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.     

基于改进长短时窗比值及优化变分模态分解的微震初至拾取算法

针对低信噪比条件下微震初至拾取准确度低的问题,基于信号幅度变化引入权重因子,对传统长短时窗比值（STA/LTA）算法进行改进,提高初次拾取精度。为了进一步降低拾取误差,对变分模态分解（VMD）算法进行优化,基于互相关系数和排列熵准则自适应确定VMD分解层数,对初次拾取结果前后2—3 s的记录进行优化VMD,并计算分解后各本征模函数（IMF）的峰度赤池信息准则值,得到各IMF的到时,以各IMF的拾取结果及能量比综合加权得到二次拾取到时。仿真实验表明:改进后的STA/LTA在较低信噪比下可降低初次拾取误差约0.01 s以上;相比经验模态分解（EMD)和小波包分解,自适应VMD分解后能再次降低误差,最终与人工拾取结果平均误差在0.023 s以内。实际微震信号初至拾取结果表明,本算法能快速有效地识别初至P波,与人工拾取结果相比误差小,准确率高。      

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.     

基于加权K均值聚类的多属性初至拾取方法

为提高初至拾取方法的准确性和自适应能力,将变异系数加权K均值聚类算法引入初至拾取中。首先提取均方根振幅、相邻道相关性、线积分、振幅谱主频等多种地震属性;然后针对地震属性进行加权K均值聚类,自动识别初至所在时窗;最后结合相位校正法,实现时窗内初至波起跳时间的拾取。在此基础上通过实际数据测试,并与长短时窗能量比法、反向传播神经网络方法对比,验证了本文方法的有效性与可行性。结果表明,基于加权K均值聚类的多属性初至拾取方法能较快速、准确地拾取低信噪比数据的初至,并且无需人为判断时窗,从而提高了拾取的自适应能力。      

First-break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms

First-break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first-break picking methods based on time domain analysis in low signal to noise ratio. S-transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S-transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S-transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise.     

基于机器学习和台阵相关性的水力压裂微地震事件自动识别及到时拾取

利用密集台阵对水力压裂微地震进行监测将有助于优化储层压裂、揭示断层活化.为满足密集台阵海量采集数据的处理需求, 本文建立了一种综合运用多种机器学习方法和台阵相关性的、无需人工干预的自动处理流程, 从而能够快速得到高质量的密集台阵震相到时目录.该综合策略包括: (1)利用迁移学习在连续波形中快速检测地震事件; (2)利用U型神经网络PhaseNet自动拾取P波、S波震相; (3)利用三重线性剔除法, 结合密集台阵到时相关性剔除异常到时数据和地震事件; (4)利用K-means和SVM两类机器学习算法, 进一步区分发震时刻接近的多个地震事件, 减小事件漏拾率.通过将该流程应用于四川盆地长宁—昭通页岩气开发区微地震监测数据, 并将自动处理结果与人工拾取结果进行比对发现, 二者在震级测定、定位以及走时成像结果等方面具有很好的一致性, 表明本文处理流程结果精度可达到手动处理精度.本文结果为密集台阵地震监测数据的高效、高精度处理提供了新思路.

     

时窗能量特征法拾取微地震波初始到时的可行性研究

微地震（MS）波初始到时的自动拾取是MS监测数据处理的关键技术之一,也是实现MS震源自动定位的技术难点.本文在MS震源定位结果反演与推断的研究基础上,对不同类型MS波的到时点特征进行了分析与描述,并对不同时窗长度下能量特征值的变化规律进行了研究,提出了控制时窗移动范围和确定时窗长度自适应参数的具体方法,利用建立的MS波初始到时点特征的模式识别库,对拾取的到时进行模式归类、定量评价和匹配,提高了自动拾取结果的可靠性.研究结果表明,对典型的信噪比高的MS波,到时自动拾取的结果与手工拾取的结果基本一致;对无量纲大振幅的MS波,到时自动拾取结果的可靠性要高于手工拾取,对信噪比低和到时点不清晰的MS波自动拾取的可靠性较低.     

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1.33×10-3s,小于3个采样点;方差为3.21×10-6s2;初至到时在手工拾取结果±0.005 s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.     

