共查询到20条相似文献，搜索用时 15 毫秒
1.
准确拾取P、S波震相到时是深入开展地震波研究工作的基础,本文改进了自动拾取参数优化函数算法和质量评估方案,引入了拾取到时优化方案,使用基于参数优化的频带-带宽拾取算法、AICD拾取算法和峰度拾取算法对腾冲地区7个宽频带地震台站记录的地震资料开展了地震P、S波到时自动拾取,对拾取结果进行了优化和质量判定.结果表明：经参数优化、拾取优化后,采用3种方法自动拾取的P、S波到时与人工拾取到时的时差在0.1 s内的记录占比分别达到74.66%、70.98%.这些参数值均优于算法改进前的同类参数,证明了优化方法的可靠性. 相似文献
2.
Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China. To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China. We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet. This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China. Then, using different subsets of the DiTing data, we fine-tune the base picker to better adapt to different regions. In total, we provide 5 pickers for major tectonic blocks in China, 33 pickers for provincial-level administrative regions, and 2 special pickers for the Capital area and the China Seismic Experimental Site. These pickers show improved performance in respective regions which they are customized for. They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets. We anticipate that this picker set will facilitate earthquake monitoring in China. 相似文献
3.
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
4.
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. 相似文献
5.
本文讨论了用于微地震信号到时自动拾取的几种方法的原理及特点,包括长短时均值比(STA/LTA)方法、AIC方法、基于高阶统计量偏斜度和峰度的PAI-S/K方法等,提出了移动时窗峰度的快速算法和改进的峰度拾取初至方法.对我国西部某地观测到的13359个微地震记录,采用两种时窗进行了初至到时拾取,并与人工拾取的结果进行了对比.为使所研究的方法达到最佳效果,采用DE全局搜索方法,以人工拾取的初至作为参照,以时差在0.3 s以内的记录所占百分比作为目标函数,自动搜索最佳的拾取参数.结果显示,在拾取时窗选为P波初至前3 s至S波初至位置时,AIC方法的结果最佳,时差在0.3 s以内的记录占比达到93.6%;在拾取时窗选为包含S波到时的时窗时,改进的峰度法效果最佳,时差在0.3 s以内的记录占比83.8%. 相似文献
6.
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. 相似文献
7.
Current deep neural networks (DNN) used for seismic phase picking are becoming more complex, which consumes much computing time without significant accuracy improvement. In this study, we introduce a cascaded classification and regression framework for seismic phase picking, named as the classification and regression phase net (CRPN), which contains two convolutional neural network (CNN) models with different complexity to meet the requirements of accuracy and efficiency. The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0, respectively. The second stage of CRPN is used for high precision classification and regression. The regression is designed to reduce the time difference between the probability maximum and the real arrival time. After being trained using 500,000 P and S phases, the CRPN can process 400 hours’ seismic data per second, whose sampling rate is 1 Hz and 25 Hz for the two stages, respectively, on a Nvidia K2200 GPU, and pick 93% P and 89% S phases with the error being reduced by 0.1s after regression correction. 相似文献
8.
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
9.
Locations and velocities were calculated for microseisms occurring in samples of rock subjected to triaxial loading and injection of pore fluid. This was accomplished by analyzing arrival times of acoustic emission using an automatic first arrival picker. Apparent velocity anomalies were observed prior to both failure of intact samples and violent slip in samples containing saw cuts. Further analysis revealed that these fluctuations in calculated velocity were not due to changes in the true seismie velocity. Instead, variations in calculated velocity are shown to be related to sampling errors in picking first arrivals. The systematic picking of late first arrivals for small magnitude events was found to be a persistent bias resulting in low calculated velocities. This has encouraged the reexamination of earthquake records to determine how important sampling biases are in contributing to reported velocity anomalies. 相似文献
10.
精确获取震相到时是地震定位和地震走时成像等研究的重要基础.近年来,随着地震台站的不断加密,地震台网监测到的地震数量成倍增长,发展快速、准确、适用性强的震相到时自动拾取算法是地震行业的迫切需求.本文在前人工作基础上,发展了Pg、Sg震相自动识别与到时拾取的U网络算法（Unet_cea）,使用汶川余震和首都圈地震台网记录的89344个不同震级、不同信噪比的样本进行训练和测试.研究表明,U网络能够较好地识别Pg、Sg震相类型和拾取到时,Pg、Sg震相的正确识别率分别为81%和79.1%,与人工标注到时的均方根误差分别为0.41 s和0.54 s.U网络在命中率、均方根误差等性能指标上均明显优于STA/LTA和峰度分析自动拾取方法.研究获得的最优模型可以为区域地震台网的自动处理提供辅助. 相似文献
11.
为了研究二氧化碳物理相变技术应用于新型震源研发的可行性,在地下成层性较好的某煤田地震测区,开展了利用二氧化碳相变技术激发地震波的野外人工震源激发-接收实验．并与传统炸药震源进行了对比．地震数据利用Aries2.66型垂直分量反射地震仪和PDS-2型三分量地震仪接收．根据实测地震数据,从野外地震记录震相识别,初至波传播距离分析,震源近场地震信号时频分析,CO_2相变激发震源子波提取和基于CO_2震源子波的地震初至波波形反演实验等多个方面,进行了关于CO_2相变激发技术能否产生地震波信号以及能否将其应用于新型震源研发的可行性研究．研究结果表明CO_2物理相变膨胀能够产生能量集中的地震波信号;在实验区地质条件和激发参量下地震记录中初至波的可识别的传播距离约为1km;震源近场地震信号的主频集中在8～13Hz;利用震源近场数据提取了CO_2震源子波;通过地震初至波波形反演实验认为这种震源子波能够应用于波形反演等方面的研究．因为CO_2相变激发具有绿色、环保、安全等方面的优点,若能进一步在激发能量、激发—延迟时间一致性等方面加以改进,该技术有望在城市隐伏活动断层探测、城市地下空间探测、煤矿高瓦斯环境人工地震勘探等领域发挥重要的作用． 相似文献
12.
