In marine seismic exploration,especially in deep-water and hard ocean-bottom cases,free-surface multiples are strongly developed.Compared with primary waves,the wider illumination aperture of the multiples is benefici...In marine seismic exploration,especially in deep-water and hard ocean-bottom cases,free-surface multiples are strongly developed.Compared with primary waves,the wider illumination aperture of the multiples is beneficial for high-resolution seismic imaging.In this study,by introducing a new compound source composed of primaries and free-surface multiples and by ignoring internal multiples,we derive a new linearized forward problem(free-surface-multiple prediction model)under a weak-scattering assumption(i.e.,first-order Born approximation).On the basis of the new linearized problem,we propose a joint inversion-imaging method by simultaneously using the primaries and free-surface multiples under the general framework of least square inversion.To eliminate the crosstalk artifacts introduced by the cross-correlation of multiples with different orders,we prove that the crosstalk artifacts can be gradually eliminated during the inversion if a proper step length is selected.Synthetic-andfield-data tests demonstrate the effectiveness of the proposed method.展开更多
One important model in handling the multivariate data is the varying-coefficient partially linear regression model. In this paper, the generalized likelihood ratio test is developed to test whether its coefficient fun...One important model in handling the multivariate data is the varying-coefficient partially linear regression model. In this paper, the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not. It is showed that the normalized proposed test follows asymptotically x2-distribution and the Wilks phenomenon under the null hypothesis, and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing. Some simulation studies illustrate that the test works well.展开更多
基金the sponsors of the WPI group for their financial supportfinancially supported by the National Key R&D Program of China (Grant Number: 2018YFA0702503, 2019YFC0312004)+2 种基金National Natural Science Foundation of China (Grant Number: 41774126)Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (ZJW-2019-04)National Science and Technology Major Project of China (Grant Number: 2016ZX05024-001, 2016ZX05006-002)。
文摘In marine seismic exploration,especially in deep-water and hard ocean-bottom cases,free-surface multiples are strongly developed.Compared with primary waves,the wider illumination aperture of the multiples is beneficial for high-resolution seismic imaging.In this study,by introducing a new compound source composed of primaries and free-surface multiples and by ignoring internal multiples,we derive a new linearized forward problem(free-surface-multiple prediction model)under a weak-scattering assumption(i.e.,first-order Born approximation).On the basis of the new linearized problem,we propose a joint inversion-imaging method by simultaneously using the primaries and free-surface multiples under the general framework of least square inversion.To eliminate the crosstalk artifacts introduced by the cross-correlation of multiples with different orders,we prove that the crosstalk artifacts can be gradually eliminated during the inversion if a proper step length is selected.Synthetic-andfield-data tests demonstrate the effectiveness of the proposed method.
基金supported by National Natural Science Foundation of China under Grant No.1117112the Fund of Shanxi Datong University under Grant No.2010K4+1 种基金the Doctoral Fund of Ministry of Education of China under Grant No.20090076110001National Statistical Science Research Major Program of China under Grant No.2011LZ051
文摘One important model in handling the multivariate data is the varying-coefficient partially linear regression model. In this paper, the generalized likelihood ratio test is developed to test whether its coefficient functions are varying or not. It is showed that the normalized proposed test follows asymptotically x2-distribution and the Wilks phenomenon under the null hypothesis, and its asymptotic power achieves the optimal rate of the convergence for the nonparametric hypotheses testing. Some simulation studies illustrate that the test works well.
