In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orie...In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio （SNR）, the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands （dominant, low, and high frequencies） separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.展开更多
A novel approach employs the principles of medical image analysis using Wavelet Transform (WT) and Difference Peak Signal-to-Noise Ratio (ΔPSNR). Both techniques are combined as a function of different decomposing le...A novel approach employs the principles of medical image analysis using Wavelet Transform (WT) and Difference Peak Signal-to-Noise Ratio (ΔPSNR). Both techniques are combined as a function of different decomposing levels of wavelets and various image search through and slicing levels, which is implemented under MATLAB environment. In this new approach, the structural change due to damage in the component or the presence of foreign bodies appearing in an image taken for a specific structure is uncovered with its extent determined after applying the search through algorithm. Such alteration of the composite structure, which could be masked by the presence of noise, is accounted for using combined WT and PSNR. Effect of Artifacts and Blurring caused by different wavelet types is investigated before choosing an appropriate wavelet, namely Sym8. This new approach, which also reduces the required layers of search within an image, produces a pattern matrix per damaged area and is an excellent way in tracing and modeling damage in structures with ability to predict effects of further damage on components and further application to artificial limbs that could suffer damage and affect users mobility.展开更多
基金supported by China National Petroleum Corporation (CNPC) Innovation Fund (Grant No.07E1019)Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (Grant No.200804251502)
文摘In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio （SNR）, the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands （dominant, low, and high frequencies） separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.
文摘A novel approach employs the principles of medical image analysis using Wavelet Transform (WT) and Difference Peak Signal-to-Noise Ratio (ΔPSNR). Both techniques are combined as a function of different decomposing levels of wavelets and various image search through and slicing levels, which is implemented under MATLAB environment. In this new approach, the structural change due to damage in the component or the presence of foreign bodies appearing in an image taken for a specific structure is uncovered with its extent determined after applying the search through algorithm. Such alteration of the composite structure, which could be masked by the presence of noise, is accounted for using combined WT and PSNR. Effect of Artifacts and Blurring caused by different wavelet types is investigated before choosing an appropriate wavelet, namely Sym8. This new approach, which also reduces the required layers of search within an image, produces a pattern matrix per damaged area and is an excellent way in tracing and modeling damage in structures with ability to predict effects of further damage on components and further application to artificial limbs that could suffer damage and affect users mobility.
