In recent decades, log system management has been widely studied fordata security management. System abnormalities or illegal operations can befound in time by analyzing the log and provide evidence for intrusions. In...In recent decades, log system management has been widely studied fordata security management. System abnormalities or illegal operations can befound in time by analyzing the log and provide evidence for intrusions. In orderto ensure the integrity of the log in the current system, many researchers havedesigned it based on blockchain. However, the emerging blockchain is facing significant security challenges with the increment of quantum computers. An attackerequipped with a quantum computer can extract the user's private key from thepublic key to generate a forged signature, destroy the structure of the blockchain,and threaten the security of the log system. Thus, blind signature on the lattice inpost-quantum blockchain brings new security features for log systems. In ourpaper, to address these, firstly, we propose a novel log system based on post-quantum blockchain that can resist quantum computing attacks. Secondly, we utilize apost-quantum blind signature on the lattice to ensure both security and blindnessof log system, which makes the privacy of log information to a large extent.Lastly, we enhance the security level of lattice-based blind signature under therandom oracle model, and the signature size grows slowly compared with others.We also implement our protocol and conduct an extensive analysis to prove theideas. The results show that our scheme signature size edges up subtly comparedwith others with the improvement of security level.展开更多
With the increasing popularity of cloud storage,data security on the cloud has become increasingly visible.Searchable encryption has the ability to realize the privacy protection and security of data in the cloud.Howe...With the increasing popularity of cloud storage,data security on the cloud has become increasingly visible.Searchable encryption has the ability to realize the privacy protection and security of data in the cloud.However,with the continuous development of quantum computing,the standard Public-key Encryption with Keyword Search(PEKS)scheme cannot resist quantumbased keyword guessing attacks.Further,the credibility of the server also poses a significant threat to the security of the retrieval process.This paper proposes a searchable encryption scheme based on lattice cryptography using blockchain to address the above problems.Firstly,we design a lattice-based encryption primitive to resist quantum keyword guessing attacks.Moreover,blockchain is to decentralize the cloud storage platform’s jurisdiction of data.It also ensures that the traceability of keyword retrieval process and maintains the credibility of search result,which malicious platforms are prevented as much as possible from deliberately sending wrong search results.Last but not least,through security analysis,our proposed scheme satisfies the credibility and unforgeability of the keyword ciphertext.The comprehensive performance evaluates that our scheme has certain advantages in terms of efficiency compared with others.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.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
Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve ...Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.展开更多
This study explores the application of machine learning techniques for predicting permeability,porosity,and flow zone indicator(FZI)in carbonate reservoirs using well log data,aiming to overcome the limitations of tra...This study explores the application of machine learning techniques for predicting permeability,porosity,and flow zone indicator(FZI)in carbonate reservoirs using well log data,aiming to overcome the limitations of traditional empirical methods.Six machine learning algorithms are utilized:support vector machine(SVM),backpropagation(BP)neural network,gaussian process regression(GPR),extreme gradient boosting(XGBoost),K-nearest neighbor(KNN),and random forest(RF).The methodology involves classifying pore-permeability types based on the flow index,leveraging logging curves and geological data.Models are trained using seven logging parameters—spectral gamma rays(SGR),uranium-free gamma rays(CGR),photoelectric absorption cross-section index(PE),lithologic density(RHOB),acoustic transit time(DT),neutron porosity(NPHI),and formation true resistivity(RT)—along with corresponding physical property labels.Machine learning models are trained and evaluated to predict carbonate rock properties.The results demonstrate that GPR achieves the highest accuracy in porosity prediction,with a coefficient of determination(R~2)value of 0.7342,while RF proves to be the most accurate for permeability prediction.Despite these improvements,accurately predicting lowpermeability zones in heterogeneous carbonate rocks remains a significant challenge.Application of cross-validation techniques optimized the performance of GPR,resulting in an accuracy index(ACI)value of 0.9699 for porosity prediction.This study provides a novel framework that leverages machine learning techniques to improve the characterization of carbonate reservoirs.展开更多
