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1.
Yinpo Xu Cheng Yin Xuefeng Zou Yudong Ni Yingjie Pan Liangjun Xu 《Geophysical Prospecting》2020,68(4):1228-1252
As the application of high-density high-efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first-break picking work of massive low signal-to-noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti-noise to low signal-to-noise ratio primary wave, this paper proposes a high accurate automated first-break picking method for low signal-to-noise ratio primary wave from high-density acquisition in areas with a complex surface. Firstly, this method determines first-break time window using multi-azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi-angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi-azimuth time window spatial interpolation technology provides the base for accurately picking first-break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal-to-noise ratio primary waves; the multi-angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal-to-noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first-break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal-to-noise ratio seismic data, has a significant effect on seismic records from high-density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near-surface modelling and statics calculation. 相似文献
2.
地震波初至走时的识别在地震勘探、人工地震层析成像以及全球地震层析成像方法研究中起重要作用．初至走时拾取的精度在很大的程度上影响地震层析成像及演的精度．本研究以提高地震波初至走时拾取的精度及定量化程度为目标,利用计算地震道时间序列分数维的方法,实现了地震波初至走时的自动拾取．本文以分形理论为基础,进行了地震道时间序列Hausdorff分数维的计算．计算结果表明地震道时间序列的分数维在初至到达前后具有不同的数值,其变化点能够定量指示出初至走时的位置．本文还给出了利用该方法对实测数据进行初至走时拾取的实例． 相似文献
3.
V. Lapenna M. Macchiato D. Patella C. Satriano C. Serio V. Tramutoli 《Geophysical Prospecting》1994,42(8):917-952
We study the geoelectrical problem of picking out the useful signal from voltage time series, monitored under conditions of a low signal-to-noise ratio and non-stationary noise. Statistical tests performed at different sites show that geoelectrical noise often belongs to the class of non-stationary phenomena with non-Gaussian probability distributions. In such cases, the application of conventional methods of geoelectrical useful signal extraction, based on the stationary white-noise assumption, gives biased estimates. For the on-line processing of geoelectrical recordings, we recommend the use of the periodogram technique combined with the Kolmogorov–Smirnov test, a suitable algorithm of which is described in detail. The suggested procedure allows data acquisition to stop as soon as the useful signal power is estimated with a relative error smaller than a predetermined value. Finally, we compare the suggested procedure with the autoregressive approach. The previously used and simpler periodogram method, applied to the solution of problems of this kind, appears to give better performances than the autoregressive analysis. 相似文献
4.
One of the main objectives of seismic digital processing is the improvement of the signal-to-noise ratio in the recorded data. Wiener filters have been successfully applied in this capacity, but alternate filtering devices also merit our attention. Two such systems are the matched filter and the output energy filter. The former is better known to geophysicists as the crosscorrelation filter, and has seen widespread use for the processing of vibratory source data, while the latter is. much less familiar in seismic work. The matched filter is designed such that ideally the presence of a given signal is indicated by a single large deflection in the output. The output energy filter ideally reveals the presence of such a signal by producing a longer burst of energy in the time interval where the signal occurs. The received seismic trace is assumed to be an additive mixture of signal and noise. The shape of the signal must be known in order to design the matched filter, but only the autocorrelation function of this signal need be known to obtain the output energy filter. The derivation of these filters differs according to whether the noise is white or colored. In the former case the noise autocorrelation function consists of only a single spike at lag zero, while in the latter the shape of this noise autocorrelation function is arbitrary. We propose a novel version of the matched filter. Its memory function is given by the minimum-delay wavelet whose autocorrelation function is computed from selected gates of an actual seismic trace. For this reason explicit knowledge of the signal shape is not required for its design; nevertheless, its performance level is not much below that achievable with ordinary matched filters. We call this new filter the “mini-matched” filter. With digital computation in mind, the design criteria are formulated and optimized with time as a discrete variable. We illustrate the techniques with simple numerical examples, and discuss many of the interesting properties that these filters exhibit. 相似文献
5.
Ideally, geophones would be placed in a noiseless environment, in which case there would be no reason to resort to arrays of geophones. If the noise is such that an array is required, the objective of the array is to enhance the signal-to-noise ratio and thus to maximize the intelligence that can be derived from a given signal. The design of the array will be a function of the signal characteristics, of the direction and velocity of the noise in the bandpass of the signal, and of the site geology. It has been demonstrated previously that in a practical sense the optimum array processing is represented by precise beam forming, by which we mean simple time-delay and summation. Increasing the number N of sensors within a given area decreases the inter-element spacing and may increase the coherency between noise samples at adjacent sensors, thus yielding poorer results compared to √N improvement one expects to get if the noise is uncorrelated. Increasing the number of sensors by proportionately increasing the area is liable to result in signal deterioration, also yielding an unfavorable comparison to √N improvement in signal-to-noise. These two effects, together with economical factors, combine to limit the number of sensors that can be used. Although the data on which our conclusions are reached were drawn from earthquake seismology, the principles involved are equally applicable to exploration seismology and to other geophysical measurements in which arrays of sensors are required. 相似文献
6.
