The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the tr...The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.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
At present,a large number of historic buildings on campus are lack of energy-saving measures from the design time,especially the dormitories. And the reconstruction mostly focuses on functional reorganization or furni...At present,a large number of historic buildings on campus are lack of energy-saving measures from the design time,especially the dormitories. And the reconstruction mostly focuses on functional reorganization or furniture replacement. However,energy efficiency design has not been paid enough attention,which leads to the high building energy consumption and the harsh physical environment. Based on the analysis of climatic characteristics of Chongqing area,taking Dormitory Six on campus B of Chongqing University as an example,the reconstruction of rooms on the top floor and West end are focused to guarantee the equal benefits of the dormitory environment. Through the simulation analysis of the software, on the basis of energy saving reconstruction of common maintenance structure,this thesis discusses the energy saving reconstruction methods and strategies of the existing campus dormitories,which are more suitable for the existing campus dormitories in Chongqing area.展开更多
Living in the dormitory is usually the requirement for university students who live far from the university. The medical disorder occurring at night among the university students is rarely reported. The acute problem ...Living in the dormitory is usually the requirement for university students who live far from the university. The medical disorder occurring at night among the university students is rarely reported. The acute problem due to accident is a topic of interest. Here, the authors summarize on pattern of accident at night of university students in a dormitory.展开更多
基金supported by The National 973 program (No. 2007 CB209505)Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601)RIPED Youth Innovation Foundation (No. 2010-A-26-01)
文摘The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of fixed transform bases. In this paper, we combine dictionary learning with total variation （TV） minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.
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.
文摘At present,a large number of historic buildings on campus are lack of energy-saving measures from the design time,especially the dormitories. And the reconstruction mostly focuses on functional reorganization or furniture replacement. However,energy efficiency design has not been paid enough attention,which leads to the high building energy consumption and the harsh physical environment. Based on the analysis of climatic characteristics of Chongqing area,taking Dormitory Six on campus B of Chongqing University as an example,the reconstruction of rooms on the top floor and West end are focused to guarantee the equal benefits of the dormitory environment. Through the simulation analysis of the software, on the basis of energy saving reconstruction of common maintenance structure,this thesis discusses the energy saving reconstruction methods and strategies of the existing campus dormitories,which are more suitable for the existing campus dormitories in Chongqing area.
文摘Living in the dormitory is usually the requirement for university students who live far from the university. The medical disorder occurring at night among the university students is rarely reported. The acute problem due to accident is a topic of interest. Here, the authors summarize on pattern of accident at night of university students in a dormitory.
