To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxeliza...To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxelization energy loss projection algorithm.First,the energy loss equation for muon transmission tomography is derived from the Bethe–Bloch formula,and the imaging region is then dissected into several units using the model voxelization method.Thereafter,the three-dimensional(3-D)imaging model is discretized into parallel and equally spaced two-dimensional(2-D)slices using the model layering method to realize a dimensional reduction of the 3-D volume data and accelerate the forward calculation speed.Subsequently,the muon energy loss transmission tomography equation is discretized using the ray energy loss projection method to establish a set of energy loss equations for the muon penetration voxel model.Finally,the muon energy loss values at the outgoing point are obtained by solving the projection coefficient matrix of the ray length-weighted model,achieving a significant reduction in the number of muons and improving the computational efficiency.A comparison of our results with the simulation results based on the Monte Carlo method verifies the accuracy and effectiveness of the algorithm proposed in this paper.The metallic mineral identification tests show that the proposed algorithm can quickly identify high-density metallic minerals.The muon energy loss response can accurately identify the boundary of the anomalies and their spatial distribution characteristics.展开更多
Owing to the complex lithology of unconventional reservoirs,field interpreters usually need to provide a basis for interpretation using logging simulation models.Among the various detection tools that use nuclear sour...Owing to the complex lithology of unconventional reservoirs,field interpreters usually need to provide a basis for interpretation using logging simulation models.Among the various detection tools that use nuclear sources,the detector response can reflect various types of information of the medium.The Monte Carlo method is one of the primary methods used to obtain nuclear detection responses in complex environments.However,this requires a computational process with extensive random sampling,consumes considerable resources,and does not provide real-time response results.Therefore,a novel fast forward computational method(FFCM)for nuclear measurement that uses volumetric detection constraints to rapidly calculate the detector response in various complex environments is proposed.First,the data library required for the FFCM is built by collecting the detection volume,detector counts,and flux sensitivity functions through a Monte Carlo simulation.Then,based on perturbation theory and the Rytov approximation,a model for the detector response is derived using the flux sensitivity function method and a one-group diffusion model.The environmental perturbation is constrained to optimize the model according to the tool structure and the impact of the formation and borehole within the effective detection volume.Finally,the method is applied to a neutron porosity tool for verification.In various complex simulation environments,the maximum relative error between the calculated porosity results of Monte Carlo and FFCM was 6.80%,with a rootmean-square error of 0.62 p.u.In field well applications,the formation porosity model obtained using FFCM was in good agreement with the model obtained by interpreters,which demonstrates the validity and accuracy of the proposed method.展开更多
The forward calculation of gravity anomalies is a non-negligible aspect contributing to the time consumption of the entire process of basement relief estimation.In this study,we develop a fast hybrid computing scheme ...The forward calculation of gravity anomalies is a non-negligible aspect contributing to the time consumption of the entire process of basement relief estimation.In this study,we develop a fast hybrid computing scheme to compute the gravity anomaly of a basement.We use the vertical prism source equation in a given region R centered at a certain gravity observation point and the vertical line source equation outside R to derive the gravity anomaly.We observe that the computation with the vertical line source equation is much faster than that of the vertical prism source equation,but the former is slightly inaccurate.Therefore,our method is highly effi cient and able to avoid the errors caused by the low accuracy of the vertical line source equation near the observation point.We then derive the general principle of choosing the size of R via a series of prism model tests.Our tests on the gravity anomaly over the Los Angeles Basin confirm the correctness of our proposed forward strategy.We modify Bott’s method with an accelerating factor to expedite the inversion procedure and presume that the density contrast between the sediments and the basement in a sedimentary basin varies laterally and can be obtained using the equivalent equation.Synthetic data and real data applications in the Weihe Basin illustrate that our proposed method can accurately and effi ciently estimate the basement relief of sedimentary basins.展开更多
Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density a...Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density and charging rates.However,traditional theoretical computational methods face the dilemma of long research cycles.Machine learning methods have in recent years shown considerable potential for accelerating research efforts.However,most approaches are limited to specific properties of particular devices.In this paper,we propose a forward prediction and screening framework for functional materials,which includes database selection,attributes,descriptors,machine learning models,and prediction and screening.Based on the Materials Project database,auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks,which finally screen 12 lithium-ion,6 zinc-ion,and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials.The results show that the proposed framework can predict material performance well toward rapid initial screening.The proposed framework can provide a specific and complete working process reference for energy materials design work,contributing to the theoretical foundation for the design of core industrial software for materials engineering.展开更多
基金supported by the National Key Research and Development Project of China(2016YFC0303104)the National Natural Science Foundation of China(41304090)。
文摘To solve the problems associated with low resolution and high computational effort infinite time,this paper proposes a fast forward modeling method for muon energy loss transmission tomography based on a model voxelization energy loss projection algorithm.First,the energy loss equation for muon transmission tomography is derived from the Bethe–Bloch formula,and the imaging region is then dissected into several units using the model voxelization method.Thereafter,the three-dimensional(3-D)imaging model is discretized into parallel and equally spaced two-dimensional(2-D)slices using the model layering method to realize a dimensional reduction of the 3-D volume data and accelerate the forward calculation speed.Subsequently,the muon energy loss transmission tomography equation is discretized using the ray energy loss projection method to establish a set of energy loss equations for the muon penetration voxel model.Finally,the muon energy loss values at the outgoing point are obtained by solving the projection coefficient matrix of the ray length-weighted model,achieving a significant reduction in the number of muons and improving the computational efficiency.A comparison of our results with the simulation results based on the Monte Carlo method verifies the accuracy and effectiveness of the algorithm proposed in this paper.The metallic mineral identification tests show that the proposed algorithm can quickly identify high-density metallic minerals.The muon energy loss response can accurately identify the boundary of the anomalies and their spatial distribution characteristics.