As computer data grows exponentially,detecting anomalies within system logs has become increasingly important.Current research on log anomaly detection largely depends on log templates derived from log parsing.Word em...As computer data grows exponentially,detecting anomalies within system logs has become increasingly important.Current research on log anomaly detection largely depends on log templates derived from log parsing.Word embedding is utilized to extract information from these templates.However,this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing.Currently,specialized research on data imbalance across log template categories remains scarce.A dual-attention-based log anomaly detection model(LogDA),which leveraged data imbalance,was proposed to address these issues in the work.The LogDA model initially utilized a pre-trained model to extract semantic embedding from log templates.Besides,the similarity between embedding was calculated to discern the relationships among the various templates.Then,a Transformer model with a dual-attention mechanism was constructed to capture positional information and global dependencies.Compared to multiple baseline experiments across three public datasets,the proposed approach could improve precision,recall,and F1 scores.展开更多
Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit wh...Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit when operating in high-angle wells,limiting the ability to detect formations ahead of the drill bit.Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling.In this study,we examine the detection capabilities of various component combinations of magnetic dipole antenna.Based on the sensitivity of each component to the axial information,a coaxial component is selected as a boundary indicator.We investigate the impact of various factors,such as frequency and transmitter and receiver(TR) distance,under different geological models.This study proposes 5 and 20 kHz as appropriate frequencies,and 10-14 and 12-17 m as suitable TR distance combinations.The accuracy of the numerical calculation results is verified via air-sea testing,confirming the instrument's detection capability.A test model that eliminated the influence of environmental factors and seawater depth is developed.The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead.It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.展开更多
Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,...Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resou...China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resources or technical limitations,not only cause environmental issues like land subsidence and groundwater contamination but also pose critical safety risks for ongoing mining operations,including water inrushes,gas outbursts,and roof collapses.Conventional geophysical methods such as seismic surveys and electromagnetic detection demonstrate limited effectiveness in complex geological conditions due to susceptibility to electrical heterogeneity,electromagnetic interference,and interpretation ambiguities.This study presents an innovative integrated approach combining the Audio-Frequency Electrical Transillumination(AFET)method with multi-parameter borehole logging to establish a three-dimensional detection system.The AFET technique employs 0.1–10 kHz electromagnetic waves to identify electrical anomalies associated with goafs,enabling extensive horizontal scanning.This is complemented by vertical high-resolution profiling through borehole measurements of resistivity,spontaneous potential,and acoustic velocity.Field applications in Shanxi Province’s typical coal mines achieved breakthrough results:Using a grid-drilling pattern(15 m spacing,300 m depth),the method successfully detected three concealed goafs missed by conventional approaches,with spatial positioning errors under 0.5 m.Notably,it accurately identified two un-collapsed water-filled cavities.This surface-borehole synergistic approach overcomes single-method limitations,enhancing goaf detection accuracy to over 92%.The technique provides reliable technical support for safe mining practices and represents significant progress in precise detection of hidden geological hazards in Chinese coal mines,offering valuable insights for global mining geophysics.展开更多
Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approache...Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.展开更多
