The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm s...The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm sea surface assumption. Hence, the traditional ＂dephase and sum＂ algorithms might produce poor-quality results in rough sea conditions. We propose an adaptive over/under data combination method, which adaptively estimates the amplitude spectrum of the ghost operator from the over/under data, and then over/under data combinations are implemented using the estimated ghost operators. A synthetic single shot gather is used to verify the performance of the proposed method in rough sea surface conditions and a real triple over/under dataset demonstrates the method performance.展开更多
To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method ca...To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.展开更多
The present paper proposes a new method of spectrophotometry based on linear combination of multiwavelength data by means of selecting a set of properly weighted coefficients and combination methods. It is clear that ...The present paper proposes a new method of spectrophotometry based on linear combination of multiwavelength data by means of selecting a set of properly weighted coefficients and combination methods. It is clear that the weighted combination absorbance attained is only in direct proportion to the concentration of the analysed component and independent of coexisting interferents.The accuracy of the analytical results is improved greatly for the analysis of light rare earths with the coexistence of heavy rare earths.The analyti- cal error from the reagent blank and co-coloration of light and heavy rare earths have also been overcome. The greatly improved linearity and additivity of absorbance are obtained.展开更多
In this study, 16 combinations of the ECMWF (European Centre for Medium Range Weather Forecast) reanalyzed daily rainfall and the pentad CMAP in China for the period 1980-1993(1 May-31 Dec.) were calculated. Correlati...In this study, 16 combinations of the ECMWF (European Centre for Medium Range Weather Forecast) reanalyzed daily rainfall and the pentad CMAP in China for the period 1980-1993(1 May-31 Dec.) were calculated. Correlation analysis was used to roughly evaluate daily rainfall for the whole of China and a combination of RPC (rotated principal component) and wavelet analyses was applied to data on observed and combined daily rainfall to obtain a detailed evaluation of the quality of these combined datasets in 6 selected major rainfall regions of eastern China. The results showed that except for intraweekly fluctuation, the best combination was roughly similar to or accorded well with observation in the aspects of space variation patterns and long period rainfall fluctuations related to monsoon onset and serious meteorologic disasters, indicating that this combination yielded better values of long term daily mean and standard deviation through the pentad CMAP (CPC Merged Analysis of Precipitation), and can also represent rainfall fluctuations through the reanalyzed daily rainfall.展开更多
Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satell...Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).展开更多
Gene sequence-based genealogies of scuticociliates are different from those produced by morphological analyses.For this reason,11 representative scuticociliates and two ambiguously related genera were chosen to test t...Gene sequence-based genealogies of scuticociliates are different from those produced by morphological analyses.For this reason,11 representative scuticociliates and two ambiguously related genera were chosen to test the ability of combined phylogenetic analyses using both gene sequences and morphological/morphogenetic characteristics.Analyses of both the SSrRNA gene sequences and the combined datasets revealed a consistent branching pattern.While the terminal branches and the order level relationships were generally well resolved,the family level relationships remain unresolved.However,two other trees based on ITS1-5.8S-ITS2 region sequences and morphological/morphogenetic characters showed limited information,due to a lack of informative sites in these two datasets.Our data suggest,however,that the combined analysis of morphological/morphogenetic characters and gene sequences did produce some changes to the phylogenetic estimates of this group.展开更多
Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-a...Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-and-effect relationship,are given priority for integration.However,few efficient integrating methods are available.Results:Here,we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency.To verify its feasibility,two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.Centered on salt tolerance,data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening,and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening.Target phenotype could be discriminated after five-day salt-treatment,much earlier than phenotypic difference appearance.20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants,almost tripled that of unintegrated analysis.Accuracy rate,confirmed with salt-tolerance analysis for experimental verification,reached 90%,which can be optimized to 100%theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis.Conclusions:Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data.Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects,providing a powerful tool for phenotype selection in new-generation crop breeding.展开更多
