A first arrival detection method for low SNR microseismic signal

Most of the microseismic signals have low signal-to-noise ratio (SNR) due to the strong background noise, which makes it difficult to locate the first arrival time. Both accuracy and stability of conventional methods are poor in this situation. To overcome this problem, here we proposed a new method based on the adaptive Morlet wavelet and principal component analysis process in wavelet coefficients matrix. The three components of microseismic signal make it possible to extract the features in wavelet coefficients domain. Then the reconstructed signal from weighted features presents an obvious first arrival. Tests on synthetic signals and real data provide a solid evidence for its feasibility in low SNR microseismic signal.     

基于稀疏分布特征的井下微地震信号识别与提取方法

井下微震监测获得的地震记录往往包含大量的噪声,记录信噪比很低.有效地震信号的识别与提取是进行后续地震定位等工作之前需要优先解决的问题.经过研究发现,井下水压裂微地震信号具有稀疏分布的特征,而井下环境噪声则具有更多的Gaussian分布特征.为此,本文提出将图像处理领域适宜于稀疏分布信号降噪处理的稀疏码收缩方法应用于井下微震监测数据处理.为解决需要利用与待处理数据中有效信号成分具有相似分布特征的无噪信号序列估算正交基以及计算效率等问题,将原方法与小波变换理论相结合.即通过优选小波基函数作为正交基进行小波变换将信号分解为不同级的小波系数,利用稀疏码收缩方法中对稀疏编码施加的非线性收缩方式作为阈值准则对小波系数进行改造.通过多方面的数值实验证明了该方法在处理地震子波及井下微地震信号方面准确可靠.含噪记录经过处理后有效地震信号的到时、波形、时频谱特征等均能得到良好的识别和恢复.并且该方法具有很强的抗噪能力,当信噪比低至-20~-30db时,仍然能够发挥作用.在处理大量实际井下微震监测数据的过程中,面对多种复杂情况,本方法展现出了计算效率高、计算结果可靠、应用简单等优势,证明了其本身具有实际应用价值,值得进一步的研究和推广.     

First arrival time picking for microseismic data based on DWSW algorithm

The first arrival time picking is a crucial step in microseismic data processing. When the signal-to-noise ratio (SNR) is low, however, it is difficult to get the first arrival time accurately with traditional methods. In this paper, we propose the double-sliding-window SW (DWSW) method based on the Shapiro-Wilk (SW) test. The DWSW method is used to detect the first arrival time by making full use of the differences between background noise and effective signals in the statistical properties. Specifically speaking, we obtain the moment corresponding to the maximum as the first arrival time of microseismic data when the statistic of our method reaches its maximum. Hence, in our method, there is no need to select the threshold, which makes the algorithm more facile when the SNR of microseismic data is low. To verify the reliability of the proposed method, a series of experiments is performed on both synthetic and field microseismic data. Our method is compared with the traditional short-time and long-time average (STA/LTA) method, the Akaike information criterion, and the kurtosis method. Analysis results indicate that the accuracy rate of the proposed method is superior to that of the other three methods when the SNR is as low as ??10 dB.     

基于Context模型的Shearlet变换地面微地震随机噪声压制

地面微地震监测采集到的微地震信号通常能量微弱,信噪比低,如何提高微震数据的信噪比是数据处理的难题.Shearlet变换是一种新型的多尺度几何分析方法,具有敏感的方向性和较强的稀疏表示特性,能起到很好的随机噪声压制效果.由于地面微震数据的有效信号大多被淹没在噪声中,基于传统阈值的Shearlet变换（the traditional threshold-based Shearlet transform TST）只考虑到尺度或方向的阈值,在去噪过程中会过度扼制有效信号系数,造成有效信号能量损失.因而,本文建立Context模型,得到基于Context模型的Shearlet变换（the Context-model-based Shearlet transform CMST）方法,改进传统Shearlet阈值方法的不足.我们通过所建立的Context模型将能量相近的各方向系数划分为同一组,并分组估计阈值,分别处理各部分系数,达到微弱同相轴有效恢复的目的.通过TST及CMST的模拟实验与实际地面微震记录处理结果对比可知,本文方法在低信噪比条件下比对比方法更加有效地恢复地面微震数据的微弱信号,随机噪声压制效果明显,在-10 dB条件下,提升信噪比18.3741 dB.     

