In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes consid...In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.展开更多
Seismic impedance inversion is a key technique for extracting reservoir information from seismic data.Traditional model-driven inversion methods often prove inadequate when dealing with complex reservoirs,which has le...Seismic impedance inversion is a key technique for extracting reservoir information from seismic data.Traditional model-driven inversion methods often prove inadequate when dealing with complex reservoirs,which has led to the growing adoption of data-driven sparse representation constrained inversion approaches.These methods typically employ redundant dictionary learning to adaptively extract feature information from logging data for effective inversion constraints.Although they excel in enhancing the vertical resolution and accuracy of inversion results,they still suffer from limitations such as high computational complexity and a lack of horizontal feature constraints,resulting in insufficient horizontal continuity,overall accuracy,and computational efficiency.To address these issues,this paper proposes a fast sparse representation-based impedance inversion method using online adaptive reservoir features.Based on logging and seismic data,the method employs an online dictionary learning strategy to adaptively extract both vertical and horizontal reservoir characteristics for sparse representation inversion constraints.To further improve computational efficiency,orthogonal dictionary learning is introduced to reduce computational costs.Ultimately,an impedance inversion method is developed based on online orthogonal dictionary learning that simultaneously imposes adaptive joint constraints on both vertical and horizontal features.Experimental results demonstrate that the proposed method not only achieves high accuracy and high-resolution inversion results but also offers significant advantages in computational efficiency.展开更多
基金supported by the National Natural Science Foundation of China(61771372,61771367,62101494)the National Outstanding Youth Science Fund Project(61525105)+1 种基金Shenzhen Science and Technology Program(KQTD20190929172704911)the Aeronautic al Science Foundation of China(2019200M1001)。
文摘In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.
文摘Seismic impedance inversion is a key technique for extracting reservoir information from seismic data.Traditional model-driven inversion methods often prove inadequate when dealing with complex reservoirs,which has led to the growing adoption of data-driven sparse representation constrained inversion approaches.These methods typically employ redundant dictionary learning to adaptively extract feature information from logging data for effective inversion constraints.Although they excel in enhancing the vertical resolution and accuracy of inversion results,they still suffer from limitations such as high computational complexity and a lack of horizontal feature constraints,resulting in insufficient horizontal continuity,overall accuracy,and computational efficiency.To address these issues,this paper proposes a fast sparse representation-based impedance inversion method using online adaptive reservoir features.Based on logging and seismic data,the method employs an online dictionary learning strategy to adaptively extract both vertical and horizontal reservoir characteristics for sparse representation inversion constraints.To further improve computational efficiency,orthogonal dictionary learning is introduced to reduce computational costs.Ultimately,an impedance inversion method is developed based on online orthogonal dictionary learning that simultaneously imposes adaptive joint constraints on both vertical and horizontal features.Experimental results demonstrate that the proposed method not only achieves high accuracy and high-resolution inversion results but also offers significant advantages in computational efficiency.
