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lp norm inverse spectral decomposition and its multi-sparsity fusion interpretation

Applied Geophysics 2021年第4期18卷 569-578,595页

作者： Li Sheng-Jun Wang Tie-Yi Gao Jian-Hu Liu Bing-Yang Gui Jin-Yong Wang Hong-Qiu Research Institute of Petroleum Exploration and Development-Northwest PetrochinaLanzhou 730020GansuChina. State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumChangpingBeijing 102249China. SINOPEC Petroleum Exploration&Production Research Institute Beijing 100083China.

Spectral decomposition has been widely used in the detection and identifi cation of underground anomalous features(such as faults,river channels,and karst caves).However,the conventional spectral decomposition method is restrained by the window function,and hence,it mostly has low time–frequency focusing and resolution,thereby hampering the fi ne interpretation of seismic *** solve this problem,we investigated the sparse inverse spectral decomposition constrained by the lp norm(0norm constrained inverse spectral attribute interpretation method based on multiresolution time–frequency feature *** comprehensively analyzing the time–frequency spectrum results constrained by the diff erent p-norms,we can obtain more refined interpretation results than those obtained by the traditional strategy,which incorporates a single norm ***,the proposed strategy was applied to the processing and interpretation of actual three-dimensional seismic data for a study area covering about 230 km^(2) in western *** results reveal that the surface water system in this area is characterized by stepwise convergence from a higher position in the north(a buried hill)toward the south and by the development of *** thus demonstrated that the proposed method has huge application potential in seismic interpretation.

关键词： Spectral decomposition lp norm multiresolution time–frequency feature fusion seismic interpretation fi ne interpretation

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Convergence Analysis of PIβDα-type Fractional-order Iterative Learning Control in the Sense of lp norm

Convergence Analysis of PIβDα-type Fractional-order Iterat...

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第36届中国控制会议

作者： Kejun Zhang Guohua Peng School of Natural and Applied Sciences Northwestern Polytechnical University School of Math and Physical Sciences Xuzhou Institute of Technology

The convergence of PID-type fractional-order iterative learning control(FOILC) is discussed for a class of fractional-order linear time-invariant system in the sense of L ***,by taking advantage of the generalized Young inequality of convolution integral,the sufficient conditions for the monotone convergence of first and second-order PID-type control laws are analyzed in the sense of L norm,and generalizes to the convergence condition of iV-th-order PID-type control *** the convergence speeds of first and second-order PID-type ILC are *** analysis indicates that the convergence is determined not only by the learning gains of control law,but also by the attributes of the system *** simulation results validate the correctness of the theory and the feasibility of the proposed control law.

关键词： Iterative learning control Fractional-order lp norm Convergence

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lp shape deformation

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Science China(Information Sciences) 2012年第5期55卷 983-993页

作者： GAO Lin1,ZHANG GuoXin 1 & LAI YuKun 2 1 Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China 2 School of Computer Science and Informatics,Cardiff University,Wales CF24 3AA,UK Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Informatics Cardiff University Wales UK

Shape deformation is a fundamental tool in geometric *** methods consider preserving local details by minimizing some energy functional measuring local distortions in the L 2 *** strategy distributes distortions quite uniformly to all the vertices and penalizes ***,there is no unique answer for a natural deformation as it depends on the nature of the *** by recent sparse signal reconstruction work with non L 2 norm,we introduce general L p norms to shape deformation;the positive parameter p provides the user with a flexible control over the distribution of unavoidable *** with the traditional L 2 norm,using smaller p,distortions tend to be distributed to a sparse set of vertices,typically in feature regions,thus making most areas less distorted and structures better *** the other hand,using larger p tends to distribute distortions more evenly across the whole *** flexibility is often desirable as it mimics objects made up with different *** specifying varying p over the shape,more flexible control can be *** demonstrate the effectiveness of the proposed algorithm with various examples.

关键词： shape deformation lp norm geometric modeling

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