The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity...The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.展开更多
In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflect...In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflection event is neither hyperbolic, nor any simple function. If traditional normal move-out （NMO） and stack imaging technology are still used, it is difficult to get a clear stack image. Based on previous techniques on non-hyperbolic stack, it is thought in this paper that no matter how complex the geological condition is, in order to get an optimized stack image, the stack should be non time move-out stack, and any stacking method limited to some kind of curve will be restricted to application conditions. In order to overcome the above-mentioned limit, a new method called optimized non-hyperbolic stack imaging based on interpretation model is presented in this paper. Based on CMP/CRP （Common-Reflection-Point） gather after NMO or pre-stack migration, this method uses the interpretation model of reflectors as constraint, and takes comparability as a distinguishing criterion, and finally forms a residual move-out correction for the gather of constrained model. Numerical simulation indicates that this method could overcome the non hyperbolic problem and get fine stack image.展开更多
Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution rem...Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.展开更多
Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of...Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.展开更多
Considering the influence of quadratic gradient term and medium deformation on the seepage equation, a well testing interpretation model for low permeability and deformation dual medium reservoirs was derived and esta...Considering the influence of quadratic gradient term and medium deformation on the seepage equation, a well testing interpretation model for low permeability and deformation dual medium reservoirs was derived and established. The difference method was used to solve the problem, and pressure and pressure derivative double logarithmic curves were drawn to analyze the seepage law. The research results indicate that the influence of starting pressure gradient and medium deformation on the pressure characteristic curve is mainly manifested in the middle and late stages. The larger the value, the more obvious the upward warping of the pressure and pressure derivative curve;the parameter characterizing the dual medium is the crossflow coefficient. The channeling coefficient determines the time and location of the appearance of the “concave”. The smaller the value, the later the appearance of the “concave”, and the more to the right of the “concave”.展开更多
For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation...For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.展开更多
This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected featur...This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified.Initially,the analysis features were extracted using Cuckoo Sandbox,an open-source malware analysis tool,then the features were divided into five categories using the extracted information.The 804 extracted features were reduced by 70%after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature elimination.Next,these important features were analyzed.The level of contribution from each one was assessed by the Random Forest classifier method.The results showed that System call features were mostly allocated.At the end,it was possible to accurately identify the malware type using only 36 to 76 features for each of the four types of malware with the most analysis samples available.These were the Trojan,Adware,Downloader,and Backdoor malware.展开更多
Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati...Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.展开更多
Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-...Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.展开更多
In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models...In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.展开更多
Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical str...Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.展开更多
Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the...Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods.展开更多
The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectrosc...The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.展开更多
This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Auto...This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.展开更多
The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intens...The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% accuracy in damage level classification. To enhance model interpretability, visual explanation heatmaps are incorporated, highlighting semantically relevant regions that the model uses for decision-making. These heatmaps closely align with real-world structural and damage features, confirming that STPEIC learns meaningful representations rather than relying on spurious correlations. Additionally, a graphical user interface (GUI) has been developed to streamline image input, classification, and interpretability visualization, improving the practical usability of the system. Overall, STPEIC provides a reliable, interpretable, and user-friendly solution for rapid post-earthquake structural evaluation.展开更多
Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its im...Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.展开更多
Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pres...Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm^(3),with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications.展开更多
Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroim...Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.展开更多
The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distributi...The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distribution in lacustrine shales and its influence on pore structure,and describes the process of porosity development.The principal findings are:(i)Three distribution patterns of OM in lacustrine shales are distinguished;laminated continuous distribution,clumped distribution,and stellate scattered distribution.The differences in total organic carbon(TOC)content,free hydrocarbon content(S_(1)),and OM porosity among these distribution patterns are discussed.(ii)Porosity is negatively correlated with TOC and plagioclase content and positively correlated with quartz,dolomite,and clay mineral content.(iii)Pore evolution in lacustrine shales is characterized by a sequence of decreasing-increasing-decreasing porosity,followed by continuously increasing porosity until a relatively stable condition is reached.(iv)A new model for evaluating porosity in lacustrine shales is proposed.Using this model,the organic and inorganic porosity of shales in the Permian Lucaogou Formation are calculated to be 2.5%-5%and 1%-6.3%,respectively,which correlate closely with measured data.These findings may provide a scientific basis and technical support for the sweet spotting in lacustrine shales in China.展开更多
In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experimen...In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.展开更多
基金supported by National Natural Science Foundation of China(41474115,42174155)Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources(Yangtze University)Ministry of Education of China(No K2018-02)。
文摘The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.
文摘In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflection event is neither hyperbolic, nor any simple function. If traditional normal move-out （NMO） and stack imaging technology are still used, it is difficult to get a clear stack image. Based on previous techniques on non-hyperbolic stack, it is thought in this paper that no matter how complex the geological condition is, in order to get an optimized stack image, the stack should be non time move-out stack, and any stacking method limited to some kind of curve will be restricted to application conditions. In order to overcome the above-mentioned limit, a new method called optimized non-hyperbolic stack imaging based on interpretation model is presented in this paper. Based on CMP/CRP （Common-Reflection-Point） gather after NMO or pre-stack migration, this method uses the interpretation model of reflectors as constraint, and takes comparability as a distinguishing criterion, and finally forms a residual move-out correction for the gather of constrained model. Numerical simulation indicates that this method could overcome the non hyperbolic problem and get fine stack image.
基金the National Key R&D Program of China(2019YFC1510700)the Sichuan Science and Technology Program(2022YFS0539)the Geomatics Technology and Application Key Laboratory of Qinghai Province,China(QHDX-2018-07).
