资料处理表明,本文提出的EMD-SVD方法简单易行,比单一的SVD方法去噪效果更显著有效地消除了地震资料中的随机噪声,提高了地震资料的信噪比,并改善了叠加剖面的质量。     

高铁地震数据干涉成像技术初探

高铁运行会引起铁轨的震动,从而产生地震波向地下介质中传播,通过研究该地震波可对高铁沿线的地质情况进行持续监测.与常规地震勘探中的震源相比,高铁地震中的震源较为复杂,为移动震源,而地震干涉技术可以通过地震记录间的相互干涉,消除震源的影响,因此可利用地震干涉技术对高铁地震信号进行处理并成像.本文通过分析研究,总结出地震干涉方法在处理高铁地震数据时的关键技术问题:不同于常规地震干涉中先干涉后叠加的干涉成像方式,高铁地震移动源的特点使得干涉顺序变为先叠加后干涉,由此带入了大量震源串扰噪声;初步提出两种解决高铁地震干涉成像的思路:通过对高铁地震信号的处理,使高铁变相"提速"或"降速",给出了"提速"或"降速"后各自的成像思路,并给出了数据处理的技术设想.     

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.     

高铁震源地震信号的稀疏化建模

我国每天有数千趟高铁列车运行在两万多公里的高铁线路上,不但会引起高铁路基的振动,还会激发出地震波.地震检波器所接收到的数据中不仅包含窄带分立谱特性的高铁震源地震信号,还包含宽频带特性的背景信号.如何实现从检波器所接收到的高铁震源地震数据中分离出高铁震源地震信号和宽频带背景信号是准确利用该类信号的关键.考虑到高铁震源地震信号与宽频带信号在频率域明显的形态特征差异,本文首次将形态成分分析这种信号分离手段引入到高铁震源地震信号处理中,实现高铁震源地震信号的稀疏化建模并进而实现从接收数据中分离出高铁震源地震信号以及宽频带背景信号.对北京大学在中国南方某高铁沿线采集到的大量高铁震源地震数据进行处理,结果表明:采用形态成分分析并结合分块坐标松弛算法,能够实现实际采集高铁震源地震数据中的高铁震源地震信号和宽频带信号的分离.     

压制空间非平稳地震勘探随机噪声的ROAD径向时频峰值滤波算法

径向时频峰值滤波算法是一种有效保持低信噪比地震勘探记录中反射同相轴的随机噪声压制方法,但该算法对空间非平稳地震勘探随机噪声压制效果不理想.本文研究空间非平稳地震勘探随机噪声,即各道噪声功率不同的地震勘探随机噪声,其在径向滤波轨线上表征近似脉冲噪声,在径向时频峰值滤波过程中干扰相邻道滤波结果.为了减小空间非平稳随机噪声的影响,本文提出一种基于绝对级差统计量(ROAD)的径向时频峰值滤波随机噪声压制方法.该方法首先根据径向轨线上信号的绝对级差统计量检测空间非平稳地震勘探随机噪声,然后结合局部时频峰值滤波和径向时频峰值滤波压制地震勘探记录中的随机噪声.将ROAD径向时频峰值滤波方法应用于合成记录和实际共炮点地震记录,结果表明ROAD径向时频峰值滤波方法可以压制空间非平稳地震勘探随机噪声且不损害有效信号,有效抑制随机噪声空间非平稳对滤波结果的影响.与径向时频峰值滤波相比,ROAD径向时频峰值滤波方法更适用于空间非平稳地震勘探随机噪声压制.     

Incorporation of a Non-linear Image Filtering Technique for Noise Reduction in Seismic Data

Seismic noise is a fundamental part of seismic data which cannot be avoided when conducting any seismic survey. It consists of coherent and random noise. Noise removal or filtering is one of the major concerns in the field of seismic processing. In this paper, we introduce an image filtering technique based on a detection-estimation algorithm for Gaussian and random noise removal in seismic data, namely the trilateral filter, based on a statistic called rank-ordered absolute differences. The non-linear and adaptive behaviour of this filter makes it very robust in the presence of random and coherent noise, in addition to its computational simplicity and its ability to automatically identify noise in data. We have modified the strategy of trilateral filtering by adapting the rank-ordered absolute differences formula in order to extract the signal component. We have successfully used this filter for the removal of surface waves and random spiky noise from synthetic and field data. Results are very encouraging and show the superiority of this filter compared with other filters, particularly when used recursively.     

基于Duffing振子混沌系统的地震速度分析方法

强随机噪声干扰是导致地震勘探资料低信噪比的主要原因,如何在强随机噪声干扰下获取有效的信息是值得关注的问题.Duffing振子混沌系统是一个非线性的动力学系统,其对强随机噪声具有免疫能力,而对特定的周期性信号具有敏感性.本文提出一种基于Duffing振子混沌系统的速度分析方法.对CMP道集按照时距曲线关系进行移动窗口截取,将所截取的信号构建为待测信号加入Duffing振子混沌系统,通过相图网格分割方法(GPM)判断系统状态的改变,从而在强随机噪声背景下获得高分辨率的速度谱.理论模型和实际资料的处理结果表明,与传统的水平叠加速度分析方法相比,本方法能够在强随机噪声背景下获得更准确的速度分析结果.     

