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 ...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.展开更多
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 functio...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.展开更多
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 sh...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.展开更多
基金supported by the National Basic Research Program of China (973 program, grant 2007CB209606) the National High Technology Research and Development Program of China (863 program, grant 2006AA09A102-09)
文摘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.
基金Supported by the National"863"Project(No.2014AA06A605)
文摘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.
基金This work was supported by Nation key R&D program(No.2016YFC060110305)Geological and mineral investigation and evaluation special project(No.DD20160160 and No.DD20160181).
文摘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.