To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the p...To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the pre-trained large language model,the Gaia significantly improved its ability in extracting sequential patterns and spatial features from well-log curves.Leveraging the adapter method for fine-tuning,this model required training only about 1/70 of its original parameters,greatly improving training efficiency.Comparative experiments,ablation experiments,and generalization experiments were designed and conducted using well-log data from 250 wells.In the comparative experiment,the Gaia model was benchmarked against cutting-edge small deep learning models and conventional large language models,demonstrating that the Gaia model reduced the mean absolute error(MAE)by at least 20%.In the ablation experiments,the synergistic effect of the Gaia model's multiple components was validated,with its MAE being at least 30%lower than that of single-component models.In the generalization experiments,the superior performance of the Gaia model in blind-well predictions was further confirmed.Compared to traditional models,the Gaia model is significantly superior in accuracy and generalization for logging curve reconstruction,fully showcasing the potential of large language models in the field of well-logging.This provides a new approach for future intelligent logging data processing.展开更多
Heap memory anomalies,such as Use-After-Free(UAF),Double-Free,andMemory Leaks,pose critical security threats including system crashes,data leakage,and remote exploits.Existing methods often fail to handle multiple ano...Heap memory anomalies,such as Use-After-Free(UAF),Double-Free,andMemory Leaks,pose critical security threats including system crashes,data leakage,and remote exploits.Existing methods often fail to handle multiple anomaly types and meet real-time detection demands.To address these challenges,this paper proposes MemHookNet,a real-time multi-class heap anomaly detection framework that combines log hooking with deep learning.Without modifying source code,MemHookNet non-intrusively captures memory operation logs at runtime and transforms them into structured sequences encoding operation types,pointer identifiers,thread context,memory sizes,and temporal intervals.A sliding-window Long Short-Term Memory(LSTM)module efficiently filters out suspicious segments,which are then transformed into pointer access graphs for classification using a GATv2-based model.Experimental results demonstrate that MemHookNet achieves 82.2% accuracy and 81.5% recall with an average inference time of 15 ms,outperforming DeepLog and GLAD-PAW by 11.7% in accuracy and reducing latency by over 80%.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.展开更多
We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to...We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.展开更多
BACKGROUND The log odds of positive lymph nodes(LODDS)are correlated with survival outcomes in gastric cancer(GC)patients.However,the prognostic value across different tumor differentiation levels remains unclear.AIM ...BACKGROUND The log odds of positive lymph nodes(LODDS)are correlated with survival outcomes in gastric cancer(GC)patients.However,the prognostic value across different tumor differentiation levels remains unclear.AIM To evaluate the independent prognostic value of LODDS and the stratified predictive efficacy in GC patients with different histologic differentiations.METHODS We conducted a retrospective analysis of 2103 GC patients who underwent radical gastrectomy at Zhejiang Cancer Hospital.The prognostic value of LODDS was compared with that of other lymph node-based metrics,including the pathologic N stage,number of positive lymph nodes,number of total lymph nodes,and lymph node ratio,stratified by tumor differentiation.RESULTS LODDS was identified as an independent prognostic factor for overall survival in moderately to poorly differentiated GC patients.LODDS demonstrated superior predictive accuracy over other lymph node metrics.A nomogram incorporating LODDS,age,carbohydrate antigen(CA)125,carcinoembryonic antigen,and tumor differentiation showed good predictive accuracy(C-index=0.703).A higher LODDS was significantly associated with an increased risk of recurrence or metastasis,poorly differentiated tumors,advanced cancer,mucinous gastric adenocarcinoma,nerve invasion,and vascular tumor thrombus.Additionally,LODDS was positively correlated with the tumor markers CA19-9,CA72-4,CA125,and CA242(all P<0.05).CONCLUSION LODDS is an independent prognostic indicator for patients with moderately and poorly differentiated GC,and its predictive performance is superior to that of other models.展开更多
基金supported by the NSFC(Grant Nos.92046001,61962009)JSPS KAKENHI Grant Number JP20F20080+3 种基金the Natural Science Foundation of Inner Mongolia(2021MS06006)Baotou Kundulun District Science and technology plan project(YF2020013)Inner Mongolia discipline inspection and supervision big data laboratory open project fund(IMDBD2020020)the Scientific Research Foundation of North China University of Technology.
