地面微地震监测速度模型优化方法研究

地层速度模型精度对水力压裂微地震事件的定位结果起着关键的作用,常规的速度建模方法是根据声波测井资料建立初始层状速度模型,根据拾取的射孔信号初至通过反演进行速度模型优化,结果受射孔信号信噪比及初至拾取误差的影响较大,不适用于地面微地震监测.为了提高地面微地震速度模型的精度,提出了将多道射孔信号的叠加能量作为目标函数,基于粒子群优化算法的地面微地震速度模型优化方法,并通过模型数据和实际数据的处理进行了验证,结果表明该方法避免了无法拾取初至或初至拾取不准对速度模型优化结果的影响,有效实现了速度模型的优化,明显提高了地面微地震监测的定位精度.     

基于波形相似性的远震初至拾取方法进展与对比研究

远震初至拾取是地震学研究中的关键环节,它在地震定位、核爆炸检测以及远震层析成像中起着重要的作用.基于波形相似性的远震初至拾取方法利用互相关估计各地震道的相对时差,具有抗噪和拾取精度高的特点.目前流行的各种基于波形相似性的远震初至拾取方法的原理各不相同,在时窗参数设定、稳定性和计算效率等方面存在差异,有必要对不同方法进行总结,并对其精度和效率进行评价.本文针对多道互相关方法、迭代互相关叠加方法、自适应叠加方法、台网远震体波处理方法、改进多道互相关方法等五种基于波形相似性的初至拾取典型方法的特点,对比分析了各方法的精度、稳定性及其与时窗参数的关系.实际数据的应用结果显示:在相同数据条件下,改进多道互相关方法精度最高,台网远震体波处理方法次之,再次是多道互相关方法和迭代互相关叠加方法,自适应叠加方法最低.不同方法的稳定性对互相关时窗长度变化的敏感度不同,时窗过短或者过长均会造成拾取精度和稳定性的降低.当初至震相信号的时窗长度取主周期的1~2倍时,互相关时窗恰好覆盖初至震相起跳部分的波形,各种方法拾取初至的精度接近.在实际应用中,建议根据不同的震源子波类型、频率成分、噪声水平等采用不同的初至拾取策略以提高拾取的精度和稳定性.     

于多道包络能量叠加的微地震事件自动识别方法

地面监测的微地震数据主要包含两个部分:人工形成的射孔数据和压力诱发的微地震数据.从微地震监测理论出发,微地震事件准确识别直接影响微地震信号初至拾取的精度.针对常规微地震信号初至拾取方法存在漏检及误检的弊端,提出了多道包络能量叠加技术的微地震事件自动识别方法.针对存在射孔数据的情况,详细论述了多道包络能量叠加技术的微地震事件自动识别方法的过程,采用模拟数据证明了基于射孔信号的微地震事件自动识别方法的可靠性.其次,采用一系列微地震正演模拟数据进行大量实验,验证基于多道包络能量叠加技术的微地震事件自动识别方法的可靠性.最后,通过实际微震压裂数据验证了新方法的可行性与正确性.     

一种改进的基于网格搜索的微地震震源定位方法

震源定位是微地震监测技术要解决的主要问题.目前,井下微地震监测多采用走时拟合法计算震源位置.常规方法受到环境噪声、初至拾取误差、速度模型误差等因素的影响,定位结果存在一定误差.为了提高定位精度,本文提出了一种改进的基于网格搜索的微地震震源定位方法.本文方法根据P波的偏振特征参数计算概率密度函数求取震源方位角,并采用改进的目标函数和搜索算法计算震源的径向距离和深度.模型数据和实际资料的处理结果表明,本文方法具有较强的抗噪性,计算得到的震源方位角更加接近真实值;与常规目标函数相比,本文方法采用的目标函数具有更好的收敛性,其定位结果受初至拾取误差和速度模型误差的影响更小;本文提出的搜索算法能够消除由于错误拾取造成的观测到时中的异常值对定位结果的影响.     

低信噪比浅水区OBS测线初至拾取研究

浅水区OBS(Ocean Bottom Seismograph)测线往往具有信噪比较低,空间假频发育严重的问题,导致OBS测线初至拾取困难,进而影响反演速度模型的可靠性.本文通过直达波校正,正演模拟校正和射线互逆实现对浅水区OBS测线初至的准确拾取和质控.直达波校正消除主要的时钟漂移量.通过多道地震与OBS的联合处理,充分利用多道地震浅层速度模型较准确的特点,通过正演模拟构建OBS道集,弥补了实际采集的OBS道集浅层空间假频严重的问题,正演模拟法基本消除了剩余时钟漂移量,校正后的OBS道集符合层析成像的要求,同时也解决了近偏移距初至拾取的问题.利用射线互逆的方法,通过不同OBS道集组合不同偏移距组的对比验证,解决了中远偏移距初至拾取对比验证问题.三步法基本解决低信噪比浅水区OBS测线初至拾取的问题,为速度反演获得准确的模型奠定了基础.     