Vibroseis data recorded at short source–receiver offsets can be swamped by direct waves from the source. The signal-to-noise ratio, where primary reflections are the signal and correlation side lobes are the noise, decreases with time and late reflection events are overwhelmed. This leads to low seismic resolution on the vibroseis correlogram. A new precorrelation filtering approach is proposed to suppress correlation noise. It is the ‘squeeze-filter-unsqueeze’ (SFU) process, a combination of ‘squeeze’ and ‘unsqueeze’ (S and U) transformations, together with the application of either an optimum least-squares filter or a linear recursive notch filter. SFU processing provides excellent direct wave removal if the onset time of the direct wave is known precisely, but when the correlation recognition method used to search for the first arrival fails, the SFU filtering will also fail. If the tapers of the source sweeps are badly distorted, a harmonic distortion will be introduced into the SFU-filtered trace. SFU appears to be more suitable for low-noise vibroseis data, and more effective when we know the sweep tapers exactly. SFU requires uncorrelated data, and is thus cpu intensive, but since it is automatic, it is not labour intensive. With non-linear sweeps, there are two approaches to the S,U transformations in SFU. The first requires the non-linear analytical sweep formula, and the second is to search and pick the zero nodes on the recorded pilot trace and then carry out the S,U transformations directly without requiring the algorithm or formula by which the sweep was generated. The latter method is also valid for vibroseis data with a linear sweep. SFU may be applied to the removal of any undesired signal, as long as the exact onset time of the unwanted signal in the precorrelation domain is known or determinable. 相似文献
7.
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。 相似文献
8.
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. 相似文献
9.
A challenge in microseismic monitoring is quantification of survey acquisition and processing errors, and how these errors jointly affect estimated locations. Quantifying acquisition and processing errors and uncertainty has multiple benefits, such as more accurate and precise estimation of locations, anisotropy, moment tensor inversion and, potentially, allowing for detection of 4D reservoir changes. Here, we quantify uncertainty due to acquisition, receiver orientation error, and hodogram analysis. Additionally, we illustrate the effects of signal to noise ratio variances upon event detection. We apply processing steps to a downhole microseismic dataset from Pouce Coupe, Alberta, Canada. We use a probabilistic location approach to identify the optimal bottom well location based upon known source locations. Probability density functions are utilized to quantify uncertainty and propagate it through processing, including in source location inversion to describe the three-dimensional event location likelihood. Event locations are calculated and an amplitude stacking approach is used to reduce the error associated with first break picking and the minimization with modelled travel times. Changes in the early processing steps have allowed for understanding of location uncertainty of the mapped microseismic events. 相似文献
10.
Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography. Manual picking is considered as the most accurate way to access the arrival times but time consuming. Many automatic picking methods were proposed in the past decades, but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals. As the increasing deployment of large seismic array, the existing methods can not meet the requirements of quick and accurate phase picking. In this study, we applied a phase picking algorithm developed on the base of deep convolutional neuron network (PickNet) to pick seismic phase arrivals in ChinArray-Phase III. The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array. The raw picked travel-time data shows a large variation deviated from the traveltime curves. The absolute location residual is a key criteria for travel-time data selection. Besides, we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet. This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake. 相似文献
11.
2016年6月在南黄海海域实施了海底地震仪(OBS)的二维深地震探测.本文详细分析了在该次地震探测中获得的浅水水域OBS数据的特征,提出了噪声的组合压制方法.研究表明,浅海水域的OBS数据在系统时间、能量及子波等方面存在明显差异,海底多次波干扰严重、有效频段中陷波问题突出,原始台站记录信噪比低、品质差、大炮检距的有效震相难以识别和拾取.本文提出的噪声组合压制处理技术与流程,主要由基于统计子波反褶积的子波整形、基于多项式插值的t-x域线性噪声压制和采用自动搜索的海底多次波压制等三部分组成.净化处理之后,反射/折射震相的波组特征清晰,信噪比得到有效改善与较大提高,可识别震相的范围较常规处理平均扩大60%以上.本文完善了浅水区OBS数据处理的步骤与流程,将为后续地壳结构研究提供可靠的基础数据,可更好的服务于地壳深地震及油气资源的探测. 相似文献
12.