文摘In recent decades, log system management has been widely studied fordata security management. System abnormalities or illegal operations can befound in time by analyzing the log and provide evidence for intrusions. In orderto ensure the integrity of the log in the current system, many researchers havedesigned it based on blockchain. However, the emerging blockchain is facing significant security challenges with the increment of quantum computers. An attackerequipped with a quantum computer can extract the user's private key from thepublic key to generate a forged signature, destroy the structure of the blockchain,and threaten the security of the log system. Thus, blind signature on the lattice inpost-quantum blockchain brings new security features for log systems. In ourpaper, to address these, firstly, we propose a novel log system based on post-quantum blockchain that can resist quantum computing attacks. Secondly, we utilize apost-quantum blind signature on the lattice to ensure both security and blindnessof log system, which makes the privacy of log information to a large extent.Lastly, we enhance the security level of lattice-based blind signature under therandom oracle model, and the signature size grows slowly compared with others.We also implement our protocol and conduct an extensive analysis to prove theideas. The results show that our scheme signature size edges up subtly comparedwith others with the improvement of security level.
基金This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202101)NSFC(Grant Nos.62176273,61962009,U1936216)+3 种基金the Foundation of Guizhou Provincial Key Laboratory of Public Big Data(No.2019BDKFJJ010,2019BDKFJJ014)the Fundamental Research Funds for Beijing Municipal Commission of Education,Beijing Urban Governance Research Base of North China University of Technology,the Natural Science Foundation of Inner Mongolia(2021MS06006)Baotou Kundulun District Science and technology plan project(YF2020013)Inner Mongolia discipline inspection and supervision big data laboratory open project fund(IMDBD2020020).
文摘With the increasing popularity of cloud storage,data security on the cloud has become increasingly visible.Searchable encryption has the ability to realize the privacy protection and security of data in the cloud.However,with the continuous development of quantum computing,the standard Public-key Encryption with Keyword Search(PEKS)scheme cannot resist quantumbased keyword guessing attacks.Further,the credibility of the server also poses a significant threat to the security of the retrieval process.This paper proposes a searchable encryption scheme based on lattice cryptography using blockchain to address the above problems.Firstly,we design a lattice-based encryption primitive to resist quantum keyword guessing attacks.Moreover,blockchain is to decentralize the cloud storage platform’s jurisdiction of data.It also ensures that the traceability of keyword retrieval process and maintains the credibility of search result,which malicious platforms are prevented as much as possible from deliberately sending wrong search results.Last but not least,through security analysis,our proposed scheme satisfies the credibility and unforgeability of the keyword ciphertext.The comprehensive performance evaluates that our scheme has certain advantages in terms of efficiency compared with others.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
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.
文摘Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.
基金funded by Fundamental Research Funds for the Central Universities(No.00007851)。
文摘This study explores the application of machine learning techniques for predicting permeability,porosity,and flow zone indicator(FZI)in carbonate reservoirs using well log data,aiming to overcome the limitations of traditional empirical methods.Six machine learning algorithms are utilized:support vector machine(SVM),backpropagation(BP)neural network,gaussian process regression(GPR),extreme gradient boosting(XGBoost),K-nearest neighbor(KNN),and random forest(RF).The methodology involves classifying pore-permeability types based on the flow index,leveraging logging curves and geological data.Models are trained using seven logging parameters—spectral gamma rays(SGR),uranium-free gamma rays(CGR),photoelectric absorption cross-section index(PE),lithologic density(RHOB),acoustic transit time(DT),neutron porosity(NPHI),and formation true resistivity(RT)—along with corresponding physical property labels.Machine learning models are trained and evaluated to predict carbonate rock properties.The results demonstrate that GPR achieves the highest accuracy in porosity prediction,with a coefficient of determination(R~2)value of 0.7342,while RF proves to be the most accurate for permeability prediction.Despite these improvements,accurately predicting lowpermeability zones in heterogeneous carbonate rocks remains a significant challenge.Application of cross-validation techniques optimized the performance of GPR,resulting in an accuracy index(ACI)value of 0.9699 for porosity prediction.This study provides a novel framework that leverages machine learning techniques to improve the characterization of carbonate reservoirs.