基于改进的散射积分算法的初至波走时层析

初至波走时层析是获取近地表速度结构的一种常用方法.随着采集技术的不断发展,可使用的数据量迅速增多,传统的基于射线追踪和解方程组的地震走时层析成像方法面临着内存占用大、方程求解不稳定等问题.为了解决这些问题,本文基于前人在波形反演研究中提出的一种改进的散射积分算法,提出了一种预条件最速下降法初至波走时层析.该方法无需存储核函数矩阵与Hessian矩阵即可方便地实现目标函数梯度的计算与预条件,且该方法计算效率高、求解稳定、易于并行.数值实验结果表明,该方法可以获得与传统方法精度相当的反演结果,但所占用的内存大幅减小.     

基于波形相似特征的微地震事件初至拾取及全局校正

微地震事件初至拾取是井下微地震监测数据处理的关键步骤之一.初至误差的存在会使微地震震源定位结果产生较大偏差,进而影响后续的压裂裂缝解释.通常初至拾取过程对所有的微地震事件选择相同的特征函数并采用一致的拾取参数进行统一处理,然而当事件的能量、震源机制、传播路径以及背景噪声等存在明显差异时,所得初至拾取结果差别显著.为了提高微地震事件初至拾取标准一致性,本文提出基于波形相似特征的初至拾取及全局校正方法.该方法首先利用互相关函数对每个事件内的各道记录进行时差校正,得到初始初至信息并形成叠加道,再对所有事件的叠加道进行全局互相关得到事件间初至相对校正量,最终初至结果可以通过各个事件的初始初至信息与其相对校正量相加得到.方法将所有微地震事件初至结果作为一个整体处理,从而能够克服常规方法初至拾取标准一致性差的缺陷.实际资料处理结果表明,相比于常规方法,该方法可以有效提高事件初至拾取和定位结果的一致性.     

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.     

Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker

P phase arrival picking of weak signals is still challenging in seismology. A wavelet denoising is proposed to enhance seismic P phase arrival picking, and the kurtosis picker is applied on the wavelet-denoised signal to identify P phase arrival. It has been called the WD-K picker. The WD-K picker, which is different from those traditional wavelet-based pickers on the basis of a single wavelet component or certain main wavelet components, takes full advantage of the reconstruction of main detail wavelet components and the approximate wavelet component. The proposed WD-K picker considers more wavelet components and presents a better P phase arrival feature. The WD-K picker has been evaluated on 500 micro-seismic signals recorded in the Chinese Yongshaba mine. The comparison between the WD-K pickings and manual pickings shows the good picking accuracy of the WD-K picker. Furthermore, the WD-K picking performance has been compared with the main detail wavelet component combining-based kurtosis (WDC-K) picker, the single wavelet component-based kurtosis (SW-K) picker, and certain main wavelet component-based maximum kurtosis (MMW-K) picker. The comparison has demonstrated that the WD-K picker has better picking accuracy than the other three-wavelet and kurtosis-based pickers, thus showing the enhanced ability of wavelet denoising.     

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.     

Deep learning for P-wave arrival picking in earthquake early warning

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.     

基于逆时成像的井中观测微地震定位精度分析

微地震定位是非常规和低渗透油气勘探开发和地下工程安全监测的关键环节,其准确性是实现油气储层实时刻画和工程灾害监测预警的重要基础.由于微地震信号具有能量弱、频率高、信噪比低的特点,微地震定位精度易受到采集观测系统布设位置的影响,尤其在井下受限空间与高温高压环境中.基于此,本文提出了一种基于逆时成像的井下微地震采集定位精度分析方法,能定量预测实际采集观测系统布设方案在水平和深度方向上的定位偏差和不确定性.区别于传统定位精度分析方法,该方法基于波形而非走时,适用于复杂非均匀介质,同时考虑了信噪比和震源机制对定位精度的影响.均匀和非均匀介质下的实例应用结果均表明,该方法能有效评价微地震采集方案的预期定位精度,进而反馈采集参数设计,从数据采集的源头改善复杂介质条件下的微地震定位效果.

