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1.
受新开发的变分模态分解(VMD)的启发,本文引入一种基于VMD的时频分析方法来分析地震数据.VMD的原理是将信号分解成具有一定中心频率的模态分量,通过这些分量来重构原始信号.这种分解方式可以降低各个模态中的残余噪声,同时进一步减少冗余的模态,很好的克服了模态混叠问题.此外,VMD是一种自适应信号分解技术,它可以非递归地将多分量信号分解为几个准正交固有模态函数,与EMD及其推广(如EEMD,CEEMD)相比,有坚实的数学基础.将VMD方法与CEEMD方法进行比较,对合成数据进行测试显示了基于VMD的时频分析方法具有更好的时频聚焦性,同时对实际数据处理也表明该方法具有突出地质特征和地层信息的潜力. 相似文献
2.
随掘地震超前探测震源具有连续、非可控的特点,需要使用互相关技术将连续震源记录转换为等效脉冲震源记录以获取有效反射信号。但随掘地震超前探测信号往往包含一些能量较强且频带较窄的优势频率成分,使得互相关处理会引入较严重的干扰。为此,本文提出将基于变分模态分解（VMD）的希尔伯特谱白化方法应用于随掘地震超前探测信号。该方法首先利用VMD将地震信号分解为若干个本征模态函数（IMF）,再对各IMF应用希尔伯特变换进行时频分解,最后使用白化滤波器对其希尔伯特谱进行谱白化。数值模拟结果表明基于VMD的希尔伯特谱白化方法能够在保持各道信号一致性的同时均衡信号的不同频率成分,有效压制互相关结果中的虚假同相轴,加快主峰旁瓣衰减。将本文方法应用于安徽淮北杨庄煤矿某巷道实际数据,成功探测了掌子面前方存在的断层构造,表明该方法具有较好的实用性。 相似文献
3.
随着勘探开发的不断深入,常规地震资料受分辨率的限制难以满足精细勘探开发的需求.由于地震信号不同频率成分的衰减程度不同,故可结合分频技术对各频率成分进行差异化补偿,进而提高地震资料分辨率.而常规分频技术普遍分频精度不高,存在模态混叠现象,不能较好地适用于地震资料处理.针对上述问题,本文提出基于自适应变分模态分解(VMD)... 相似文献
4.
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
5.
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. 相似文献
6.
从能量释放的角度讨论了深部岩体开挖激发微地震的机制. 研究表明, 伴随着爆破破岩新自由面形成而发生的岩体弹性应变能释放属于瞬态过程, 高地应力条件下爆破开挖产生的微地震由爆炸荷载和初始地应力(开挖荷载)瞬态释放耦合作用引起. 地应力瞬态释放激发的微地震可成为周围岩体振动的主要组成部分, 这有赖于岩体自身的蓄能能力、岩体开挖方式及开挖面的大小. 通过瀑布沟地下厂房爆破开挖过程中实测围岩地震信号的时能密度和幅值谱分析, 对地应力瞬态释放激发的微地震进行了识别. 耦合地震信号的低频成分主要由初始地应力瞬态卸荷引起,而高频成分主要由爆炸荷载引起. 应用数字信号处理的FIR滤波方法对耦合振动信号进行了初步分离,数值计算验证了分离结果的可靠性. 相似文献
7.
A method to identify the P-arrival of microseismic signals is proposed in this work, based on the algorithm of intrinsic timescale decomposition (ITD). Using the results of ITD decomposition of observed data, information of instantaneous amplitude and frequency can be determined. The improved ratio function of short-time average over long-time average and the information of instantaneous frequency are applied to the time-frequency-energy denoised signal for picking the P-arrival of the microseismic signal. We compared the proposed method with the wavelet transform method based on the denoised signal resulting from the best basis wavelet packet transform and the single-scale reconstruction of the wavelet transform. The comparison results showed that the new method is more effective and reliable for identifying P-arrivals of microseismic signals. 相似文献
8.
为了有效分离矿集区音频大地电磁（AMT）信号中的大尺度强干扰、抑制近源效应,本文提出利用变分模态分解（VMD）和匹配追踪（MP）联合压制AMT强干扰的方法.首先,对比了VMD与经验模态分解（EMD）、固有时间尺度分解（ITD）的处理效果,验证了VMD在避免模态混叠和端点效应方面的优势;讨论了VMD中模态个数对典型大尺度强干扰的去噪性能,并选择合适的模态初步获取待处理信号的重构信息.然后,运用MP对VMD重构信号做二次信噪分离处理,进一步滤除残余的尖脉冲干扰.通过对模拟和实测数据的分析处理,以及与远参考法结果对比,本研究能有效剔除时间域序列中的大尺度强干扰,且重构信号中保留了更多的低频缓变化信息和细节成分,近源干扰得到有效压制;视电阻率-相位曲线更为光滑、连续,低频段的数据质量得到明显改善,其结果能更为真实、可靠地反映地下电性结构信息. 相似文献
9.
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. 相似文献
10.
将地震信号分解成包含频谱互不重叠的单主周期的分量有利于地震信号的分析.分析了经验模态分解(EMD)中模态混叠的内在原因和已有的解决方法,梳理了解决模态混叠的思路框架,进而提出了一种新的基于输入递归高通滤波的EMD算法.首先用递归高通滤波器将信号预分解成频率由高到低的多个分量,实现信号的等价带通滤波,再用EMD对各带通分量按频率高低逐级递归筛分,获得完备的经验模态分量.通过合成信号和地震信号的仿真实验表明,该算法较好地克服了模态混叠,获得了频谱互不重叠的单主周期分量,并成功用于震相分离和分析,为地震信号分析提供了一种新思路. 相似文献
11.