基金funded by the Hainan Provincial Natural Science Foundation Project(Grant No.622RC675)the National Natural Science Foundation of China(Grant No.62262019).
文摘As computer data grows exponentially,detecting anomalies within system logs has become increasingly important.Current research on log anomaly detection largely depends on log templates derived from log parsing.Word embedding is utilized to extract information from these templates.However,this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing.Currently,specialized research on data imbalance across log template categories remains scarce.A dual-attention-based log anomaly detection model(LogDA),which leveraged data imbalance,was proposed to address these issues in the work.The LogDA model initially utilized a pre-trained model to extract semantic embedding from log templates.Besides,the similarity between embedding was calculated to discern the relationships among the various templates.Then,a Transformer model with a dual-attention mechanism was constructed to capture positional information and global dependencies.Compared to multiple baseline experiments across three public datasets,the proposed approach could improve precision,recall,and F1 scores.
基金co-funded by the National Key Research and Development Program of China under Grant (2019YFA0708301)the CAS Project for Young Scientists in Basic Research (Grant No.YSBR-082)Research Instrument and Equipment Development Project of Chinese Academy of Sciences (GJJSTD20210008)。
文摘Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit when operating in high-angle wells,limiting the ability to detect formations ahead of the drill bit.Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling.In this study,we examine the detection capabilities of various component combinations of magnetic dipole antenna.Based on the sensitivity of each component to the axial information,a coaxial component is selected as a boundary indicator.We investigate the impact of various factors,such as frequency and transmitter and receiver(TR) distance,under different geological models.This study proposes 5 and 20 kHz as appropriate frequencies,and 10-14 and 12-17 m as suitable TR distance combinations.The accuracy of the numerical calculation results is verified via air-sea testing,confirming the instrument's detection capability.A test model that eliminated the influence of environmental factors and seawater depth is developed.The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead.It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.
基金supported by National Natural Science Foundation of China(Nos.41872150,U2344209 and U19B6003)the PetroChina Southwest Oil and Gasfield Company(No.2020-54365)。
文摘Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
文摘China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resources or technical limitations,not only cause environmental issues like land subsidence and groundwater contamination but also pose critical safety risks for ongoing mining operations,including water inrushes,gas outbursts,and roof collapses.Conventional geophysical methods such as seismic surveys and electromagnetic detection demonstrate limited effectiveness in complex geological conditions due to susceptibility to electrical heterogeneity,electromagnetic interference,and interpretation ambiguities.This study presents an innovative integrated approach combining the Audio-Frequency Electrical Transillumination(AFET)method with multi-parameter borehole logging to establish a three-dimensional detection system.The AFET technique employs 0.1–10 kHz electromagnetic waves to identify electrical anomalies associated with goafs,enabling extensive horizontal scanning.This is complemented by vertical high-resolution profiling through borehole measurements of resistivity,spontaneous potential,and acoustic velocity.Field applications in Shanxi Province’s typical coal mines achieved breakthrough results:Using a grid-drilling pattern(15 m spacing,300 m depth),the method successfully detected three concealed goafs missed by conventional approaches,with spatial positioning errors under 0.5 m.Notably,it accurately identified two un-collapsed water-filled cavities.This surface-borehole synergistic approach overcomes single-method limitations,enhancing goaf detection accuracy to over 92%.The technique provides reliable technical support for safe mining practices and represents significant progress in precise detection of hidden geological hazards in Chinese coal mines,offering valuable insights for global mining geophysics.
基金funded by the Basic Science Centre Project of the National Natural Science Foundation of China(Grant No.72088101)supported by the Higher Education Commission,Pakistan(Grant No.20-14925/NRPU/R&D/HEC/2021-2021)+1 种基金the Researchers Supporting Project Number(Grant No.RSP2025R351)King Saud University,Riyadh,Saudi Arabia,for funding this research article.
