The absence of low-frequency information in seismic data is one of the most difficult problems in elastic full waveform inversion. Without low-frequency data, it is difficult to recover the long-wavelength components ...The absence of low-frequency information in seismic data is one of the most difficult problems in elastic full waveform inversion. Without low-frequency data, it is difficult to recover the long-wavelength components of subsurface models and the inversion converges to local minima. To solve this problem, the elastic envelope inversion method is introduced. Based on the elastic envelope operator that is capable of retrieving low- frequency signals hidden in multicomponent data, the proposed method uses the envelope of multicomponent seismic signals to construct a misfit function and then recover the long- wavelength components of the subsurface model. Numerical tests verify that the elastic envelope method reduces the inversion nonlinearity and provides better starting models for the subsequent conventional elastic full waveform inversion and elastic depth migration, even when low frequencies are missing in multicomponent data and the starting model is far from the true model. Numerical tests also suggest that the proposed method is more effective in reconstructing the long-wavelength components of the S-wave velocity model. The inversion of synthetic data based on the Marmousi-2 model shows that the resolution of conventional elastic full waveform inversion improves after using the starting model obtained using the elastic envelope method. Finally, the limitations of the elastic envelope inversion method are discussed.展开更多
Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized...Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.展开更多
The surface morphology of Ti-Mg supported catalyst and the polyethyleneparticles are studied using scanning electron microscope(SEM) technology. The results show thateithen the catalyst's surface or polymer partic...The surface morphology of Ti-Mg supported catalyst and the polyethyleneparticles are studied using scanning electron microscope(SEM) technology. The results show thateithen the catalyst's surface or polymer particle's surface is irregular and has fractalcharacteristics, which can be described by fractal parameter. The more interesting discovery is thatthe surface fractal dimension values of the polymer particles vary periodically with thepolymerization time. We call this phenomenon fractal evolution, which can be divided into the'revolution' stage and the 'evolution' stage. And then we present polymerization fractal growingmodel (PFGM), and successfully describe and/or predict the whole evolving process of thepolyethylene particle morphology under the different slurry polymerization (includingpre-polymerization) conditions without H_2.展开更多
文摘The absence of low-frequency information in seismic data is one of the most difficult problems in elastic full waveform inversion. Without low-frequency data, it is difficult to recover the long-wavelength components of subsurface models and the inversion converges to local minima. To solve this problem, the elastic envelope inversion method is introduced. Based on the elastic envelope operator that is capable of retrieving low- frequency signals hidden in multicomponent data, the proposed method uses the envelope of multicomponent seismic signals to construct a misfit function and then recover the long- wavelength components of the subsurface model. Numerical tests verify that the elastic envelope method reduces the inversion nonlinearity and provides better starting models for the subsequent conventional elastic full waveform inversion and elastic depth migration, even when low frequencies are missing in multicomponent data and the starting model is far from the true model. Numerical tests also suggest that the proposed method is more effective in reconstructing the long-wavelength components of the S-wave velocity model. The inversion of synthetic data based on the Marmousi-2 model shows that the resolution of conventional elastic full waveform inversion improves after using the starting model obtained using the elastic envelope method. Finally, the limitations of the elastic envelope inversion method are discussed.
基金supported by the National High-Tech Research and Development Program of China(863 Program)(No.2008AA093001)
文摘Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.
基金Supported by the National Natural Science Foundation of China (No. 29706010, No. 20203016).
文摘The surface morphology of Ti-Mg supported catalyst and the polyethyleneparticles are studied using scanning electron microscope(SEM) technology. The results show thateithen the catalyst's surface or polymer particle's surface is irregular and has fractalcharacteristics, which can be described by fractal parameter. The more interesting discovery is thatthe surface fractal dimension values of the polymer particles vary periodically with thepolymerization time. We call this phenomenon fractal evolution, which can be divided into the'revolution' stage and the 'evolution' stage. And then we present polymerization fractal growingmodel (PFGM), and successfully describe and/or predict the whole evolving process of thepolyethylene particle morphology under the different slurry polymerization (includingpre-polymerization) conditions without H_2.
