Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learn...Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.展开更多
In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inve...In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.展开更多
Aims Carbon(C)sequestration in terrestrial ecosystems is strongly regulated by nitrogen(N)processes.However,key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well qu...Aims Carbon(C)sequestration in terrestrial ecosystems is strongly regulated by nitrogen(N)processes.However,key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well quantified.Methods Here,we used a Bayesian probabilistic inversion approach to estimate 14 target parameters related to ecosystem C and N interactions from 19 datasets obtained from Duke Forests under ambient and elevated carbon dioxide(CO_(2)).Important FindingsOur results indicated that 8 of the 14 target parameters,such as C:N ratios in most ecosystem compartments,plant N uptake and external N input,were well constrained by available datasets whereas the others,such as N allocation coefficients,N loss and the initial value of mineral N pool were poorly constrained.Our analysis showed that elevated CO_(2)led to the increases in C:N ratios in foliage,fine roots and litter.Moreover,elevated CO_(2)stimulated plant N uptake and increased ecosystem N capital in Duke Forests by 25.2 and 8.5%,respectively.In addition,elevated CO_(2)resulted in the decrease of C exit rates(i.e.increases in C residence times)in foliage,woody biomass,structural litter and passive soil organic matter,but the increase of C exit rate in fine roots.Our results demonstrated that CO_(2)enrichment substantially altered key parameters in determining terrestrial C and N interactions,which have profound implications for model improvement and predictions of future C sequestration in terrestrial ecosystems in response to global change.展开更多
The delineation of shale oil sweet spots is a crucial step in the exploration of shale oil reservoirs.A single attribute such as total organic carbon(TOC)is conventionally used to evaluate the sweet spots of shale oil...The delineation of shale oil sweet spots is a crucial step in the exploration of shale oil reservoirs.A single attribute such as total organic carbon(TOC)is conventionally used to evaluate the sweet spots of shale oil.This study proposes a probabilistic Fisher discriminant approach for estimating shale oil sweet spots,in which the probabilistic method and Gaussian mixture model are incorporated.Statistical features of shale oil facies are obtained based on the well log interpretation of the samples.Several key parameters of shale oil are projected to data sets with low dimensions in each shale oil facies.Furthermore,the posterior distribution of different shale oil facies is built based on the classification of each shale oil facies.Various key physical parameters of shale oil facies are inversed by the Bayesian method,and important elastic properties are extracted from the elastic impedance inversion(EVA-DSVD method).The method proposed in this paper has been successfully used to delineate the sweet spots of shale oil reservoirs with multiple attributes from the real pre-stack seismic data sets and is validated by the well log data.展开更多
Aims Terrestrial ecosystem carbon(C)uptake is remarkably regulated by nitrogen(N)availability in the soil.However,the coupling of C and N cycles,as reflected by C:N ratios in different components,has not been well exp...Aims Terrestrial ecosystem carbon(C)uptake is remarkably regulated by nitrogen(N)availability in the soil.However,the coupling of C and N cycles,as reflected by C:N ratios in different components,has not been well explored in response to climate change.Methods Here,we applied a data assimilation approach to assimilate 14 datasets collected from a warming experiment in an alpine meadow in China into a grassland ecosystem model.We attempted to evaluate how experimental warming affects C and N coupling as indicated by constrained parameters under ambient and warming treatments separately.Important Findings The results showed that warming increased soil N availability with decreased C:N ratio in soil labile C pool,leading to an increase in N uptake by plants.Nonetheless,C input to leaf increased more than N,leading to an increase and a decrease in the C:N ratio in leaf and root,respectively.Litter C:N ratio was decreased due to the increased N immobilization under high soil N availability or warming-accelerated decomposition of litter mass.Warming also increased C:N ratio of slow soil organic matter pool,suggesting a greater soil C sequestration potential.As most models usually use a fixed C:N ratio across different environments,the divergent shifts of C:N ratios under climate warming detected in this study could provide a useful benchmark for model parameterization and benefit models to predict C-N coupled responses to future climate change.展开更多
基金the sponsorship of Shandong Province Foundation for Laoshan National Laboratory of Science and Technology Foundation(LSKJ202203400)National Natural Science Foundation of China(42174139,42030103)Science Foundation from Innovation and Technology Support Program for Young Scientists in Colleges of Shandong Province and Ministry of Science and Technology of China(2019RA2136)。
文摘Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.
