Human population growth and land-use changes raise demand and competition for water resources. The Upper OumErRabia River Basin is experiencing high rangeland and matorral conversion to irrigated agricultural land exp...Human population growth and land-use changes raise demand and competition for water resources. The Upper OumErRabia River Basin is experiencing high rangeland and matorral conversion to irrigated agricultural land expansion. Given Morocco’s per capita water availability, River-basin hydrologic </span><span style="font-family:Verdana;">modelling</span><span style="font-family:Verdana;"> could potentially bring together agricultural, water resources </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> conservation objectives. However, not everywhere have hydrological models considered events and continuous assessment of climatic data. In this study, HEC-HMS </span><span style="font-family:Verdana;">modelling</span><span style="font-family:Verdana;"> approach is used to explore the event-based and continuous-process simulation of land-use and </span><span style="font-family:Verdana;">land cover</span><span style="font-family:Verdana;"> change (LULCC) impact on water balance. The use of HEC-GeoHMS facilitated the digital data processing for coupling with the model. The basin’s physical characteristics and the hydro-climatic data helped to generate a geospatial database for </span><span style="font-family:Verdana;">HEC-HMS</span><span style="font-family:Verdana;"> model. We analyzed baseline and future scenario changes for the 1980-2016 period using the SCS Curve-Number and the Soil Moisture Accounting (SMA) loss methods. SMA was coupled with the Hargreaves evapotranspiration method. Model calibration focused on reproducing observed basin runoff hydrograph. To evaluate the model performance for both calibration and validation</span></span><span style="font-family:Verdana;">, </span><span style="font-family:""><span style="font-family:Verdana;">the Coefficient of determination (R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">), Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RSR) </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> Percent Bias (PBIAS) criteria were exploited. The average calibration NSE values were</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">0.740 and 0.585 for event-based (daily) and continuous-process (annual) respectively. The R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">, RSR </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> PBIAS values were 0.624, 0.634 </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> +16.7 respectively. This is rated as good performance besides the validation simulations </span><span style="font-family:Verdana;">were</span><span style="font-family:Verdana;"> satisfactory for subsequent hydrologic analyses. We conclude that the basin’s hydrologic response to positive and negative LULCC scenarios is significant </span><span style="font-family:Verdana;">both</span><span style="font-family:Verdana;"> positive and negative scenarios. The study findings provide useful information for key stakeholders/decision-makers in water resources.展开更多
为对比分析HEC-HMS模型三种降雨损失方法在沁河流域的适用性。借助Morris筛选法识别降雨损失方法的关键参数,选用流域内5场雨洪资料进行参数率定和模拟精度分析。结果表明:(1)SCS CN值曲线法、Green-Ampt法、Initial and Uniform法主要...为对比分析HEC-HMS模型三种降雨损失方法在沁河流域的适用性。借助Morris筛选法识别降雨损失方法的关键参数,选用流域内5场雨洪资料进行参数率定和模拟精度分析。结果表明:(1)SCS CN值曲线法、Green-Ampt法、Initial and Uniform法主要敏感性参数分别为CN值、土壤饱和导水率、恒定损失率。(2)选取洪峰流量、洪水总量、峰现时刻误差以及Nash系数对模型模拟精度进行评价,SCS CN值曲线法和Initial and Uniform法模拟结果达到乙等精度,Green-Ampt法模拟结果达到丙等精度。研究成果可为半湿润地区中小流域降雨损失方法的选择提供参考。展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
文摘Human population growth and land-use changes raise demand and competition for water resources. The Upper OumErRabia River Basin is experiencing high rangeland and matorral conversion to irrigated agricultural land expansion. Given Morocco’s per capita water availability, River-basin hydrologic </span><span style="font-family:Verdana;">modelling</span><span style="font-family:Verdana;"> could potentially bring together agricultural, water resources </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> conservation objectives. However, not everywhere have hydrological models considered events and continuous assessment of climatic data. In this study, HEC-HMS </span><span style="font-family:Verdana;">modelling</span><span style="font-family:Verdana;"> approach is used to explore the event-based and continuous-process simulation of land-use and </span><span style="font-family:Verdana;">land cover</span><span style="font-family:Verdana;"> change (LULCC) impact on water balance. The use of HEC-GeoHMS facilitated the digital data processing for coupling with the model. The basin’s physical characteristics and the hydro-climatic data helped to generate a geospatial database for </span><span style="font-family:Verdana;">HEC-HMS</span><span style="font-family:Verdana;"> model. We analyzed baseline and future scenario changes for the 1980-2016 period using the SCS Curve-Number and the Soil Moisture Accounting (SMA) loss methods. SMA was coupled with the Hargreaves evapotranspiration method. Model calibration focused on reproducing observed basin runoff hydrograph. To evaluate the model performance for both calibration and validation</span></span><span style="font-family:Verdana;">, </span><span style="font-family:""><span style="font-family:Verdana;">the Coefficient of determination (R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">), Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RSR) </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> Percent Bias (PBIAS) criteria were exploited. The average calibration NSE values were</span></span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">0.740 and 0.585 for event-based (daily) and continuous-process (annual) respectively. The R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">, RSR </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> PBIAS values were 0.624, 0.634 </span><span style="font-family:Verdana;">and</span><span style="font-family:Verdana;"> +16.7 respectively. This is rated as good performance besides the validation simulations </span><span style="font-family:Verdana;">were</span><span style="font-family:Verdana;"> satisfactory for subsequent hydrologic analyses. We conclude that the basin’s hydrologic response to positive and negative LULCC scenarios is significant </span><span style="font-family:Verdana;">both</span><span style="font-family:Verdana;"> positive and negative scenarios. The study findings provide useful information for key stakeholders/decision-makers in water resources.
文摘为对比分析HEC-HMS模型三种降雨损失方法在沁河流域的适用性。借助Morris筛选法识别降雨损失方法的关键参数,选用流域内5场雨洪资料进行参数率定和模拟精度分析。结果表明:(1)SCS CN值曲线法、Green-Ampt法、Initial and Uniform法主要敏感性参数分别为CN值、土壤饱和导水率、恒定损失率。(2)选取洪峰流量、洪水总量、峰现时刻误差以及Nash系数对模型模拟精度进行评价,SCS CN值曲线法和Initial and Uniform法模拟结果达到乙等精度,Green-Ampt法模拟结果达到丙等精度。研究成果可为半湿润地区中小流域降雨损失方法的选择提供参考。
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