样条曲线拟合初至波剩余静校正方法

针对复杂探区因模型道品质差导致反射波剩余静校正效果不好的难题,本文提出了一种基于样条曲线拟合的二维初至波剩余静校正方法.该方法相对直线拟合法,可以用的初至信息更多,统计的短波长静校正量更为准确;相对折线拟合法,它不用拾取折射拐点,操作方便快捷,更具有可操作性.它采用三次样条函数将应用了长波长静校正量的初至拟合成一条光滑曲线,根据地表一致性原则将延迟时分解为炮点、检波点短波长静校正量.实际资料应用结果表明,基于样条曲线拟合的单炮初至变得光滑,叠加剖面同相轴连续性得到增强,可为反射波剩余静校正提供较好品质的模型道,对低信噪比资料剩余静校正问题的解决具有实际意义.     

基于U-Net的井中多道联合微地震震相识别和初至拾取方法

微地震震相识别和初至拾取是水力压裂微地震监测资料处理中的两个关键步骤,其结果会对后续事件定位和压裂裂缝缝网解释产生重要影响.常规方法如STA/LTA法、模板匹配法、多道互相关法等需要提取有效信号与噪声间振幅、偏振、频率、波形相似性等方面的特征差异完成震相识别和拾取工作.本文基于深度学习技术的自动特征提取能力,根据井中微地震观测系统的多道数据源特点,提出基于U-Net的多道联合震相识别和初至拾取方法(MT-Net).方法采用具有"逐采样点"识别能力的U-Net模型,模型训练阶段以具有不同信号特征的多道微地震监测记录作为输入,以P波、S波及噪声的概率分布标签作为输出,通过设置二维卷积操作使得道内与道间的波形信息同时被自适应地学习,以满足对相邻道间波形记录处理结果高度一致性的要求;测试阶段将连续记录中的分段波形馈入模型,通过设定P波、S波概率分布曲线阈值完成单震相、双震相和噪声的波形分类,同时对含有效震相的微地震事件完成初至拾取.实际微地震资料处理结果显示,本文方法与同样基于U-Net的单道方法(ST-Net)相比,显著降低了震相识别中低信噪比事件漏拾与误拾发生的概率;同时有效避免了部分单道发生严重的初至拾取结果偏差及P、S震相误拾等情况.本文方法的识别与拾取结果整体上达到了与多道互相关法接近的水平,可满足微地震监测资料处理中实时性和准确性的要求.     

基于瞬时相位的微地震干涉定位方法研究

波形叠加定位法具有自动性和抗噪性等优点,已被广泛应用于微地震事件定位.当该类方法采用特征函数变换原始波形以克服初至极性变化影响时,会降低成像分辨率.而将相位加权叠加法应用于微地震成像时,虽然提高了成像分辨率,但并未考虑复杂震源机制对定位的影响.为了校正波形极性,并提高干涉成像法的分辨率和压制噪声的能力,本文重新组合了原...     

初至叠加法在宣汉地区转换波静校正中的应用

由于转换波记录静校正量大、信噪比一般较低,利用反射波进行静校正往往不易取得满意的结果.由于转换波自身特点,转换波初至的识别和拾取一直是一个难题.论文通过推导共检波点道集中初至的时距曲线公式得到了共检波点道集中P-P-P波初至和P-P-SV波初至在一定偏移据范围内平行的结论.根据这一结论,提出了共检波点域初至波叠加法确定转换波初至的算法.利用这种算法对宣汉地区的转换波资料进行了转换波初至识别,并拾取了纵波和转换波初至时差,使用延迟时时差法计算静校正量取得了令人满意的效果.     

基于深度学习UNet++网络的初至波拾取方法

初至波拾取是地震资料处理中一项基础而重要的工作.为解决我国西部沙漠、黄土塬、戈壁等地区地震资料信噪比低,致使初至波拾取准确率不高的难题.本文创新提出一种基于图像分割技术——UNet++神经网络应用于初至波智能拾取.输入原始地震数据及少量初至时间的标签数据进行监督学习,并建立UNet++模型,应用西部某工区地震数据测试,实验证明,UNet++模型性能稳定,炸药震源初至波拾取准确率达到98％,可控震源初至波拾取准确率达到98％.此外,本方法与商业软件、U-net网络的初至拾取对比表明,UNet++优势明显,具有准确率高,抗噪能力强,性能稳定、高效等特点.     

