地震随机噪声衰减的改进经验模态分解法研究及应用

为解决地震随机噪声衰减问题,引入经验模态分解法,但该方法存在严重的端点效应问题并使得结果存在较大的误差。因此提出通过寻找包络线极大值点并进行平行延拓从而修正边界极值点的方法来对经验模态分解法进行改进。通过仿真实验和理论模型的测试,发现改进后的方法能很好地抑制端点效应,分解更精准,在对地震随机噪声进行衰减同时能更好地保护构造信息。

一种改进微地震数据极化滤波性能的偏振特征函数设计

地震数据极化滤波广泛应用于地震数据处理中噪声压制、震相识别和波形分离且效果很好。在时间域极化滤波中,通常利用协方差矩阵的特征值构造偏振特征函数来设计极化滤波器。针对传统极化滤波器对有效信号和噪声识别不清晰的问题,总结分析了当前应用较为广泛的10种偏振特征函数,讨论了这些函数对地震噪声信号的表征能力。在分析偏振特征函数优、缺点的基础上,使用特征值构造一种新的偏振特征函数表达式,基于该特征函数设计的极化滤波器能够提高地震有效信号和噪声的识别精度,更有效实现噪声衰减。利用合成和实测的微地震数据验证了不同偏振特征函数对极化滤波器性能的影响。结果表明,采用新构建的偏振特征函数设计的滤波器,能有效识别噪声数据段,对初始相位影响较小且滤波后地震数据的信噪比(SNR)、均方根误差(RMSE)和互相关归一化系数(NCC)均优于其它偏振特征函数设计的滤波器。处理后的地震信号矢端曲线图能更好地反映地震信号的偏振方向,更有利于确定微地震初至波的极化方向。

宽频带流动地震台站中地震计浅井式安装研究

在井下地震观测中,各种地表背景噪声具有显著衰减作用,针对中国地震科学台阵现有大量非专用浅井式设计的guralp CMg系列地震计,以及目前多个新获批项目在高背景噪声区域的观测需求,设计一种野外实用浅井观测方式,在北京国家地球观象台进行不同深度、不同井壁材质的观测对比实验,分析研究观测数据的背景噪声功率谱密度,结果表明,随着浅井深度的增加,地震背景噪声在垂直、水平向得到改善,改善程度随频率变化有所不同。分析认为,在华北现有地质环境下,6 m浅井是较为经济的观测方式,性价比高、占地小、施工简便,可用于后续多个项目的宽频带流动地震台阵观测。

Seismic noise attenuation using nonstationary polynomial fitting

We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting （NPF）, which can estimate coherent components with amplitude variation along the event. The NPF with time-varying coefficients can adaptively estimate the coherent components. The smoothness of the polynomial coefficients is controlled by shaping regularization. The signal is coherent along the offset axis in a common midpoint （CMP） gather after normal moveout （NMO）. We use NPF to estimate the effective signal and thereby to attenuate the random noise. For radial events-like noise such as ground roll, we first employ a radial trace （RT） transform to transform the data to the time-velocity domain. Then the NPF is used to estimate coherent noise in the RT domain. Finally, the coherent noise is adaptively subtracted from the noisy dataset. The proposed method can effectively estimate coherent noise with amplitude variations along the event and there is no need to propose that noise amplitude is constant. Results of synthetic and field data examples show that, compared with conventional methods such as stationary polynomial fitting and low cut filters, the proposed method can effectively suppress seismic noise and preserve the signals.

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.

Experiments on excitation and data processing of low-frequency vibroseis in permafrost area of the tibetan plateau

Few seismic exploration work was carried out in Tibetan Plateau due to the characteristics of alpine hypoxia and harsh environmental protection needs.Complex near surface geological conditions,especially the signal shielding and static correction of permafrost make the quality of seismic data is not ideal,the signal to noise ratio(SNR)is low,and deep target horizon imaging is difficult.These data cannot provide high quality information for oil and gas geological survey and structural sedimentary research in the area.To solve the issue of seismic exploration in Tibetan Plateau,this test used low frequency vibroseis wide-line and high-density acquisition scheme.In view of the actual situation of the study area,the terrain,the source and the diff erent observation system were simulated,and the processing technique was adopted to improve the quality of seismic data.Low-frequency components with a minimum of 1.5Hz of vibroseis ensure the deep geological target imaging quality in the area,the seismic profi le wave group is clear,and the SNR is relatively high,which can meet the needs of oil and gas exploration.Seismic data can provide the support for the development of oil and gas survey in the Tibet plateau.