基金This work is supported by National Natural Science Foundation of China(Nos.U23B20151 and 52171253).
文摘Owing to the complex lithology of unconventional reservoirs,field interpreters usually need to provide a basis for interpretation using logging simulation models.Among the various detection tools that use nuclear sources,the detector response can reflect various types of information of the medium.The Monte Carlo method is one of the primary methods used to obtain nuclear detection responses in complex environments.However,this requires a computational process with extensive random sampling,consumes considerable resources,and does not provide real-time response results.Therefore,a novel fast forward computational method(FFCM)for nuclear measurement that uses volumetric detection constraints to rapidly calculate the detector response in various complex environments is proposed.First,the data library required for the FFCM is built by collecting the detection volume,detector counts,and flux sensitivity functions through a Monte Carlo simulation.Then,based on perturbation theory and the Rytov approximation,a model for the detector response is derived using the flux sensitivity function method and a one-group diffusion model.The environmental perturbation is constrained to optimize the model according to the tool structure and the impact of the formation and borehole within the effective detection volume.Finally,the method is applied to a neutron porosity tool for verification.In various complex simulation environments,the maximum relative error between the calculated porosity results of Monte Carlo and FFCM was 6.80%,with a rootmean-square error of 0.62 p.u.In field well applications,the formation porosity model obtained using FFCM was in good agreement with the model obtained by interpreters,which demonstrates the validity and accuracy of the proposed method.
基金supported by the National Natural Science Foundation of China(41904115)。
文摘The forward calculation of gravity anomalies is a non-negligible aspect contributing to the time consumption of the entire process of basement relief estimation.In this study,we develop a fast hybrid computing scheme to compute the gravity anomaly of a basement.We use the vertical prism source equation in a given region R centered at a certain gravity observation point and the vertical line source equation outside R to derive the gravity anomaly.We observe that the computation with the vertical line source equation is much faster than that of the vertical prism source equation,but the former is slightly inaccurate.Therefore,our method is highly effi cient and able to avoid the errors caused by the low accuracy of the vertical line source equation near the observation point.We then derive the general principle of choosing the size of R via a series of prism model tests.Our tests on the gravity anomaly over the Los Angeles Basin confirm the correctness of our proposed forward strategy.We modify Bott’s method with an accelerating factor to expedite the inversion procedure and presume that the density contrast between the sediments and the basement in a sedimentary basin varies laterally and can be obtained using the equivalent equation.Synthetic data and real data applications in the Weihe Basin illustrate that our proposed method can accurately and effi ciently estimate the basement relief of sedimentary basins.
基金financially supported by the Defense Industrial Technology Development Program(JCKY2021-601B019).
文摘Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density and charging rates.However,traditional theoretical computational methods face the dilemma of long research cycles.Machine learning methods have in recent years shown considerable potential for accelerating research efforts.However,most approaches are limited to specific properties of particular devices.In this paper,we propose a forward prediction and screening framework for functional materials,which includes database selection,attributes,descriptors,machine learning models,and prediction and screening.Based on the Materials Project database,auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks,which finally screen 12 lithium-ion,6 zinc-ion,and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials.The results show that the proposed framework can predict material performance well toward rapid initial screening.The proposed framework can provide a specific and complete working process reference for energy materials design work,contributing to the theoretical foundation for the design of core industrial software for materials engineering.