Objective To investigate the clinical value of different magnetic resonance (MR) pulse sequences in diagnosis of spinal metastatic tumor. Methods Fifteen patients with clinically suspected spinal metastatic tumor were...Objective To investigate the clinical value of different magnetic resonance (MR) pulse sequences in diagnosis of spinal metastatic tumor. Methods Fifteen patients with clinically suspected spinal metastatic tumor were included in this study. These patients were with documented primary tumors. Four MR pulse sequences, T1-weighted spin echo (T1WI SE), T2-weighted fast spin echo (T2WI FSE), short time inversion recovery (STIR), and gradient echo 2-D multi echo data imaging combination (GE Me-2D) were used to detect spinal metastasis. Results Fifteen vertebral bodies were entire involvement, 38 vertebral bodies were section involvement, and totally 53 vertebral bodies were involved. There were 19 focal infections in pedicle of vertebral arch, 15 metastases in spinous process and transverse process. Fifty-three vertebral bodies were abnormal in T1WI SE and GE Me-2D, 35 vertebral bodies were found abnormal in T2WI FSE, and 50 vertebral bodies were found abnormal in STIR. The verges of focal signal of involved vertebral bodies were comparatively clear in T1WI SE, comparatively clear or vague in T2WI FSE, vague in STIR, and clear in GE Me-2D.Conclusions GE Me-2D may be the most sensitive technique to detect metastases. So three sequences (T1WI SE, T2WI FSE, GE Me-2D) can demonstrate the early changes of spinal metastasis roundly.展开更多
The Ordovician fracture-vug carbonate reservoirs of Tarim Basin,are featured by developed vugs,caves and fractures.The strong heterogeneity results in huge uncertainty when these reservoirs are quantitatively characte...The Ordovician fracture-vug carbonate reservoirs of Tarim Basin,are featured by developed vugs,caves and fractures.The strong heterogeneity results in huge uncertainty when these reservoirs are quantitatively characterized using merely static seismic data.The effective quantitative characterization of the reservoirs has been an urgent problem to be solved.This study creatively proposes the"second quantitative characterization"technique with the combination of dynamic and static data based on the primary static quantitative characterization and fully considering lots of key influence factors when conducting characterization.In this technique,dynamic analysis methods such as well testing,production rate transient analysis,dynamic reserve evaluation and dynamic connectivity evaluation are used to get understandings on this kind of reservoir.These understandings are used as statistical parameters to constrain the inversion of seismic wave impedance to improve the relationship between wave impedance and porosity and determine the fracture-vug morphology,calculate dynamic reserves,and then a more accurate fracture-vugmodel can be selected and used to calculate the oil-water contact inversely based on the results of"second quantitative characterization".This method can lower the uncertainties in the primary quantitative characterization of fracture-vug reservoirs,enhance the accuracy of characterization results significantly,and has achieved good application results in the fracture-vug carbonate reservoirs of Tarim Basin.展开更多
Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of...Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.展开更多
The recently developed borehole dipole shear wave(S-wave)reflection imaging technique has been successfully applied to deep hydrocarbon exploration,allowing for accurate detection of fracture-cavity reservoirs tens of...The recently developed borehole dipole shear wave(S-wave)reflection imaging technique has been successfully applied to deep hydrocarbon exploration,allowing for accurate detection of fracture-cavity reservoirs tens of meters away from the borehole.Further developments in this technology needs to resolve the azimuthal 180°-ambiguity caused by the symmetry of the dipole sources and receivers,known as the“180°-ambiguity problem”in the well logging community.This paper aims to determine the azimuth of reflectors outside a borehole uniquely,which is crucial for optimizing deep hydrocarbon exploration and critical field operations(such as directional drilling).Based on the theory of interaction between elastic waves and reflectors outside the borehole,this paper analyzes the azimuthal response characteristics of reflected waves received in the borehole.We propose a new full-azimuthal dipole measurement mode to eliminate the azimuthal ambiguity through a multi-azimuth data reception and combination method.The method has been validated in laboratory azimuthal model test wells.Theoretical and experimental results indicate that the new measurement method preserves the azimuthal amplitude variation characteristic of the conventional four-component(4C)measurement and possesses a 360°periodicity for the wave phase variation,which can be effectively used to resolve the azimuthal 180°-ambiguity of the dipole shear reflection imaging.Based on the new method,a new full-azimuthal dipole shear reflection imaging tool prototype has been developed and field tested in a horizontal development well in the Tarim oil field.The acoustic reflection imaging results clearly demonstrate that the horizontal well crosses an approximately 30 m thick fault-karst body,with the imaging range outside the borehole extending up to 65 m.By analyzing the amplitude and phase shift of the acoustic reflection data,allowing for delineating the fault-karst body in the formation.This research provides both theoretical and experimental foundations for the development and application of borehole azimuthal acoustic reflection imaging technology for deep hydrocarbon exploration.展开更多