一种神经网络改进小波的地震数据随机噪声去除方法

地震资料的有效信号反射弱,且易受多次波的影响,不可避免地存在随机噪声干扰。提出一种基于神经网络改进小波的地震数据随机噪声去除方法,采用神经网络模型,识别出随机噪声信号,对该信号进行小波包分解,获取多类别随机噪声信号,采用级联BP神经网络模型提取出多类别随机噪声信号,实现地震数据的随机信号压制。实验结果显示,这种改进小波方法对地震数据随机噪声信号的去噪效果较好,在复杂沉积地质结构被探测介质的地震数据随机噪声压制方面具有较强的适用性。     

基于空间相关的地震前兆数据库的信息提取与数据更新方法

针对当前提取地震前兆数据易受到噪声干扰,且数据库中数据更新速度较慢的问题,提出基于空间相关的地震前兆数据库信息提取与数据更新方法。利用快速Myriad滤波器,引入滑动窗,选择窗口数据参与到计算中,将计算结果当作目前窗口滤波输出值,实现数据滤波,由此实现信息提取的去噪处理。依据初步滤波结果,将当前数据作为中心,并确定空间窗,在横向上进行相关数据统计。针对空间窗中各数据选取滑动时窗,并对其中的数据进行S变换,利用指数拟合的数据传输能力参数,获取缺失数据的填充修复参数。引入曲面加权函数对填充修复参数进行平滑,根据平滑之后的填充修复参数对S变换数据进行更新,实现空间相关下地震前兆数据库的数据更新。实验结果表明,所提方法的数据信噪比较高,数据更新时间较短。     

基于波动方程的叠前地震反演

随着地震勘探和开发的不断深入,面向地质目标的精细储层预测技术变得越来越重要.由于透射损失、层间多次波、波模式转换以及随机噪声等的影响,观测地震数据和待反演的地下介质属性之间呈现出很强的非线性.考虑到这些非线性,本文基于积分波动方程开展叠前地震反演,从观测地震数据中恢复出介质属性和整体波场,其中反演参数是波动方程中的压缩系数、剪切柔度和密度的对比度,相比于常规线性AVO反演的波阻抗弹性参数,它们对流体指示有更强的敏感性.在反演过程中,从平滑的低频背景场出发,交替迭代求解数据方程和目标方程.采用乘性正则化方法于共轭梯度框架下求解反演参数,采用优化的散射级数Neumann序列获得整体波场,这种方法不易陷入局部极值,能收敛到正确解.测井资料和典型山前带模型测试表明,利用上述反演方法能获得高分辨率的深度域地下介质属性,可直接进行储层预测和解释.     

Coherence between the fields of low-frequency seismic noise in Japan and California

The relationship is considered between the statistics of the field of low-frequency seismic noise which was synchronously recorded by two broadband seismic networks in Japan (78 stations) and California (81 stations). The analysis is based on the data for seven years of observations (2008–2014). For each network, the daily time series of the median values are constructed for five parameters of seismic noise: kurtosis (excess), minimal normalized entropy of the distribution of the squared wavelet coefficients, generalized Hurst exponent, support width of the singularity spectrum, and index of linear predictability. The median values for each parameter were calculated on a daily basis over all the stations of the networks and resulted in a time series containing 2557 data points of the integral characteristics of the noise with a daily time step. The use of the median values of the noise parameters avoids considering the effects of the gaps in recording by individual stations and provides the continuous time series as the integral characteristic of the whole network. Next, for each network, the aggregate signals were calculated for the obtained five-variate time series. By construction, the aggregate signal is a scalar signal which maximally accumulates the most general variations that are simultaneously present in all the analyzed signals and, at the same time, rejects the components that are only characteristic of a single process. The final step of the analysis consists in estimating the evolution of the quadratic spectrum in the moving time window with a length of one year. It is shown that during the considered interval of the observations, the coherence is characterized by the increasing linear trend, which independently supports the previous conclusion about the enhancement of the synchronization between the parameters of the global seismic noise.     

Mapping the properties of low-frequency microseisms for seismic hazard assessment

The structure of low-frequency seismic noise in the range of periods from 2 min to 500 min is studied from the data of continuous seismic monitoring at 77 seismic stations of the F-net broadband network in Japan from the beginning of 1997 to May 15, 2012. A new statistical characteristic of seismic noise is suggested, namely, the minimal normalized entropy En of the distribution of squared orthogonal wavelet coefficients. This parameter of seismic noise is analyzed in conjunction with the multifractal statistics—the support width of the singularity spectrum, Δα, and the generalized Hurst exponent, α*, which were extensively used by the author in the previous works for analyzing the low-frequency seismic noise. The method for constructing the maps of spatial distribution of Δα, α*, En, and their aggregated normalized value over the time windows with a given length is proposed. The maps are constructed by averaging the succession of the elementary charts, each of which corresponds to a day of observations. It is shown that, for the islands of Japan, the reduction in Δα and α* and the increase in En outline the area of the forthcoming mega earthquake of March 11, 2011, with M = 9 (Tohoku earthquake). According to the analysis of about a year’s worth of data after this event, the region south of Tokyo (Nankai trough) is still dominated by decreased Δα and α* and increased En. This gives grounds to hypothesize that this region remains at a high level of seismic threat since the accumulated stresses were incompletely released by the Tohoku earthquake. Drawing an analogy to the behavior of the coefficient of correlation between Δα and α*, we may suppose that there is an increased probability of a strong earthquake occurring in the second half of 2013 or the first half of 2014. Constructing the averaged maps of the distributions of seismic noise parameters and their aggregated value in a moving time window is suggested as a new method for dynamical assessment of seismic hazards.     

An automated cross‐correlation based event detection technique and its application to a surface passive data set

In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running‐window energy ratio of the short‐term average to the long‐term average of the passive seismic data for each trace. We show that for the common case of a low signal‐to‐noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross‐correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal‐to‐noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.