本文针对噪声成分和噪声结构的复杂性及弱信号的特征,发展了最新的在线字典学习去噪方法.在线字典学习去噪方法是以数据驱动的方式,反复进行学习构建字典方式,求得信号的稀疏性解以实现对信号的去噪,在此基础上,提出了数据驱动与模型驱动联合的模型约束下的在线字典学习去噪方法,先通过模型驱动方式获得一个较优质的学习样本以构建字典再进行去噪处理.通过和传统小波变换进行理论地震合成记录的效果对比,在高噪声比例的弱信号情况下远远优于传统的时频域去噪方法.实际数据去噪处理表明,模型约束下的在线字典学习去噪方法是一种有效的去噪方法,这种联合去噪方式能在高噪声背景下有效地提取出弱信号,具有广阔的推广应用前景. 相似文献
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14.
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
15.
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. 相似文献
16.
Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods. However, methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking. The present study establishes a deep learning network model combining a convolutional neural network (CNN) and recurrent neural network (RNN). The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time. The neural network automatically picks the P-wave arrival time, providing a strong constraint for small earthquake positioning. The model is shown to achieve an accuracy rate of 90.7% in picking P waves of microseisms in the reservoir area, with a recall rate reaching 92.6% and an error rate lower than 2%. The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes, thus providing new technical measures for subsequent microseismic monitoring in the reservoir area. 相似文献
17.
基于多用户峰度准则的海洋强噪声衰减方法(英文) 总被引：1，自引：1，他引：0
海洋地震勘探过程中,由于采集设备的老化或电源的不稳定而造成的漏电,在地震记录表现为强噪音干扰,利用常规噪音衰减方法处理此类强噪音效果并不理想。鉴于强噪音在统计学上具有相同的特性,本文在基于峰度的盲分离(blind source separation,BSS)算法研究基础上,推导出一种基于多用户峰度(multiuser kurtosis,MUK)准则的噪音衰减算法来估计地震记录中具有相同统计特性的强噪音,并将其从地震记录中分离,从而达到衰减强噪音的目的。模型试验与实际资料的处理表明:该方法能够在好的衰减海洋地震勘探记录中的强噪音,保留了更多的有效信息,提高海洋地震数据的信噪比,具有可行性和应用前景。 相似文献
18.
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
19.
地震震相拾取是地震数据自动处理的首要环节,包括了信号检测、到时估计和震相识别等过程,震相拾取的准确性直接影响到后续事件关联处理的性能,影响观测报告的质量.为了提高震相拾取的准确性,进而提高观测报告质量,本文采用深度卷积神经网络方法来解决震相拾取问题,构建了多任务卷积神经网络模型,设计了分类和回归的联合损失函数,定义了基于加权的分类损失函数,以三分量地震台站的波形数据作为输入,同时实现对震相的检测识别和到时的精确估计.利用美国南加州地震台网的200万条震相和噪声数据对模型进行训练、验证和测试,对于测试集中直达波P、S震相识别的查全率达到98%以上,到时估计的标准偏差分别为0.067s,0.082s.利用迁移学习和数据增强,将模型用于对我国东北地区台网的6个台站13000条数据的训练、验证和测试中,对该数据集P、S震相查全率分别达到91.21%、85.65%.基于迁移训练后的模型,设计了用于连续数据的震相拾取方法,利用连续的地震数据对该算法进行了实际应用测试,并与国家数据中心和中国地震局的观测报告进行比对,该方法的震相检测识别率平均可达84.5%,验证了该方法在实际应用中的有效性.本文所提出的方法展示了深度神经网络在地震震相拾取中的优异性能,为地震震相和事件的检测识别提供了新的思路. 相似文献
20.
Weak Seismic Signal Extraction Based on the Curvelet Transform 总被引：1，自引：1，他引：0
Seismic signal denoising is a key step in seismic data processing. Airgun signals are easy to be interfered with by noise when it travels a long distance due to the weak energy of active source signal of the airgun. Aiming to solve this problem, and considering that the conventional Curvelet transform threshold processing method does not use the seismic spectrum information, we independently process the Curvelet scale layer corresponding to valid data based on the characteristics of the Curvelet transform of multi-scale, multi-direction and capable of expressing the sparse seismic signals in order to fully excavate the information features. Combined with the Curvelet adaptive threshold denoising the algorithm, we apply the Curvelet transform to denoising seismic signals while retaining the weak information in the signal as much as possible. The simulation experiments show that the improved threshold denoising method based on Curvelet transform is superior to the frequency domain filtering, wavelet denoising and traditional Curvelet denoising method in detailed information extraction and signal denoising of low SNR signals. The calculation accuracy of the relative wave velocity variation of underground medium is improved. 相似文献