The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial in...The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications,which is a problem that needs to be addressed.This paper proposes a novel multimodal learning framework for metaverse healthcare,MMLMH,based on collaborative intra-and intersample representation and adaptive fusion.Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text,audio,and visual health data.By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms,MMLMH achieves superior feature representation for complex health assessments.The framework’s adaptive fusion approach,utilizing attention mechanisms and gated neural networks,demonstrates robust performance across varying noise levels and data quality conditions.Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics.Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy,patient-system interaction efficacy,and data integration complexity.The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars,which could lead to greater personalization of healthcare experiences in the metaverse.MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources.They can be successfully utilized in next-generation healthcare delivery through virtual reality.展开更多
文摘The traditional ＂dephase and sum＂ algorithms for over/under data combination estimate the ghost operator by assuming a calm sea surface. However, the real sea surface is typically rough, which invalidates the calm sea surface assumption. Hence, the traditional ＂dephase and sum＂ algorithms might produce poor-quality results in rough sea conditions. We propose an adaptive over/under data combination method, which adaptively estimates the amplitude spectrum of the ghost operator from the over/under data, and then over/under data combinations are implemented using the estimated ghost operators. A synthetic single shot gather is used to verify the performance of the proposed method in rough sea surface conditions and a real triple over/under dataset demonstrates the method performance.
基金Supported by the National Nature Science Founda tion of China(50099620)863 Project(2001AA132050-03)
文摘To resolve the data combination of Phased-array Ground Penetrating Radar (PAGPR), we first build a model of PAGPR and a layered model, and then a new data combination algorithm is presented based on it. This method calculates time delay of multi-receivers, basing on the signal of the nearest receiver, then shifts other signals and adds them up, and gets one signal at last. It has been proved that this method can restrain noise, multiple waves, clutter waves and improve the precision of time location. In the end, an example is given to prove the method's efficiency.
文摘The present paper proposes a new method of spectrophotometry based on linear combination of multiwavelength data by means of selecting a set of properly weighted coefficients and combination methods. It is clear that the weighted combination absorbance attained is only in direct proportion to the concentration of the analysed component and independent of coexisting interferents.The accuracy of the analytical results is improved greatly for the analysis of light rare earths with the coexistence of heavy rare earths.The analyti- cal error from the reagent blank and co-coloration of light and heavy rare earths have also been overcome. The greatly improved linearity and additivity of absorbance are obtained.
文摘In this study, 16 combinations of the ECMWF (European Centre for Medium Range Weather Forecast) reanalyzed daily rainfall and the pentad CMAP in China for the period 1980-1993(1 May-31 Dec.) were calculated. Correlation analysis was used to roughly evaluate daily rainfall for the whole of China and a combination of RPC (rotated principal component) and wavelet analyses was applied to data on observed and combined daily rainfall to obtain a detailed evaluation of the quality of these combined datasets in 6 selected major rainfall regions of eastern China. The results showed that except for intraweekly fluctuation, the best combination was roughly similar to or accorded well with observation in the aspects of space variation patterns and long period rainfall fluctuations related to monsoon onset and serious meteorologic disasters, indicating that this combination yielded better values of long term daily mean and standard deviation through the pentad CMAP (CPC Merged Analysis of Precipitation), and can also represent rainfall fluctuations through the reanalyzed daily rainfall.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).
