Data reconstruction is a crucial step in seismic data preprocessing.To improve reconstruction speed and save memory,the commonly used three-dimensional(3D)seismic data reconstruction method divides the missing data in...Data reconstruction is a crucial step in seismic data preprocessing.To improve reconstruction speed and save memory,the commonly used three-dimensional(3D)seismic data reconstruction method divides the missing data into a series of time slices and independently reconstructs each time slice.However,when this strategy is employed,the potential correlations between two adjacent time slices are ignored,which degrades reconstruction performance.Therefore,this study proposes the use of a two-dimensional curvelet transform and the fast iterative shrinkage thresholding algorithm for data reconstruction.Based on the significant overlapping characteristics between the curvelet coefficient support sets of two adjacent time slices,a weighted operator is constructed in the curvelet domain using the prior support set provided by the previous reconstructed time slice to delineate the main energy distribution range,eff ectively providing prior information for reconstructing adjacent slices.Consequently,the resulting weighted fast iterative shrinkage thresholding algorithm can be used to reconstruct 3D seismic data.The processing of synthetic and field data shows that the proposed method has higher reconstruction accuracy and faster computational speed than the conventional fast iterative shrinkage thresholding algorithm for handling missing 3D seismic data.展开更多
The theory of increasing and convex-along-rays(ICAR)functions defined on a convex cone in a real locally convex topological vector space X was already well developed.In this paper,we first examine abstract convexity o...The theory of increasing and convex-along-rays(ICAR)functions defined on a convex cone in a real locally convex topological vector space X was already well developed.In this paper,we first examine abstract convexity of increasing plus-convex-along-rays(IPCAR)functions defined on a real normed linear space X.We also study,for this class of functions,some concepts of abstract convexity,such as support sets and subdifferentials.Finally,as an application,we characterize the maximal elements of the support set of strictly IPCAR functions and give optimality conditions for the global minimum of the difference between two IPCAR functions.展开更多
Indirect association is a high level relationship between items and frequent item sets in data. There are many potential applications for indirect associations, such as database marketing, intelligent data analysis, w...Indirect association is a high level relationship between items and frequent item sets in data. There are many potential applications for indirect associations, such as database marketing, intelligent data analysis, web -log analysis, recommended system, etc. Existing indirect association mining algorithms are mostly based on the notion of post - processing of discovery of frequent item sets. In the mining process, all frequent item sets need to be generated first, and then they are fihered and joined to form indirect associations. We have presented an indirect association mining algorithm （NIA） based on anti -monotonicity of indirect associations whereas k candidate indirect associations can be generated directly from k - 1 candidate indirect associations, without all frequent item sets generated. We also use the frequent itempair support matrix to reduce the time and memory space needed by the algorithm. In this paper, a novel algorithm （NIA2） is introduced based on the generation of indirect association patterns between itempairs through one item mediator sets from frequent itempair support matrix. A notion of mediator set support threshold is also presented. NIA2 mines indirect association patterns directly from the dataset, without generating all frequent item sets. The frequent itempair support matrix and the notion of using tm as the support threshold for mediator sets can significantly reduce the cost of joint operations and the search process compared with existing algorithms. Results of experiments on a real - word web log dataset have proved NIA2 one order of magnitude faster than existing algorithms.展开更多
Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the ...Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.展开更多
基金National Natural Science Foundation of China under Grant 42304145Jiangxi Provincial Natural Science Foundation under Grant 20242BAB26051,20242BAB25191 and 20232BAB213077+1 种基金Foundation of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing under Grant 2024QZ-TD-13Open Fund(FW0399-0002)of SINOPEC Key Laboratory of Geophysics。
文摘Data reconstruction is a crucial step in seismic data preprocessing.To improve reconstruction speed and save memory,the commonly used three-dimensional(3D)seismic data reconstruction method divides the missing data into a series of time slices and independently reconstructs each time slice.However,when this strategy is employed,the potential correlations between two adjacent time slices are ignored,which degrades reconstruction performance.Therefore,this study proposes the use of a two-dimensional curvelet transform and the fast iterative shrinkage thresholding algorithm for data reconstruction.Based on the significant overlapping characteristics between the curvelet coefficient support sets of two adjacent time slices,a weighted operator is constructed in the curvelet domain using the prior support set provided by the previous reconstructed time slice to delineate the main energy distribution range,eff ectively providing prior information for reconstructing adjacent slices.Consequently,the resulting weighted fast iterative shrinkage thresholding algorithm can be used to reconstruct 3D seismic data.The processing of synthetic and field data shows that the proposed method has higher reconstruction accuracy and faster computational speed than the conventional fast iterative shrinkage thresholding algorithm for handling missing 3D seismic data.
基金the Mahani Mathematical Research Center,Iran,grant no:97/3267。
文摘The theory of increasing and convex-along-rays(ICAR)functions defined on a convex cone in a real locally convex topological vector space X was already well developed.In this paper,we first examine abstract convexity of increasing plus-convex-along-rays(IPCAR)functions defined on a real normed linear space X.We also study,for this class of functions,some concepts of abstract convexity,such as support sets and subdifferentials.Finally,as an application,we characterize the maximal elements of the support set of strictly IPCAR functions and give optimality conditions for the global minimum of the difference between two IPCAR functions.
文摘Indirect association is a high level relationship between items and frequent item sets in data. There are many potential applications for indirect associations, such as database marketing, intelligent data analysis, web -log analysis, recommended system, etc. Existing indirect association mining algorithms are mostly based on the notion of post - processing of discovery of frequent item sets. In the mining process, all frequent item sets need to be generated first, and then they are fihered and joined to form indirect associations. We have presented an indirect association mining algorithm （NIA） based on anti -monotonicity of indirect associations whereas k candidate indirect associations can be generated directly from k - 1 candidate indirect associations, without all frequent item sets generated. We also use the frequent itempair support matrix to reduce the time and memory space needed by the algorithm. In this paper, a novel algorithm （NIA2） is introduced based on the generation of indirect association patterns between itempairs through one item mediator sets from frequent itempair support matrix. A notion of mediator set support threshold is also presented. NIA2 mines indirect association patterns directly from the dataset, without generating all frequent item sets. The frequent itempair support matrix and the notion of using tm as the support threshold for mediator sets can significantly reduce the cost of joint operations and the search process compared with existing algorithms. Results of experiments on a real - word web log dataset have proved NIA2 one order of magnitude faster than existing algorithms.
基金supported by the National Natural Science Foundation of China(61771258)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX 210749)。
文摘Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.
