Seismic inversion,such as velocity and impedance,is an ill-posed problem.To solve this problem,swarm intelligence(SI)algorithms have been increasingly applied as the global optimization approach,such as differential e...Seismic inversion,such as velocity and impedance,is an ill-posed problem.To solve this problem,swarm intelligence(SI)algorithms have been increasingly applied as the global optimization approach,such as differential evolution(DE)and particle swarm optimization(PSO).Based on the well logs,the sparse probability distribution(PD)of the reflectivity distribution is spatial stationarity.Therefore,we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity,which helps to provide more accurate potential solutions for the seismic inversion.In the proposed scheme,as two key operations,the creating of probability density function library and probability transformation are inserted into standard SI algorithms.In particular,two targeted DE-PD and PSO-PD algorithms are implemented.Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO.In particular,the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion.Overall,the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.展开更多
Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dyna...Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.展开更多
This paper studies variable selection using the penalized likelihood method for dis-tributed sparse regression with large sample size n under a limited memory constraint.This is a much needed research problem to be so...This paper studies variable selection using the penalized likelihood method for dis-tributed sparse regression with large sample size n under a limited memory constraint.This is a much needed research problem to be solved in the big data era.A naive divide-and-conquer method solving this problem is to split the whole data into N parts and run each part on one of N machines,aggregate the results from all machines via averaging,andfinally obtain the selected variables.However,it tends to select more noise variables,and the false discovery rate may not be well controlled.We improve it by a special designed weighted average in aggregation.Although the alternating direction method of multiplier can be used to deal with massive data in the literature,our proposed method reduces the computational burden a lot and performs better by mean square error in most cases.Theoretically,we establish asymptotic properties of the resulting estimators for the likelihood models with a diverging number of parame-ters.Under some regularity conditions,we establish oracle properties in the sense that our distributed estimator shares the same asymptotic efficiency as the estimator based on the full sample.Computationally,a distributed penalized likelihood algorithm is proposed to refine the results in the context of general likelihoods.Furthermore,the proposed method is evaluated by simulations and a real example.展开更多
Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and ...Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment(A-DSBA)to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism(SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3 D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm(running on eight nodes with 48 cores) is up to41 times that of the serial SBA(running on a single node).展开更多
基金supported by the National Natural Science Foundation of China(41974137).
文摘Seismic inversion,such as velocity and impedance,is an ill-posed problem.To solve this problem,swarm intelligence(SI)algorithms have been increasingly applied as the global optimization approach,such as differential evolution(DE)and particle swarm optimization(PSO).Based on the well logs,the sparse probability distribution(PD)of the reflectivity distribution is spatial stationarity.Therefore,we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity,which helps to provide more accurate potential solutions for the seismic inversion.In the proposed scheme,as two key operations,the creating of probability density function library and probability transformation are inserted into standard SI algorithms.In particular,two targeted DE-PD and PSO-PD algorithms are implemented.Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO.In particular,the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion.Overall,the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.
基金supported by the Innovation Project of Graduate Students of Jiangsu Province, China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240, No. 61201160, No. 61172118+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 12KJB510019the Science and Technology Research Program of Hubei Provincial Department of Education under Grants No. D20121408, No. D20121402the Program for Research Innovation of Nanjing Institute of Technology Project under Grant No. CKJ20110006
文摘Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.
基金supported by NSFC(11871263)NSF grant of Guangdong Province of China(No.2017A030313012).
文摘This paper studies variable selection using the penalized likelihood method for dis-tributed sparse regression with large sample size n under a limited memory constraint.This is a much needed research problem to be solved in the big data era.A naive divide-and-conquer method solving this problem is to split the whole data into N parts and run each part on one of N machines,aggregate the results from all machines via averaging,andfinally obtain the selected variables.However,it tends to select more noise variables,and the false discovery rate may not be well controlled.We improve it by a special designed weighted average in aggregation.Although the alternating direction method of multiplier can be used to deal with massive data in the literature,our proposed method reduces the computational burden a lot and performs better by mean square error in most cases.Theoretically,we establish asymptotic properties of the resulting estimators for the likelihood models with a diverging number of parame-ters.Under some regularity conditions,we establish oracle properties in the sense that our distributed estimator shares the same asymptotic efficiency as the estimator based on the full sample.Computationally,a distributed penalized likelihood algorithm is proposed to refine the results in the context of general likelihoods.Furthermore,the proposed method is evaluated by simulations and a real example.
基金Project supported by the National Natural Science Foundation of China(Nos.U1435219,U1435222,and 61572515)the National Key R&D Program of China(No.2016YFB0200401)the Major Research Plan of the National Key R&D Program of China(No.2016YFC0901600)
文摘Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment(A-DSBA)to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism(SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3 D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm(running on eight nodes with 48 cores) is up to41 times that of the serial SBA(running on a single node).