基金Supported by the National Natural Science Foundation of China(No.30870280)a grant from the Center of Excellence in Biodiversity,King Saud University,Riyadh,Saudi Arabia
文摘Gene sequence-based genealogies of scuticociliates are different from those produced by morphological analyses.For this reason,11 representative scuticociliates and two ambiguously related genera were chosen to test the ability of combined phylogenetic analyses using both gene sequences and morphological/morphogenetic characteristics.Analyses of both the SSrRNA gene sequences and the combined datasets revealed a consistent branching pattern.While the terminal branches and the order level relationships were generally well resolved,the family level relationships remain unresolved.However,two other trees based on ITS1-5.8S-ITS2 region sequences and morphological/morphogenetic characters showed limited information,due to a lack of informative sites in these two datasets.Our data suggest,however,that the combined analysis of morphological/morphogenetic characters and gene sequences did produce some changes to the phylogenetic estimates of this group.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA26030102)the CAS-CSIRO Project(063GJHZ2022047MI)the CAS Special Research Assistant(SRA)Program(Y973RG1001).
文摘Background:Plant phenomics has made significant progress recently,with new demand to move from external characterization to internal exploration through data combination.Hyperspectral and metabolomic data,with cause-and-effect relationship,are given priority for integration.However,few efficient integrating methods are available.Results:Here,we showed the way to explore hyperspectral data through combining with upper-level metabolomic data and perform higher-level-data-guided dimension reduction in target-trait-oriented manner to obtain high analysis efficiency.To verify its feasibility,two-stage pipeline combining hyperspectral and metabolic data was designed to discriminate salt-tolerant phenotype for Medicago truncatula mutants.Centered on salt tolerance,data are combined through constructing metabolite-based spectral indices outlining tolerance-related metabolic changes in primary screening,and models converting hyperspectral data to metabolite content for detailed characterizing in secondary screening.Target phenotype could be discriminated after five-day salt-treatment,much earlier than phenotypic difference appearance.20 mutants with salt-tolerant phenotype were successfully identified from about 1000 mutants,almost tripled that of unintegrated analysis.Accuracy rate,confirmed with salt-tolerance analysis for experimental verification,reached 90%,which can be optimized to 100%theoretically utilizing results from hierarchical-clustering-assisted Principal Component Analysis.Conclusions:Mutant-screening pipeline provided here is a practical example for targeted data integration and data mining under the guide of upper-layer omic data.Targeted combination of phenomic and metabolomic data provides the ability for accurate phenotype discrimination and prediction from both external and internal aspects,providing a powerful tool for phenotype selection in new-generation crop breeding.
文摘Objective To investigate the clinical value of different magnetic resonance (MR) pulse sequences in diagnosis of spinal metastatic tumor. Methods Fifteen patients with clinically suspected spinal metastatic tumor were included in this study. These patients were with documented primary tumors. Four MR pulse sequences, T1-weighted spin echo (T1WI SE), T2-weighted fast spin echo (T2WI FSE), short time inversion recovery (STIR), and gradient echo 2-D multi echo data imaging combination (GE Me-2D) were used to detect spinal metastasis. Results Fifteen vertebral bodies were entire involvement, 38 vertebral bodies were section involvement, and totally 53 vertebral bodies were involved. There were 19 focal infections in pedicle of vertebral arch, 15 metastases in spinous process and transverse process. Fifty-three vertebral bodies were abnormal in T1WI SE and GE Me-2D, 35 vertebral bodies were found abnormal in T2WI FSE, and 50 vertebral bodies were found abnormal in STIR. The verges of focal signal of involved vertebral bodies were comparatively clear in T1WI SE, comparatively clear or vague in T2WI FSE, vague in STIR, and clear in GE Me-2D.Conclusions GE Me-2D may be the most sensitive technique to detect metastases. So three sequences (T1WI SE, T2WI FSE, GE Me-2D) can demonstrate the early changes of spinal metastasis roundly.
基金Supported by the General Program of Natural Science Foundation of China(51874346).