微地震事件初至拾取SLPEA算法

微地震事件初至拾取是微地震数据处理的关键步骤之一.实际微地震监测资料中存在大量低信噪比事件,而传统方法对这些事件的应用效果并不理想.为了克服传统方法抗噪性弱的缺点,本文通过综合地震信号与环境噪声在振幅、偏振以及统计特征等方面的存在的差异,设计了一种针对低信噪比微地震事件的初至拾取方法——SLPEA算法.为了检验本文方法的可行性和有效性,分别对模型数据和实际资料进行了处理,并将处理结果与传统方法及手工拾取的结果进行了对比.分析表明,利用本文方法得到的初至到时与手工拾取结果的绝对误差平均值仅为1.33×10~(-3)s,小于3个采样点;方差为3.21×10~(-6)s~2;初至到时在手工拾取结果±0.005s误差范围内的个数占总数的95.8%.这些参数值均优于传统方法的同类参数,证明了本文方法的可靠性.     

地震检测与震相自动拾取研究

针对微震事件易受噪声干扰等特点,本文将STA/LTA方法和基于方差的AIC方法（var-AIC）相结合,在震相到时初步拾取的基础上,使用台站的德洛内（Delaunay）三角剖分及台站间最大走时差约束来减少噪声干扰的影响. 利用到时进行地震定位之后,根据台站预测到时,在设定的时间窗内对地震震相进行更精细的分析. 特别是针对微震事件信噪比低的特点,设计了基于偏振分析的拾取函数,根据窗内STA/LTA方法和var-AIC方法的拾取结果自动选择合适的值作为震相到时. 最后,对西昌流动地震台阵2013年304个单事件波形数据的分析处理和检验结果表明,本文方法较传统方法具有更高的地震事件检测能力和更高的震相拾取精度.      

Determination of a shallow velocity–depth model from seismic refraction data by coherence inversion1

Seismic refractions have different applications in seismic prospecting. The travel- times of refracted waves can be observed as first breaks on shot records and used for field static calculation. A new method for constructing a near-surface model from refraction events is described. It does not require event picking on prestack records and is not based on any approximation of arrival times. It consists of the maximization of the semblance coherence measure computed using shot gathers in a time window along refraction traveltimes. Time curves are generated by ray tracing through the model. The initial model for the inversion was constructed by the intercept-time method. Apparent velocities and intercept times were taken from a refraction stacked section. Such a section can be obtained by appling linea moveout corrections to common-shot records. The technique is tested successfully on synthetic and real data. An important application of the proposed method for solving the statics problem is demonstrated.     

Constant normal-moveout (CNMO) correction: a technique and test results

We introduce a processing technique which minimizes the 'stretching effects' of conventional NMO correction. Unlike conventional NMO, the technique implies constant normal moveout (CNMO) for a finite time interval of a seismic trace. The benefits of the proposed method include preservation of higher frequencies and reduction of spectral distortions at far offsets. The need for severe muting after the correction is reduced, allowing longer spreads for stack, velocity and AVO analysis. The proposed technique has been tested on model and real data. The method may improve the resolution of CMP stack and AVO attribute analysis. The only assumptions for this stretch-free NMO correction are (i) all time samples of a digital reflected wavelet at a particular offset have the same normal moveout, and (ii) reflection records have an interference nature.     

基于时频稀疏性分析法的低信噪比微震事件识别与恢复

微震监测是直观评价压裂过程和压裂效果的有效手段.微震事件识别是微震监测的首要步骤.然而对于低信噪比微震监测数据,常规识别方法很难取得满意效果.基于微震事件在时频域中的稀疏性,本文提出利用Renyi熵值表示微震监测数据的时频稀疏程度,并以时频距离为约束条件,建立以低熵值的道数为判别阈值的目标函数.本文方法能在识别出微震事件的同时,恢复出较为清晰的微震事件.通过数值计算和对实际监测数据的测试,表明该方法对低信噪比的微震监测数据有较好的处理效果.     

