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为定量刻画数字化形变观测资料中背景信息和噪声的时 频分布特征, 本文应用二进小波变换方法, 通过对小波分解的主模特征和随机白噪声识别因子变化特征分析, 剖析了山东数字化形变观测资料的正常动态背景和噪声变化规律. 结果表明, 当尺度取2, 3和4时, 分解后的细节部分存在着1/4日波、 半日波、 日波和半月波等准循环周期信号, 其中尤以尺度为3时的信号波幅最大; 尺度取1和5时的细节部分主要包含着噪声; 通过分析和追踪指定尺度的数字化形变观测资料小波变换的非震异常特征变化, 可望捕捉到与强地震孕育过程有关的前兆异常信息. 相似文献
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基于小波变换方法的包头台形变分析 总被引：3，自引：0，他引：3
采用小波变换方法相继对包头台洞体应变和水管倾斜进行处理,研究形变数据固体潮、同震响应和周期性特征等。采用db4作为母小波对包头台形变进行5阶小波分解．分解后的细节部分能清晰地显示出包括1／3日波、半日波、日波和半月波等在内的固体潮汐波。对于2008年以来全球发生的M≥8．0级地震．包头台水管倾斜NS分量均有显著的同震响应现象发生．且在小波分解后的不同阶曲线中同震响应后续波形表现各异．另外在部分地震之前能观测到异常变化。采用Moflet小波作为母小波对包头台水管倾斜NS分量进行小波变换分析．获得的小波变换系数分布图能清晰地显示出倾斜数据中存在的包括半日潮和日潮在内的周期特征及其随时间变化情况。 相似文献
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上海佘山钻孔形变观测资料正常背景噪声变化特征分析 总被引：1，自引：0，他引：1
对上海佘山钻孔形变观测资料正常的背景噪声进行初步分析并定量刻画其正常信息场的变化特征。结果显示:上海佘山形变观测资料的小波变换细节部分不同尺度包含着不同的信号成分,通过研究形变观测资料小波变换各尺度信号的非震异常特征变化,可能会捕捉到与地震孕育过程有关的前兆异常信息。 相似文献
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借助Madab平台,应用能给出不同尺度下信号分解的近似部分和细节部分的小波变换方法．分析了福建省定点和流动跨断层场地的短水准形变观测资料。福建天马定点跨断层2003-2007年短水准观测资料的小波分析结果显示。分解到第七层的近似信号在极值和拐点出现前后往往有中强地震发生。由于福建省流动跨断层短水准测量为不定期观测,为取得等时间间隔的时序数据,我们采用三阶样条插值．在此基础上进行小波分析,发现分解到第三层的细节信号在台湾1999年“9．21”南投Ms7．6级地震前有明显的年变规律被破坏的现象． 相似文献
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武汉及周边地区数字化形变观测资料质量分析 总被引：1，自引：1，他引：0
对武汉及周边地区2007—2016年数字化形变观测资料进行长期背景跟踪分析,认为形变观测资料的日均变化幅度、M_2波潮汐因子均方差、长周期拟合相对噪声水平等指标变化稳定,资料可信度高,DSQ型水管倾斜仪观测精度与资料稳定性均优于SS-Y型铟瓦棒洞体应变仪。对武汉狮子山及黄石地震台形变观测数据典型变化进行系统总结与跟踪分析,发现主要受到人为干扰(调零、标定)、自然环境(降雨、气压)和观测系统故障等因素影响。 相似文献
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针对探地雷达信号处理和分析时小波基选取存在的问题,本文在分析探地雷达信号特点的基础上,首先从理论上讨论小波基的选取准则,然后再从实验角度进行对比、判别,认为在进行小波分解和重构时应该分别选择不同的小波基函数进行处理,这样可以保证重构信号的精确度,增强对信号的处理能力,从而也突破了以往分解与重构时都采用同一个小波基进行处理的做法.最后通过实际资料的处理,指出bior2.6小波基在进行雷达信号处理时效果最佳,不仅去噪彻底,而且能够保留有效信号的高频部分,提高信号的分辨率和信噪比,为后续解释工作打好了基础. 相似文献
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应用小波变换法可对不同频率成分进行分解的特性,选取db4小波基函数,尺度为1~8阶,对海拉尔地震台前兆资料进行分析研究。结果表明,应用小波变换方法可较容易地识别和剔除气压、场地环境干扰等影响海拉尔地震台伸缩仪观测资料精度的诸多因素,气压和场地环境干扰在小波细节分解d 3~d 6部分异常显著;小波变换法对同震效应的识别、提取和去除具有良好效果,远震在小波细节分解d 3~d 5部分异常显著,近震在小波细节分解d 1~d 3、d 6~d 8部分明显;爆破信号在小波分解d 1~d 8部分中均清晰可见,异常的宽度和幅度变化不大。因此,应用小波分解法能有效识别无法判断的干扰源。 相似文献
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The time-frequency analysis and anomaly detection of wavelet transformation make the method irresistibly advantageous in non-stable
signal processing. In the paper, the two characteristics are analyzed and demonstrated with synthetic signal. By applying
wavelet transformation to deformation data processing, we find that about 4 months before strong earthquakes, several deformation
stations near the epicenter received at the same time the abnormal signal with the same frequency and the period from several
days to more than ten days. The GPS observation stations near the epicenter all received the abnormal signal whose period
is from 3 months to half a year. These abnormal signals are possibly earthquake precursors.
