Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equatio...Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.展开更多
Fields that employ artificial neural networks(ANNs)have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence.ANN has been adopted widely a...Fields that employ artificial neural networks(ANNs)have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence.ANN has been adopted widely and put into practice by research-ers in light of increasing concerns over ecological issues such as global warming,frequent El Nio-Southern Oscillation(ENSO)events,and atmospheric circulation anomalies.Limitations exist and there is a potential risk for misuse in that ANN model pa-rameters require typically higher overall sensitivity,and the chosen network structure is generally more dependent upon individ-ual experience.ANNs,however,are relatively accurate when used for short-term predictions;despite global climate change re-search favoring the effects of interactions as the basis of study and the preference for long-term experimental research.ANNs remain a better choice than many traditional methods when dealing with nonlinear problems,and possesses great potential for the study of global climate change and ecological issues.ANNs can resolve problems that other methods cannot.This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available.It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.展开更多
Based on the eigensystem {λj,φjof -Δ, the multiple solutions for nonlinear problem Δu+f(u)=0 in Ω,u=0 onαΩ are approximated. A new search-extension method (SEM), which consists of three steps in three level sub...Based on the eigensystem {λj,φjof -Δ, the multiple solutions for nonlinear problem Δu+f(u)=0 in Ω,u=0 onαΩ are approximated. A new search-extension method (SEM), which consists of three steps in three level subspaces, is proposed. Numerical simulations for several typical nonlinear cases, i.e. f(u)=u3,u2(u-p),u2(u2-p), are completed and some conjectures are presented.展开更多
基金supported by the Fundamental Research Funds for the Central Universities of China(No.2652017438)the National Science and Technology Major Project of China(No.2016ZX05003-003)
文摘Multiwave seismic technology promotes the application of joint PP–PS amplitude versus offset (AVO) inversion;however conventional joint PP–PS AVO inversioan is linear based on approximations of the Zoeppritz equations for multiple iterations. Therefore the inversion results of P-wave, S-wave velocity and density exhibit low precision in the faroffset;thus, the joint PP–PS AVO inversion is nonlinear. Herein, we propose a nonlinear joint inversion method based on exact Zoeppritz equations that combines improved Bayesian inference and a least squares support vector machine (LSSVM) to solve the nonlinear inversion problem. The initial parameters of Bayesian inference are optimized via particle swarm optimization (PSO). In improved Bayesian inference, the optimal parameter of the LSSVM is obtained by maximizing the posterior probability of the hyperparameters, thus improving the learning and generalization abilities of LSSVM. Then, an optimal nonlinear LSSVM model that defi nes the relationship between seismic refl ection amplitude and elastic parameters is established to improve the precision of the joint PP–PS AVO inversion. Further, the nonlinear problem of joint inversion can be solved through a single training of the nonlinear inversion model. The results of the synthetic data suggest that the precision of the estimated parameters is higher than that obtained via Bayesian linear inversion with PP-wave data and via approximations of the Zoeppritz equations. In addition, results using synthetic data with added noise show that the proposed method has superior anti-noising properties. Real-world application shows the feasibility and superiority of the proposed method, as compared with Bayesian linear inversion.
基金supported by the Introducing Advanced Technology Program(948Pro-gram)(2010-4-03)the New Century Excellent Talents Program from the Ministry of Education,China(NCET-06-0715)+1 种基金the Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Provincethe Furong Scholar Program
文摘Fields that employ artificial neural networks(ANNs)have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence.ANN has been adopted widely and put into practice by research-ers in light of increasing concerns over ecological issues such as global warming,frequent El Nio-Southern Oscillation(ENSO)events,and atmospheric circulation anomalies.Limitations exist and there is a potential risk for misuse in that ANN model pa-rameters require typically higher overall sensitivity,and the chosen network structure is generally more dependent upon individ-ual experience.ANNs,however,are relatively accurate when used for short-term predictions;despite global climate change re-search favoring the effects of interactions as the basis of study and the preference for long-term experimental research.ANNs remain a better choice than many traditional methods when dealing with nonlinear problems,and possesses great potential for the study of global climate change and ecological issues.ANNs can resolve problems that other methods cannot.This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available.It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.
基金This work was supported by the Special Funds of State Major Basic Research Projects(Grant No.G1999032804)the National Natural Science Foundation of China(Grant No.19871027)Mathematical Tianyuan Youth Foundation of the National Natural Science Foundation of China(No.10226016).
文摘Based on the eigensystem {λj,φjof -Δ, the multiple solutions for nonlinear problem Δu+f(u)=0 in Ω,u=0 onαΩ are approximated. A new search-extension method (SEM), which consists of three steps in three level subspaces, is proposed. Numerical simulations for several typical nonlinear cases, i.e. f(u)=u3,u2(u-p),u2(u2-p), are completed and some conjectures are presented.