Attenuation from microseismic datasets by the peak frequency method benchmarked with the spectral ratio method

Recently proposed peak-frequency method is used to estimate the P- and S-wave quality factors from microseismic events. We use a downhole monitoring dataset of 10 high signal-to-noise ratio microseismic events to calculate P- and S-wave effective attenuation of a carbonate reservoir. We benchmark these results with the spectral ratio method and obtain mutually consistent results. Additionally we develop and test two techniques of peak frequency determination. We show that the peak frequency method can be successfully used in the estimation of the quality factor and it provides precise measurements of attenuation.     

用优化聚束滤波方法消除低信噪比地震资料中的多次波

针对常规方法如f k,Radon变换不能很好解决的消除低信噪比地震勘探资料的多次波问题,本文提出了优化聚束滤波方法,采用具有静态权的自适应聚束滤波器,并调整聚束滤波器设计的约束准则,去除低信噪比复杂实际资料中的多次波．实际资料处理结果表明,优化聚束滤波方法改善了最小方差无偏聚束滤波方法不能较好地处理低覆盖低信噪资料处理的局限性,在实际处理中计算量增加不大．     

A first arrival picking method of microseismic data based on single time window with window length independent

First arrival picking is a key factor which affects the precision of microseismic data analysis. Here, we propose a new method, which employs the maximum eigenvalue to constraint the Maeda-Akaike Information Criterion (Maeda-AIC) algorithm. First, aims at addressing the pick result affected by signal-to-noise ratio (SNR) of microseismic data, maximum eigenvalue method based on polarization analysis is applied, and the maximum eigenvalue is calculated firstly, as for three component (3C) microseismic data, the maximum eigenvalue is calculated with corresponding covariance matrix, a time window need to be set in the process of building the covariance matrix, and it is the only time window set in the method proposed in this paper, so the method is called single window Maeda-AIC (SWM-AIC), to the single component (1C) microseismic data, the variance of the data is taken as the maximum eigenvalue. Then, to reduce the effect of time window and increase the automation of the algorithm, Maeda-AIC method which is a non-window-based first arrival picking method is applied. Maeda-AIC values in preliminary window are calculated, and the preliminary window is the sequence before the largest eigenvalue of the 3C or 1C data. We validate the developed method with both synthetic and field microseismic data, using a range of signal-to-noise ratios. The developed method is compared with some basic methods, specifically STA/LTA, Maeda-AIC, and the maximum eigenvalue method. The results demonstrate that the new method is much better at identifying first arrival times than basic methods when the data have a low signal-to-noise ratio, and is even faster than the STA/LTA method with 1C data. In contrast to other improved methods, threshold value is not required for this method, and the only time window used in this method is just for maximum eigenvalue calculation, through test in the paper, its length has almost no effect on the first arrival picking.     

A seismic interpolation and denoising method with curvelet transform matching filter

A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.     

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.     

Filter formulation and wavefield separation of cross-well seismic data

Multichannel filtering to obtain wavefield separation has been used in seismic processing for decades and has become an essential component in VSP and cross-well reflection imaging. The need for good multichannel wavefield separation filters is acute in borehole seismic imaging techniques such as VSP and cross-well reflection imaging, where strong interfering arrivals such as tube waves, shear conversions, multiples, direct arrivals and guided waves can overlap temporally with desired arrivals. We investigate the effects of preprocessing (alignment and equalization) on the quality of cross-well reflection imaging wavefield separation and we show that the choice of the multichannel filter and filter parameters is critical to the wavefield separation of cross-well data (median filters, f–k pie-slice filters, eigenvector filters). We show that spatial aliasing creates situations where the application of purely spatial filters (median filters) will create notches in the frequency spectrum of the desired reflection arrival. Eigenvector filters allow us to work past the limits of aliasing, but these kinds of filter are strongly dependent on the ratio of undesired to desired signal amplitude. On the basis of these observations, we developed a new type of multichannel filter that combined the best characteristics of spatial filters and eigenvector filters. We call this filter a ‘constrained eigenvector filter’. We use two real data sets of cross-well seismic experiments with small and large well spacing to evaluate the effects of these factors on the quality of cross-well wavefield separation. We apply median filters, f–k pie-slice filters and constrained eigenvector filters in multiple domains available for these data sets (common-source, common-receiver, common-offset and common-midpoint gathers). We show that the results of applying the constrained eigenvector filter to the entire cross-well data set are superior to both the spatial and standard eigenvector filter results.     