Foundation item: Joint Seismological Science Foundation of China (604021) and National Natural Science Foundation of China (40074024). 相似文献
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Introduction The study mainly focused on tidal information, i.e. earth tide in the continuous deformationdata processing and analysis before. And the analysis methods used were specially for earth tideonly. As to the medium-long period and non-tidal information, fitting and filtering are not the bestmethods. Because they are not able to reflect the variation process of frequency information withtime and to distinguish and extract more earthquake information, although they can eliminateyea… 相似文献
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Mallat算法在数字地震信号压缩中的应用 总被引：1，自引：1，他引：0
王军 《地震地磁观测与研究》2017,38(5):133-138
地震台站多、数据采集量大,日产出数据量庞大,研究数字地震信号的压缩方法成为行业热门课题。尝试将Mallat算法应用于数字地震波形数据压缩。选取不同的小波分解函数,对不同类型的数字地震信号进行3—5层的小波分解,将得到的小波系数进行分层硬阈值重构运算,对原始信号和处理信号进行压缩。分析可知,Mallat算法压缩比更高,与原始信号相比,重构信号不失真、能量保留系数高。 相似文献
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Introduction The accurate gradual change style seismic phases identification is an older new question for discussion. Wavelet transform has good localization properties in time-frequency domain, and is a effective tool to analyze seismic signal which has been applied to detection of many kinds of signal, picture processing and other fields. When a kind of new signal stack up another kind of signal, more high frequency signal components will happen at onset time of new signal. Based on the wave… 相似文献
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基于小波分析的去噪方法在地震资料叠前处理中得到了广泛应用.本文主要介绍利用小波变换的分频特性来压制相干噪声.通过小波分频技术将叠前地震信号分解为不同频带,然后利用有效波和相干干扰波的频谱差异来区分有效信号和噪声,最后利用加权方法去掉不需要的噪声信息来达到去除相干噪声的目的.实际资料的处理结果表明：基于小波分频方法能很好地压制相干噪声,从而提高地震资料信噪比和分辨率. 相似文献
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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
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针对多年时序形变观测资料有效信息提取复杂的问题,对基于多核函数的滤波方法进行研究,得到以下有益结论:(1)当核函数指数为0.5,光滑因子为0.003时,10天及以上核点间隔的滤波模型单位权中误差最小;(2)核点间隔控制滤波信息频谱的高低,间隔越大频谱信息越低,反之则频谱信息越高;(3)因数据缺失部分造成核点减少,当连续减少2个以上时滤波失败,当连续减少2个时数据缺失部分滤波出现失真,当减少1个时滤波效果不受影响;(4)通过对GPS时序资料、定点形变时序资料和非构造形变时序资料的滤波应用,获取不同频谱的信息,验证了本文方法的稳定性和可靠性。 相似文献
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Denoising of full-tensor gravity-gradiometer data involves detailed information from field sources, especially the data mixed with high-frequency random noise. We present a denoising method based on the translation-invariant wavelet with mixed thresholding and adaptive threshold to remove the random noise and retain the data details. The novel mixed thresholding approach is devised to filter the random noise based on the energy distribution of the wavelet coefficients corresponding to the signal and random noise. The translationinvariant wavelet suppresses pseudo-Gibbs phenomena, and the mixed thresholding better separates the wavelet coefficients than traditional thresholding. Adaptive Bayesian threshold is used to process the wavelet coefficients according to the specific characteristics of the wavelet coefficients at each decomposition scale. A two-dimensional discrete wavelet transform is used to denoise gridded data for better computational efficiency. The results of denoising model and real data suggest that compared with Gaussian regional filter, the proposed method suppresses the white Gaussian noise and preserves the high-frequency information in gravity-gradiometer data. Satisfactory denoising is achieved with the translation-invariant wavelet. 相似文献