Ground-roll attenuation using a 2D time-derivative filter

We present a new filtering method for the attenuation of ground-roll. The method is based on the application of a bi-dimensional filter for obtaining the time-derivative of the seismograms. Before convolving the filter with the input data matrix, the normal moveout correction is applied to the seismograms with the purpose of flattening the reflections. The method can locally attenuate the amplitude of data of low frequency (in the ground-roll and stretch normal moveout region) and enhance flat events (reflections). The filtered seismograms can reveal horizontal or sub-horizontal reflections while vertical or sub-vertical events, associated with ground-roll, are attenuated. A regular set of samples around each neighbourhood data sample of the seismogram is used to estimate the time-derivative. A numerical approximation of the derivative is computed by taking the difference between the interpolated values calculated in both the positive and the negative neighbourhood of the desired position. The coefficients of the 2D time-derivative filter are obtained by taking the difference between two filters that interpolate at positive and negative times. Numerical results that use real seismic data show that the proposed method is effective and can reveal reflections masked by the ground-roll. Another benefit of the method is that the stretch mute, normally applied after the normal moveout correction, is unnecessary. The new filtering approach provides results of outstanding quality when compared to results obtained from the conventional FK filtering method.     

三维地震勘探数据的剩余静校正方法研究与软件实现

剩余静校正的目的是消除地震道中由近地表因素造成的静态剩余时差的影响,通常以时差的四因子分解模型为基础建立剩余静校正方程组,并采用迭代求解方法获得剩余静校正量。但实际上,由于地层的倾斜,时差中还应包含横向和纵向倾角项,因此本文首先对剩余校正的理论模型进行了改进,提出了以扩展面元为计算单元的时差分解六因子模型。又由于地层的走向具有局部线性性,因此剩余动校正项,横向及纵向倾角项系数仅在一些给定的面元上计     

Common-reflection-surface stack — a real data example

The simulation of a zero-offset (ZO) stack section from multi-coverage reflection data is a standard imaging method in seismic processing. It significantly reduces the amount of data and increases the signal-to-noise ratio due to constructive interference of correlated events. Conventional imaging methods, e.g., normal moveout (NMO)/dip moveout (DMO)/stack or pre-stack migration, require a sufficiently accurate macro-velocity model to yield appropriate results, whereas the recently introduced common-reflection-surface stack does not depend on a macro-velocity model. For two-dimensional seismic acquisition, its stacking operator depends on three wavefield attributes and approximates the kinematic multi-coverage reflection response of curved interfaces in laterally inhomogeneous media. The common-reflection-surface stack moveout formula defines a stacking surface for each particular sample in the ZO section to be simulated. The stacking surfaces that fit best to actual events in the multi-coverage data set are determined by means of coherency analysis. In this way, we obtain a coherency section and a section of each of the three wavefield attributes defining the stacking operator. These wavefield attributes characterize the curved interfaces and, thus, can be used for a subsequent inversion. In this paper, we focus on an application to a real land data set acquired over a salt dome. We propose three separate one-parametric search and coherency analyses to determine initial common-reflection-surface stack parameters. Optionally, a subsequent optimization algorithm can be performed to refine these initial parameters. The simulated ZO section obtained by the common-reflection-surface stack is compared to the result of a conventional NMO/DMO/stack processing sequence. We observe an increased signal-to-noise ratio and an improved continuity along the events for our proposed method — without loss of lateral resolution.     

CRS-stack-based residual static correction

In the case of onshore data sets, the acquired reflection events can be strongly impaired due to rough top‐surface topography and inhomogeneities in the uppermost low‐velocity layer, the so‐called weathering layer. Without accounting for these influences, the poor data quality will make data processing very difficult. Usually, the correction for the top‐surface topography is not perfect. The residuals from this correction and the influence of the weathering layers lead to small distortions along the reflection events. We integrated a residual static correction method into our data‐driven common‐reflection‐surface‐stack‐based imaging workflow to further eliminate such distortions. The moveout‐corrected traces and the stacked pilot trace are cross‐correlated to determine a final estimate of the surface‐consistent residual statics in an iterative manner. As the handling of top‐surface topography within the common‐reflection‐surface stack is discussed in a separate paper in this special issue, the corresponding residual static correction will be explained in more detail. For this purpose, the results obtained with a data set from the Arabian Peninsula will be presented.     

A robust method for microseismic event detection based on automatic phase pickers

We present a robust method for the automatic detection and picking of microseismic events that consists of two steps. The first step provides accurate single-trace picks using three automatic phase pickers adapted from earthquake seismology. In the second step, a multi-channel strategy is implemented to associate (or not) the previous picks with actual microseismic signals by taking into account their expected alignment in all the available channels, thus reducing the false positive rate. As a result, the method provides the number of declared microseismic events, a confidence indicator associated with each of them, and the corresponding traveltime picks. Results using two field noisy data records demonstrate that the automatic detection and picking of microseismic events can be carried out with a relatively high confidence level and accuracy.