文摘Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.
文摘Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.
文摘Considering the influence of quadratic gradient term and medium deformation on the seepage equation, a well testing interpretation model for low permeability and deformation dual medium reservoirs was derived and established. The difference method was used to solve the problem, and pressure and pressure derivative double logarithmic curves were drawn to analyze the seepage law. The research results indicate that the influence of starting pressure gradient and medium deformation on the pressure characteristic curve is mainly manifested in the middle and late stages. The larger the value, the more obvious the upward warping of the pressure and pressure derivative curve;the parameter characterizing the dual medium is the crossflow coefficient. The channeling coefficient determines the time and location of the appearance of the “concave”. The smaller the value, the later the appearance of the “concave”, and the more to the right of the “concave”.
基金the National Basic Research Program of China (973 Program) ( 2007CB407206)the National Key Technologies Research and Develop-ment Program in the Eleventh Five-Year Plan of China (2006BAC01A11)
文摘For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.
基金supported by the Research Program through the National Research Foundation of Korea,NRF-2018R1D1A1B07050864.
文摘This study was conducted to enable prompt classification of malware,which was becoming increasingly sophisticated.To do this,we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified.Initially,the analysis features were extracted using Cuckoo Sandbox,an open-source malware analysis tool,then the features were divided into five categories using the extracted information.The 804 extracted features were reduced by 70%after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature elimination.Next,these important features were analyzed.The level of contribution from each one was assessed by the Random Forest classifier method.The results showed that System call features were mostly allocated.At the end,it was possible to accurately identify the malware type using only 36 to 76 features for each of the four types of malware with the most analysis samples available.These were the Trojan,Adware,Downloader,and Backdoor malware.
基金Supported by the National Natural Science Foundation of China(61473026,61104131)the Fundamental Research Funds for the Central Universities(JD1413)
文摘Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.
基金Supported by the National Natural Science Foundation of China(61374166)the Doctoral Fund of Ministry of Education of China(20120010110010)the Natural Science Fund of Ningbo(2012A610001)
文摘Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.
文摘In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.
基金Supported by the National Natural Science Foundation of China(61374166,6153303)the Doctoral Fund of Ministry of Education of China(20120010110010)the Fundamental Research Funds for the Central Universities(YS1404,JD1413,ZY1502)
文摘Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.
文摘Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Grant number:RS-2025-02316700,and RS-2025-00522430)the China Scholarship Council Program。
文摘The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.
基金funded by the National Natural Science Foundation of China(42050104).
文摘This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.
基金support from General Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(2025JC-YBMS-443)Fundamental Research Funds for the Central Universities,CHU(300102213209)+1 种基金Research Funds for the Interdisciplinary Projects,CHU(300104240915)National Natural Science Foundation of China(Grant No.52361135806).
文摘The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% accuracy in damage level classification. To enhance model interpretability, visual explanation heatmaps are incorporated, highlighting semantically relevant regions that the model uses for decision-making. These heatmaps closely align with real-world structural and damage features, confirming that STPEIC learns meaningful representations rather than relying on spurious correlations. Additionally, a graphical user interface (GUI) has been developed to streamline image input, classification, and interpretability visualization, improving the practical usability of the system. Overall, STPEIC provides a reliable, interpretable, and user-friendly solution for rapid post-earthquake structural evaluation.
基金partially supported by the National Natural Science Foundation of China(42177164,52474121)the Outstanding Youth Project of Hunan Provincial Department of Education(23B0008).
文摘Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.
基金supported by the National Natural Science Foundation of China(Grant numbers:52174012,52394250,52394255,52234002,U22B20126,51804322).
文摘Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm^(3),with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications.
文摘Predicting the progression from Mild Cognitive Impairment(MCI)to Alzheimer's Disease(AD)is a critical challenge for enabling early intervention and improving patient outcomes.While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression,its effective analysis is hampered by significant challenges:temporal heterogeneity(irregularly sampled scans),multi-modal misalignment,and the propensity of deep learning models to learn spurious,noncausal correlations.We propose CASCADE-Net,a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction.Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation(Neural ODE)to model the continuous,underlying progression of pathology from irregularly sampled scans,effectively mapping heterogeneous patient data to a unified latent timeline.This aligned,noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism.This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training.This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal,confounder-based changes.Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy.Furthermore,our model provides highly interpretable,causally-grounded attention maps,offering valuable insights into the disease progression process and fostering greater clinical trust.
基金sponsored by the National Natural Science Foundation of China(42072187,42090025)CNPC Key Project of Science and Technology(2021DQ0405)。
文摘The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distribution in lacustrine shales and its influence on pore structure,and describes the process of porosity development.The principal findings are:(i)Three distribution patterns of OM in lacustrine shales are distinguished;laminated continuous distribution,clumped distribution,and stellate scattered distribution.The differences in total organic carbon(TOC)content,free hydrocarbon content(S_(1)),and OM porosity among these distribution patterns are discussed.(ii)Porosity is negatively correlated with TOC and plagioclase content and positively correlated with quartz,dolomite,and clay mineral content.(iii)Pore evolution in lacustrine shales is characterized by a sequence of decreasing-increasing-decreasing porosity,followed by continuously increasing porosity until a relatively stable condition is reached.(iv)A new model for evaluating porosity in lacustrine shales is proposed.Using this model,the organic and inorganic porosity of shales in the Permian Lucaogou Formation are calculated to be 2.5%-5%and 1%-6.3%,respectively,which correlate closely with measured data.These findings may provide a scientific basis and technical support for the sweet spotting in lacustrine shales in China.
文摘In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.
