Based on the Aki-Richards approximate equations for reflection coefficients and Bayes theorem, we developed an inversion method to estimate P- and S-wave velocity contrasts and density contrast from combined PP and PS...Based on the Aki-Richards approximate equations for reflection coefficients and Bayes theorem, we developed an inversion method to estimate P- and S-wave velocity contrasts and density contrast from combined PP and PS data. This method assumes that the parameters satisfy a normal distribution and introduces the covariance matrix to describe the degree of correlation between the parameters and thus to improve the inversion stability. Then, we suppose that the parameter sequence is subject to the Cauchy distribution and employs another matrix Q to describe the parameter sequence sparseness to improve the inversion result resolution. Tests on both synthetic and real multi-component data prove that this method is valid, efficient, more stable, and more accurate compared to methods using PP data only.展开更多
Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion met...Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.展开更多
Acid grasslands are threatened both by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment.In order to understand and quantify the effect of selected en...Acid grasslands are threatened both by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment.In order to understand and quantify the effect of selected environmental and land-use factors on the observed variation and changes in the vegetation of acid grasslands,large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model.The important sources of measurement and sampling uncertainties have been included using a hierarchical model structure.Furthermore,uncertainties associated with the measurement and sampling are separated from the process uncertainty,which is important when generating ecological predictions that may feed into local conservation management decisions.Generally,increasing atmospheric nitrogen deposition led to more grass-dominated acid grassland habitats at the expense of the cover of forbs.Sandy soils were relatively more acidic,and the effects of soil type on the vegetation include both direct effects of soil type and indirect effects mediated by the effect of soil type on soil pH.Both soil type and soil pH affected the vegetation of acid grasslands.Even though only a relatively small proportion of the temporal variation in cover was explained by the model,it would still be useful to quantify the uncertainties when using the model for generating local ecological predictions and adaptive management plans.展开更多
基金supported by the China Important National Science & Technology Specific Projects (Grant No. 2011ZX05019-008)the National Natural Science Foundation of China (Grant No. 40839901)
文摘Based on the Aki-Richards approximate equations for reflection coefficients and Bayes theorem, we developed an inversion method to estimate P- and S-wave velocity contrasts and density contrast from combined PP and PS data. This method assumes that the parameters satisfy a normal distribution and introduces the covariance matrix to describe the degree of correlation between the parameters and thus to improve the inversion stability. Then, we suppose that the parameter sequence is subject to the Cauchy distribution and employs another matrix Q to describe the parameter sequence sparseness to improve the inversion result resolution. Tests on both synthetic and real multi-component data prove that this method is valid, efficient, more stable, and more accurate compared to methods using PP data only.
基金supported by the National Nature Science Foundation Project(Nos.41604101 and U1562215)the National Grand Project for Science and Technology(No.2016ZX05024-004)+2 种基金the Natural Science Foundation of Shandong(No.BS2014NJ005)Science Foundation from SINOPEC Key Laboratory of Geophysics(No.33550006-15-FW2099-0027)the Fundamental Research Funds for the Central Universities
文摘Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.
文摘Acid grasslands are threatened both by agricultural intensification with nutrient addition and increased livestock densities as well as by land abandonment.In order to understand and quantify the effect of selected environmental and land-use factors on the observed variation and changes in the vegetation of acid grasslands,large-scale spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model.The important sources of measurement and sampling uncertainties have been included using a hierarchical model structure.Furthermore,uncertainties associated with the measurement and sampling are separated from the process uncertainty,which is important when generating ecological predictions that may feed into local conservation management decisions.Generally,increasing atmospheric nitrogen deposition led to more grass-dominated acid grassland habitats at the expense of the cover of forbs.Sandy soils were relatively more acidic,and the effects of soil type on the vegetation include both direct effects of soil type and indirect effects mediated by the effect of soil type on soil pH.Both soil type and soil pH affected the vegetation of acid grasslands.Even though only a relatively small proportion of the temporal variation in cover was explained by the model,it would still be useful to quantify the uncertainties when using the model for generating local ecological predictions and adaptive management plans.