大数据量、强噪声环境给地震P波到时的自动提取带来很大挑战.针对此问题,本文通过构建特殊的特征函数,建立SNR与STA/LTA的内在联系,提出两种基于SNR的地震P波到时自动提取方法,即基于SNR的STA/LTA方法与基于SNR的综合方法.这两种方法分别是运用SNR概念对传统STA/LTA方法和STA/LTA与AIC综合方法的改进.仿真分析结果表明:对于弱噪声环境(10dB)和一般噪声环境(6dB),本文方法较传统STA/LTA方法对地震P波到时提取的准确度更高;而对于强噪声环境(3dB),本文方法仍能准确提取地震P波到时,而传统STA/LTA方法则出现了较大的误判率(10%)与漏判率(65%).本文方法为STA/LTA赋予了明确的物理意义,使其阈值的选取建立在严密的数学推导之上.另外,本文方法在进行地震P波到时自动提取的同时,兼具数据预处理功能,无需额外的基线校正或高通滤波,因而具有较好的实时性. 相似文献
13.
Emil Blias 《Geophysical Prospecting》2012,60(6):1024-1029
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. 相似文献
14.
An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding 
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下载免费PDF全文 Jubran Akram 《Geophysical Prospecting》2018,66(5):872-893
Noise suppression or signal‐to‐noise ratio enhancement is often desired for better processing results from a microseismic dataset. In this paper, a polarization–linearity and time–frequency‐thresholding‐based approach is used for denoising waveforms. A polarization–linearity filter is initially applied to preserve the signal intervals and suppress the noise amplitudes. This is followed by time–frequency thresholding for further signal‐to‐noise ratio enhancement in the S transform domain. The parameterisation for both polarization filter and time–frequency thresholding is also discussed. Finally, real microseismic data examples are shown to demonstrate the improvements in processing results when denoised waveforms are considered in the workflow. The results indicate that current denoising approach effectively suppresses the background noise and preserves the vector fidelity of signal waveform. Consequently, the quality of event detection, arrival‐time picking, and hypocenter location improves. 相似文献
15.
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。 相似文献
16.
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. 相似文献
17.
Woodon Jeong Constantinos Tsingas Mohammed S. Almubarak 《Geophysical Prospecting》2020,68(6):1742-1757
We propose a workflow of deblending methodology comprised of rank-reduction filtering followed by a signal enhancing process. This methodology can be used to preserve coherent subsurface reflections and at the same time to remove incoherent and interference noise. In pseudo-deblended data, the blending noise exhibits coherent events, whereas in any other data domain (i.e. common receiver, common midpoint and common offset), it appears incoherent and is regarded as an outlier. In order to perform signal deblending, a robust implementation of rank-reduction filtering is employed to eliminate the blending noise and is referred to as a joint sparse and low-rank approximation. Deblending via rank-reduction filtering gives a reasonable result with a sufficient signal-to-noise ratio. However, for land data acquired using unconstrained simultaneous shooting, rank-reduction–based deblending applications alone do not completely attenuate the interference noise. A considerable amount of signal leakage is observed in the residual component, which can affect further data processing and analyses. In this study, we propose a deblending workflow via a rank-reduction filter followed by post-processing steps comprising a nonlinear masking filter and a local orthogonalization weight application. Although each application shows a few footprints of leaked signal energy, the proposed combined workflow restores the signal energy from the residual component achieving significantly signal-to-noise ratio enhancement. These hierarchical schemes are applied on land simultaneous shooting acquisition data sets and produced cleaner and reliable deblended data ready for further data processing. 相似文献
18.
自适应Chirplet变换及其在探地雷达目标探测中的应用(英文) 总被引：4，自引：2，他引：2
探地雷达不仅能够探测金属目标体,而且能够探测非金属目标体,而成为UX0和地雷探测的一种重要的浅部地球物理方法。但是在地雷和UX0探测中,目标体埋藏深度浅,在探地雷达数据信噪比较低情况下,地表和土壤层的反射严重干扰对目标体的拾取。本文采用自适用Chirplet变换来消除地表层和土壤层变化的干扰,并在Radon—Wigner分布的基础上,采用自适用Chirplet变换来拾取目标体的信号。通过对实际探测实验数据应用证明,本方法处理结果比传统的偏移方法具有较高的信噪比,并能清晰地提取目标体信号。 相似文献
19.
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. 相似文献
20.
本文将时频峰值滤波(TFPF)去噪技术应用于共炮点地震资料的随机噪声压制.时频峰值滤波技术是通过频率调制将信号调制成解析信号的瞬时频率,利用解析信号的Wigner-Ville分布的峰值进行瞬时频率估计,恢复有效信号,与其它去噪方法相比,TFPF具有在较少的约束条件下压制强随机噪声的优点.本文针对实际地震资料的非线性特性,利用加窗的Wigner-Ville分布实现TFPF,使得地震信号在一个窗长内近似满足线性瞬时频率条件,减小由地震信号非线性引起的偏差.本文对共炮点地震记录做时频峰值滤波处理,滤波结果表明在地震勘探资料中存在强随机噪声的情况下,利用局部线性化处理的时频峰值滤波技术可以有效地压制地震资料中的随机噪声,恢复出湮没在随机噪声中的地震反射信号.信噪比提高3～6 dB. 相似文献