A technique to improve the empirical mode decomposition in the Hilbert-Huang transform 总被引：2，自引：0，他引：2
The Hilbert-based time-frequency analysis has promising capacity to reveal the time-variant behaviors of a system. To admit
well-behaved Hilbert transforms, component decomposition of signals must be performed beforehand. This was first systematically
implemented by the empirical mode decomposition (EMD) in the Hilbert-Huang transform, which can provide a time-frequency representation
of the signals. The EMD, however, has limitations in distinguishing different components in narrowband signals commonly found
in free-decay vibration signals. In this study, a technique for decomposing components in narrowband signals based on waves’
beating phenomena is proposed to improve the EMD, in which the time scale structure of the signal is unveiled by the Hilbert
transform as a result of wave beating, the order of component extraction is reversed from that in the EMD and the end effect
is confined. The proposed technique is verified by performing the component decomposition of a simulated signal and a free
decay signal actually measured in an instrumented bridge structure. In addition, the adaptability of the technique to time-variant
dynamic systems is demonstrated with a simulated time-variant MDOF system. 相似文献
12.
Rui-Sheng Jia Hong-Mei Sun Yan-Jun Peng Yong-Quan Liang Xin-Ming Lu 《Journal of Seismology》2017,21(4):735-748
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. 相似文献
13.
Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition 
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下载免费PDF全文 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. 相似文献
14.
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. 相似文献
15.
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
16.
针对煤矿上覆岩层层状赋存和离层带的特点,构建矿井尺度的微震监测系统异向波速模型,模型中波速向量由地面探头速度与井下探头速度组成.研究了在只有强矿震信号和混有爆破信号两种条件下,以到时残差最小为目标和震源定位误差最小为目标的两种求解模型,模型求解选用具有全局寻优特性的遗传算法与CMEAS算法结合的混合算法.现场实际应用得出,只使用爆破信号的到时残差法最优,混有强矿震信号的到时残差法其次;与爆破信号定位所用的统一简化波速模型相比,震源定位误差大幅度降低.在此基础上进一步减低定位误差,还需从微震台网的优化布设方面解决. 相似文献
17.
山西运城振动事件S变换时频分析 总被引：1，自引：1，他引：0
基于S变换,对2005年以来山西南部运城及其附近地区的振动事件波形进行时频分析,并与山西南部地区的天然地震、爆破和塌陷地震波谱特征进行对比分析。结果表明：天然地震一般S波携带能量较大,衰减较慢,震相高低频成分呈现均匀分布;一般近台记录的人工爆破P波比S波发育,能量衰减较快,震相急促短暂;塌陷地震波列能量随时间和频率的展布相对集中,一般分布在频率较低区域;振动事件震相简单,被不同台站记录的波形能量优势分布及频率分布范围差异较大,衰减特征不明显。据此,基本可以排除山西南部及附近区域的振动事件为天然地震、爆破和塌陷事件的可能。 相似文献
18.
基于前人研究成果以及现场的实测结果,采用卓资山露天钼矿微震监测项目产出资料,提取了5类微震事件的波形特征和时频特征。波形特征显示:微地震的振幅、辐射均匀性和频率变化特征表明微地震是由于岩层受到单力偶和剪切力作用破裂而产生;爆破具有P波初动方向向上、S波不易识别的特点,包含“初震段、主震段、尾波段”三段变化形态;小型边坡滑坡波事件属楔体滑坡,是由多个“加速—缓冲—终止”构成,波形是由包络线呈“V”字形的多组脉冲波列组成;机械开采震动事件具有自振能量不变、脉冲幅度相差很大、持续时间间隔不确定的特点;运输车辆波形振幅具有形态“弱—强—弱”、等频率、包络线呈多段纺锤形的特征。时频空间分布可以分为相对独立、界限分明的两类:一类包含微地震、爆破、机械开采、小型边坡滑坡事件,另一类只包含车辆运输事件。 相似文献
19.
From the data of a microseismic survey of the Lanzarote Island territory (Canary Archipelago) we obtained a microseisms amplitude distribution in the frequency range 0.3–12.5 Hz. We found a distinguished anomaly such as an amplitude depression, whose size and magnitude depend on the frequency. After studying the statistical and polarization properties of microseism signals we proposed a model explaining this depression, based on the presence of a rigid intrusive body in the center of the island. Results of our survey coincided well with independent detailed gravity survey results whose interpretation also implies the presence of intrusion. We estimated shear-wave velocity for the rocks of intrusion and for surrounding rocks using microseismic data. 相似文献
20.
2017年8月8日四川九寨沟M7.0地震是继2008年汶川M8.0地震和2013年芦山M7.0地震之后,青藏高原东缘在不到10年的时间内发生的第3个震级M7.0以上的强震,震中位于青藏高原巴颜喀拉块体东缘东昆仑断裂带东端的塔藏断裂、岷江断裂和虎牙断裂交汇部位,四川省地震局的数字强震台网共有37个台站获取了主震的三分量强震加速度记录。由于傅里叶（Fourier）变换仅能提供强震记录的频域信息,故本文在对九寨沟M7.0地震的加速度记录进行时频分析时采用了一种基于聚类经验模态分解（EEMD）的希尔伯特-黄变换（HHT）方法提取信号时频特性,通过对震中附近台站的加速度记录进行EEMD分解和希尔伯特（Hilbert）变换及谱分析,最终有效获得了信号能量的时频分布特征,量化提取了中心频率、Hilbert能量、最大振幅对应的时间等特性,并与Fourier变换进行了对比研究。研究结果表明:对于非线性的强震记录采用EEMD能够有效抑制经验模态分解（EMD）中存在的模态混叠问题,FFT谱与Hilbert边际谱相比,它在低频处会低估地震动的幅值,随着频率的增加,FFT谱又会放大其幅值。 相似文献