文摘The Ordovician fracture-vug carbonate reservoirs of Tarim Basin,are featured by developed vugs,caves and fractures.The strong heterogeneity results in huge uncertainty when these reservoirs are quantitatively characterized using merely static seismic data.The effective quantitative characterization of the reservoirs has been an urgent problem to be solved.This study creatively proposes the"second quantitative characterization"technique with the combination of dynamic and static data based on the primary static quantitative characterization and fully considering lots of key influence factors when conducting characterization.In this technique,dynamic analysis methods such as well testing,production rate transient analysis,dynamic reserve evaluation and dynamic connectivity evaluation are used to get understandings on this kind of reservoir.These understandings are used as statistical parameters to constrain the inversion of seismic wave impedance to improve the relationship between wave impedance and porosity and determine the fracture-vug morphology,calculate dynamic reserves,and then a more accurate fracture-vugmodel can be selected and used to calculate the oil-water contact inversely based on the results of"second quantitative characterization".This method can lower the uncertainties in the primary quantitative characterization of fracture-vug reservoirs,enhance the accuracy of characterization results significantly,and has achieved good application results in the fracture-vug carbonate reservoirs of Tarim Basin.
基金the National Key Research and Development Program of China (2017YFC1502102)National Natural Science Youth Fund of China (41905089)。
文摘Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.
基金supported jointly by the National Natural Science Foundation of China(Grant Nos.U21B2064 and 42174145)the Natural Science Foundation of Shandong Province(Grant No.ZR2024YQ062)+1 种基金the Laoshan National Laboratory Science and Technology Innovation Project(Grant No.LSKJ202203407)the Major Scientific and Technological Projects of China National Petroleum Corporation(Grant No.ZD2019-183-004)。
文摘The recently developed borehole dipole shear wave(S-wave)reflection imaging technique has been successfully applied to deep hydrocarbon exploration,allowing for accurate detection of fracture-cavity reservoirs tens of meters away from the borehole.Further developments in this technology needs to resolve the azimuthal 180°-ambiguity caused by the symmetry of the dipole sources and receivers,known as the“180°-ambiguity problem”in the well logging community.This paper aims to determine the azimuth of reflectors outside a borehole uniquely,which is crucial for optimizing deep hydrocarbon exploration and critical field operations(such as directional drilling).Based on the theory of interaction between elastic waves and reflectors outside the borehole,this paper analyzes the azimuthal response characteristics of reflected waves received in the borehole.We propose a new full-azimuthal dipole measurement mode to eliminate the azimuthal ambiguity through a multi-azimuth data reception and combination method.The method has been validated in laboratory azimuthal model test wells.Theoretical and experimental results indicate that the new measurement method preserves the azimuthal amplitude variation characteristic of the conventional four-component(4C)measurement and possesses a 360°periodicity for the wave phase variation,which can be effectively used to resolve the azimuthal 180°-ambiguity of the dipole shear reflection imaging.Based on the new method,a new full-azimuthal dipole shear reflection imaging tool prototype has been developed and field tested in a horizontal development well in the Tarim oil field.The acoustic reflection imaging results clearly demonstrate that the horizontal well crosses an approximately 30 m thick fault-karst body,with the imaging range outside the borehole extending up to 65 m.By analyzing the amplitude and phase shift of the acoustic reflection data,allowing for delineating the fault-karst body in the formation.This research provides both theoretical and experimental foundations for the development and application of borehole azimuthal acoustic reflection imaging technology for deep hydrocarbon exploration.
基金supported by the National Natural Science Foundation of China under Grant No.62202247the National Key R&D Program of China under Grant No.2022ZD0115303.
文摘The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications,which is a problem that needs to be addressed.This paper proposes a novel multimodal learning framework for metaverse healthcare,MMLMH,based on collaborative intra-and intersample representation and adaptive fusion.Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text,audio,and visual health data.By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms,MMLMH achieves superior feature representation for complex health assessments.The framework’s adaptive fusion approach,utilizing attention mechanisms and gated neural networks,demonstrates robust performance across varying noise levels and data quality conditions.Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics.Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy,patient-system interaction efficacy,and data integration complexity.The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars,which could lead to greater personalization of healthcare experiences in the metaverse.MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources.They can be successfully utilized in next-generation healthcare delivery through virtual reality.