     

Optimization approach to automatic first arrival picking for three‐component three‐dimensional vertical seismic profiling data

A new, adaptive multi‐criteria method for accurate estimation of three‐component three‐dimensional vertical seismic profiling of first breaks is proposed. Initially, we manually pick first breaks for the first gather of the three‐dimensional borehole set and adjust several coefficients to approximate the first breaks wave‐shape parameters. We then predict the first breaks for the next source point using the previous one, assuming the same average velocity. We follow this by calculating an objective function for a moving trace window to minimize it with respect to time shift and slope. This function combines four main properties that characterize first breaks on three‐component borehole data: linear polarization, signal/noise ratio, similarity in wave shapes for close shots and their stability in the time interval after the first break. We then adjust the coefficients by combining current and previous values. This approach uses adaptive parameters to follow smooth wave‐shape changes. Finally, we average the first breaks after they are determined in the overlapping windows. The method utilizes three components to calculate the objective function for the direct compressional wave projection. An adaptive multi‐criteria optimization approach with multi three‐component traces makes this method very robust, even for data contaminated with high noise. An example using actual data demonstrates the stability of this method.     

三维地震与地面微地震联合校正方法

由于地面微地震监测台站布设在地表,会受到地表起伏、低降速带厚度和速度变化的影响,降低了微地震事件的识别准确度和定位精度,限制了地面微地震监测技术在复杂地表地区的应用.因此,将三维地震勘探技术的思路引入到地面微地震监测中,提出了三维地震与地面微地震联合校正方法,将油气勘探和开发技术更加紧密地结合在一起.根据三维地震数据和低降速带测量数据,通过约束层析反演方法建立精确的近地表速度模型,将地面微地震台站从起伏地表校正到高速层中的平滑基准面上,有效消除复杂近地表的影响.其次,根据射孔数据和声波测井速度信息,通过非线性反演方法建立最优速度模型,由于已经消除复杂近地表的影响,在进行速度模型优化时不需要考虑近地表的影响,因而建立的速度模型更加准确.最后,在精确速度模型的基础上,通过互相关方法求取剩余静校正量,进一步消除了复杂近地表和速度模型近似误差的影响.三维地震与地面微地震联合校正方法采用逐步校正的思路,能够有效消除复杂近地表的影响,提高微地震数据的品质和速度模型的精确度,保证了微地震事件的定位精度,具有良好的应用前景.

     

Automatic pickup of arrival time of channel wave based on multi-channel constraints

Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography (CT) result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multichannel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.     

Fault detection based on microseismic events

In unconventional reservoirs, small faults allow the flow of oil and gas as well as act as obstacles to exploration; for, (1) fracturing facilitates fluid migration, (2) reservoir flooding, and (3) triggering of small earthquakes. These small faults are not generally detected because of the low seismic resolution. However, such small faults are very active and release sufficient energy to initiate a large number of microseismic events (MEs) during hydraulic fracturing. In this study, we identified microfractures (MF) from hydraulic fracturing and natural small faults based on microseismicity characteristics, such as the time–space distribution, source mechanism, magnitude, amplitude, and frequency. First, I identified the mechanism of small faults and MF by reservoir stress analysis and calibrated the ME based on the microseismic magnitude. The dynamic characteristics (frequency and amplitude) of MEs triggered by natural faults and MF were analyzed; moreover, the geometry and activity types of natural fault and MF were grouped according to the source mechanism. Finally, the differences among time–space distribution, magnitude, source mechanism, amplitude, and frequency were used to differentiate natural faults and manmade fractures.     

Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition

The existence of strong random noise in surface microseismic data may decrease the utility of these data. Non‐subsampled shearlet transform can effectively suppress noise by properly setting a threshold to the non‐subsampled shearlet transform coefficients. However, when the signal‐to‐noise ratio of data is low, the coefficients related to the noise are very close to the coefficients associated with signals in the non‐subsampled shearlet transform domain that the coefficients related to the noise will be retained and be treated as signals. Therefore, we need to minimise the overlapping coefficients before thresholding. In this paper, a singular value decomposition algorithm is introduced to the non‐subsampled shearlet transform coefficients, and low‐rank approximation reconstructs each non‐subsampled shearlet transform coefficient matrix in the singular value decomposition domain. The non‐subsampled shearlet transform coefficients of signals have bigger singular values than those of the random noise, which implies that the non‐subsampled shearlet transform coefficients can be well estimated by taking only a few largest singular values. Therefore, those properties of singular value decomposition may significantly help minimise overlapping of noise and signals coefficients in the non‐subsampled shearlet transform domain. Finally, the denoised microseismic data are obtained easily by giving a simple threshold to the reconstructed coefficient matrix. The performance of the proposed method is evaluated on both synthetic and field microseismic data. The experimental results illustrate that the proposed method can eliminate random noise and preserve signals of interest more effectively.