基金provided by the National Science and Technology Major Project(No.2011ZX05004-004)China National Petroleum Corporation Key Projects(No.2014E2105)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.
基金financially supported by US National Science Foundation(NSF)(DEB 0743778,DEB 0840964,DBI 0850290 and EPS 0919466)Office of Science(BER)+1 种基金Department of Energy(DE-FG02-006ER64319)idwestern Regional Center of the National Institute for Climatic Change Research at Michigan Technological University(DE-FC02-06ER64158).
文摘Aims Carbon(C)sequestration in terrestrial ecosystems is strongly regulated by nitrogen(N)processes.However,key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well quantified.Methods Here,we used a Bayesian probabilistic inversion approach to estimate 14 target parameters related to ecosystem C and N interactions from 19 datasets obtained from Duke Forests under ambient and elevated carbon dioxide(CO_(2)).Important FindingsOur results indicated that 8 of the 14 target parameters,such as C:N ratios in most ecosystem compartments,plant N uptake and external N input,were well constrained by available datasets whereas the others,such as N allocation coefficients,N loss and the initial value of mineral N pool were poorly constrained.Our analysis showed that elevated CO_(2)led to the increases in C:N ratios in foliage,fine roots and litter.Moreover,elevated CO_(2)stimulated plant N uptake and increased ecosystem N capital in Duke Forests by 25.2 and 8.5%,respectively.In addition,elevated CO_(2)resulted in the decrease of C exit rates(i.e.increases in C residence times)in foliage,woody biomass,structural litter and passive soil organic matter,but the increase of C exit rate in fine roots.Our results demonstrated that CO_(2)enrichment substantially altered key parameters in determining terrestrial C and N interactions,which have profound implications for model improvement and predictions of future C sequestration in terrestrial ecosystems in response to global change.
基金the sponsorship of the National Natural Science Foundation of China(Grant Nos.41974119 and 42030103)Science Foundation from Innovation and Technology Support Program for Young Scientists in Colleges of Shandong Province and Ministry of Science and Technology of China.
文摘The delineation of shale oil sweet spots is a crucial step in the exploration of shale oil reservoirs.A single attribute such as total organic carbon(TOC)is conventionally used to evaluate the sweet spots of shale oil.This study proposes a probabilistic Fisher discriminant approach for estimating shale oil sweet spots,in which the probabilistic method and Gaussian mixture model are incorporated.Statistical features of shale oil facies are obtained based on the well log interpretation of the samples.Several key parameters of shale oil are projected to data sets with low dimensions in each shale oil facies.Furthermore,the posterior distribution of different shale oil facies is built based on the classification of each shale oil facies.Various key physical parameters of shale oil facies are inversed by the Bayesian method,and important elastic properties are extracted from the elastic impedance inversion(EVA-DSVD method).The method proposed in this paper has been successfully used to delineate the sweet spots of shale oil reservoirs with multiple attributes from the real pre-stack seismic data sets and is validated by the well log data.
基金This study was financially supported by the National Natural Science Foundation of China(31625006,31988102)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23080302)the International Collaboration Project of Chinese Academy of Sciences(131A11KYSB20180010).
文摘Aims Terrestrial ecosystem carbon(C)uptake is remarkably regulated by nitrogen(N)availability in the soil.However,the coupling of C and N cycles,as reflected by C:N ratios in different components,has not been well explored in response to climate change.Methods Here,we applied a data assimilation approach to assimilate 14 datasets collected from a warming experiment in an alpine meadow in China into a grassland ecosystem model.We attempted to evaluate how experimental warming affects C and N coupling as indicated by constrained parameters under ambient and warming treatments separately.Important Findings The results showed that warming increased soil N availability with decreased C:N ratio in soil labile C pool,leading to an increase in N uptake by plants.Nonetheless,C input to leaf increased more than N,leading to an increase and a decrease in the C:N ratio in leaf and root,respectively.Litter C:N ratio was decreased due to the increased N immobilization under high soil N availability or warming-accelerated decomposition of litter mass.Warming also increased C:N ratio of slow soil organic matter pool,suggesting a greater soil C sequestration potential.As most models usually use a fixed C:N ratio across different environments,the divergent shifts of C:N ratios under climate warming detected in this study could provide a useful benchmark for model parameterization and benefit models to predict C-N coupled responses to future climate change.