Median filter behaviour with seismic data1

Median filters may be used with seismic data to attenuate coherent wavefields. An example is the attenuation of the downgoing wavefield in VSP data processing. The filter is applied across the traces in the ‘direction’ of the wavefield. The final result is given by subtracting the filtered version of the record from the original record. This method of median filtering may be called ‘median filtering operated in subtraction’. The method may be extended by automatically estimating the slowness of coherent wavefields on a record. The filter is then applied in a time- and-space varying manner across the record on the basis of the slowness values at each point on the record. Median filters are non-linear and hence their behaviour is more difficult to determine than linear filters. However, there are a number of methods that may be used to analyse median filter behaviour: (1) pseudo-transfer functions to specific time series; (2) the response of median filters to simple seismic models; and (3) the response of median filters to steps that simulate terminating wavefields, such as faults on stacked data. These simple methods provide an intuitive insight into the behaviour of these filters, as well as providing a semiquantitative measurement of performance. The performance degradation of median filters in the presence of trace-to-trace variations in amplitude is shown to be similar to that of linear filters. The performance of median filters (in terms of signal distortion) applied obliquely across a record may be improved by low-pass filtering (in the t-dimension). The response of median filters to steps is shown to be affected by background noise levels. The distortion of steps introduced by median filters approaches the distortion of steps introduced by the corresponding linear filter for high levels of noise.     

LEAST-SQUARES FILTERS WITHOUT TRANSIENT ERRORS: AN EXAMINATION OF THE ERRORS IN LEAST-SQUARES FILTER DESIGN*

A new approach has been developed for the design of cross-equalization filters by the least-squares method. The filters estimated by this new exact method are subject to only two types of error: bias and random error. Cross-equalization filters estimated by a more conventional least-squares method are further subject to “transient error”. This type of error becomes important when designing filters from a data gate of a length comparable with the length of the filter, i.e., less than four times the length of the filter. The effect of altering various design parameters has been investigated for the new method. It has been found that the proportion of bias in the filter decreases as the effective filter length increases, whereas the random error in the filter decreases with increase in either the signal-to-noise ratio of the data or the ratio of the data duration to the filter length. The level of whitening applied to the auto-correlation matrix before inversion was not found to be a critical design parameter. Also, two techniques have been tested for reducing any anomalous d.c. component in the calculated filter.     

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.     

Full‐waveform based microseismic event detection and signal enhancement: An application of the subspace approach

Microseismic monitoring has proven invaluable for optimizing hydraulic fracturing stimulations and monitoring reservoir changes. The signal to noise ratio of the recorded microseismic data varies enormously from one dataset to another, and it can often be very low, especially for surface monitoring scenarios. Moreover, the data are often contaminated by correlated noises such as borehole waves in the downhole monitoring case. These issues pose a significant challenge for microseismic event detection. In addition, for downhole monitoring, the location of microseismic events relies on the accurate polarization analysis of the often weak P‐wave to determine the event azimuth. Therefore, enhancing the microseismic signal, especially the low signal to noise ratio P‐wave data, has become an important task. In this study, a statistical approach based on the binary hypothesis test is developed to detect the weak events embedded in high noise. The method constructs a vector space, known as the signal subspace, from previously detected events to represent similar, yet significantly variable microseismic signals from specific source regions. Empirical procedures are presented for building the signal subspace from clusters of events. The distribution of the detection statistics is analysed to determine the parameters of the subspace detector including the signal subspace dimension and detection threshold. The effect of correlated noise is corrected in the statistical analysis. The subspace design and detection approach is illustrated on a dual‐array hydrofracture monitoring dataset. The comparison between the subspace approach, array correlation method, and array short‐time average/long‐time average detector is performed on the data from the far monitoring well. It is shown that, at the same expected false alarm rate, the subspace detector gives fewer false alarms than the array short‐time average/long‐time average detector and more event detections than the array correlation detector. The additionally detected events from the subspace detector are further validated using the data from the nearby monitoring well. The comparison demonstrates the potential benefit of using the subspace approach to improve the microseismic viewing distance. Following event detection, a novel method based on subspace projection is proposed to enhance weak microseismic signals. Examples on field data are presented, indicating the effectiveness of this subspace‐projection‐based signal enhancement procedure.     

Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array

Distributed acoustic sensing is a growing technology that enables affordable downhole recording of strain wavefields from microseismic events with spatial sampling down to ∼1 m. Exploiting this high spatial information density motivates different detection approaches than typically used for downhole geophones. A new machine learning method using convolutional neural networks is described that operates on the full strain wavefield. The method is tested using data recorded in a horizontal observation well during hydraulic fracturing in the Eagle Ford Shale, Texas, and the results are compared to a surface geophone array that simultaneously recorded microseismic activity. The neural network was trained using synthetic microseismic events injected into real ambient noise, and it was applied to detect events in the remaining data. There were 535 detections found and no false positives. In general, the signal-to-noise ratio of events recorded by distributed acoustic sensing was lower than the surface array and 368 of 933 surface array events were found. Despite this, 167 new events were found in distributed acoustic sensing data that had no detected counterpart in the surface array. These differences can be attributed to the different detection threshold that depends on both magnitude and distance to the optical fibre. As distributed acoustic sensing data quality continues to improve, neural networks offer many advantages for automated, real-time microseismic event detection, including low computational cost, minimal data pre-processing, low false trigger rates and continuous performance improvement as more training data are acquired.     

可控震源定向照明方法的仿真研究

当野外噪声很强,即使使用组合震源地震也无法获得满意信噪比的地震数据时,本文提出了一种基于可控震源阵列的定向照明控制方法,采用该方法可形成定向地震波.通过仿真研究合成了8激震器可控震源阵列分别采用简单组合及定向照明技术得到的单炮地震记录,可以看出采用合适的延时参数,定向照明单炮地震记录的反射波信噪比高于组合地震情况.定量的计算结果表明,实验条件下采用0.89 ms延时参数,各反射波信噪比分别提高了10.19 dB,3.23 dB和1.02 dB.由此可见,可控震源定向照明地震技术是一种提高原始地震资料信噪比的有效方法.     

叠前三维地震近地表吸收补偿技术在大庆油田的应用研究(英文)

与深层致密岩石相比,相对疏松的近地表地层严重吸收了地震波的高频成分,降低了地震数据的分辨率和高频成分的信噪比。本文利用井中雷管激发、地面检波器接收的微测井直达波资料,通过分析近地表地层不同传播距离的地震直达波频谱信息差异,采用维纳滤波方法,进行近地表吸收补偿反滤波器的求取,将不同微测井测量点对应的近地表吸收补偿反滤波器应用于相应的叠前共检波点道集地震数据,完成了叠前地震数据的空变吸收衰减补偿,克服了叠后地震数据无法实现空变补偿的难题。叠前三维地震近地表吸收补偿后的数据,较补偿前地震数据的优势信噪比频带宽度明显拓宽,低频成分基本保持,反射信息量有较大程度增加,而且与合成记录吻合更好,能更好的满足地质解释的需要,提高解释精度。     

Suppressing random noise to broaden the useful bandwidth of seismic reflection data

The signal-to-noise ratio （SNR） of seismic reflection data in many areas is rather poor and conventional two-dimensional filters designed to suppress noise with different moveout from the signal tend to generate artifacts. We have extended a method of multichannel filtering, based on the hypothesis that signals on adjacent channels are similar, for enhancing the SNR on stacked sections. Using only the mid-range frequencies where the SNR is highest, the event trend is found for overlapping windows on the section and the average signal vector is calculated. Then the data from the full bandwidth section are projected onto the spatially varying unit similarity vectors and the results are merged for the overlapping windows. Application of the method to synthetic data containing steeply dipping events and to a stacked section for a marine 2D line has produced good results. The modifications we have introduced carry a small overhead in computing time but they should enable the method to be used effectively even on sections containing steep dips.     

可展宽地震资料有效频宽的随机噪声衰减方法

很多地区地震资料的信噪比较低,而用于压制与信号具有不同方向的随机噪声的常规二维滤波方法常常产生假信息。基于相邻信号具有相干性这一假设,本文提出了一种叠后衰减随机噪声的多道滤波方法。该方法利用信噪比最高的中频段信息(含有主频的这一频率区间)分时窗计算信号单位矢量,并将该时窗内全频段数据向信号单位矢量方向投影,对各时窗(包括时间方向和空间方向)重叠部分按比例进行加权。我们利用这种方法对含有陡倾角的合成地震数据和海上二维实际地震资料进行了处理,处理效果很好。这种方法较为费时,但不受倾角限制,应用范围广。     

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.