文摘Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.
基金Supported by the National Natural Science Foundation of China(52288101)National Key R&D Program of China(2024YFF1500600)。
文摘To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the pre-trained large language model,the Gaia significantly improved its ability in extracting sequential patterns and spatial features from well-log curves.Leveraging the adapter method for fine-tuning,this model required training only about 1/70 of its original parameters,greatly improving training efficiency.Comparative experiments,ablation experiments,and generalization experiments were designed and conducted using well-log data from 250 wells.In the comparative experiment,the Gaia model was benchmarked against cutting-edge small deep learning models and conventional large language models,demonstrating that the Gaia model reduced the mean absolute error(MAE)by at least 20%.In the ablation experiments,the synergistic effect of the Gaia model's multiple components was validated,with its MAE being at least 30%lower than that of single-component models.In the generalization experiments,the superior performance of the Gaia model in blind-well predictions was further confirmed.Compared to traditional models,the Gaia model is significantly superior in accuracy and generalization for logging curve reconstruction,fully showcasing the potential of large language models in the field of well-logging.This provides a new approach for future intelligent logging data processing.
基金upported by Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education of China(No.KLCS20240211).
文摘Heap memory anomalies,such as Use-After-Free(UAF),Double-Free,andMemory Leaks,pose critical security threats including system crashes,data leakage,and remote exploits.Existing methods often fail to handle multiple anomaly types and meet real-time detection demands.To address these challenges,this paper proposes MemHookNet,a real-time multi-class heap anomaly detection framework that combines log hooking with deep learning.Without modifying source code,MemHookNet non-intrusively captures memory operation logs at runtime and transforms them into structured sequences encoding operation types,pointer identifiers,thread context,memory sizes,and temporal intervals.A sliding-window Long Short-Term Memory(LSTM)module efficiently filters out suspicious segments,which are then transformed into pointer access graphs for classification using a GATv2-based model.Experimental results demonstrate that MemHookNet achieves 82.2% accuracy and 81.5% recall with an average inference time of 15 ms,outperforming DeepLog and GLAD-PAW by 11.7% in accuracy and reducing latency by over 80%.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
基金financially supported by the Russian federal research project No.FWZZ-2022-0026“Innovative aspects of electro-dynamics in problems of exploration and oilfield geophysics”.
文摘We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the nearwellbore environment.The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy.The workflow is demonstrated through the modeling of wireline electromagnetic propagation resistivity logs,where the measured responses exhibit a highly nonlinear relationship with formation properties.The motivation for this research is the need for advanced modeling al-gorithms that are fast enough for use in modern quantitative interpretation tools,where thousands of simulations may be required in iterative inversion processes.The proposed algorithm achieves a remarkable enhancement in performance,being up to 3000 times faster than the finite element method alone when utilizing a GPU.While still ensuring high accuracy,this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.Furthermore,the algorithm’s efficiency positions it as a promising tool for stochastic Bayesian inversion,facilitating reliable uncertainty quantification in subsurface property estimation.
基金Supported by the National Natural Science Foundation of China,No.82473195 and No.32370797the Natural Science Foundation of Zhejiang Province,No.LTGY23H160018+3 种基金the Zhejiang Medical and Health Science and Technology Program,No.2024KY789 and No.2023KY615the National Research Center for Translational Medicine at Shanghai Program,No.NRCTM(SH)-2025-07the Beijing Science and Technology Innovation Medical Development Foundation,No.KC2023-JX-0270-07the Key Laboratory of Prevention,Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province,No.2022E10021.
文摘BACKGROUND The log odds of positive lymph nodes(LODDS)are correlated with survival outcomes in gastric cancer(GC)patients.However,the prognostic value across different tumor differentiation levels remains unclear.AIM To evaluate the independent prognostic value of LODDS and the stratified predictive efficacy in GC patients with different histologic differentiations.METHODS We conducted a retrospective analysis of 2103 GC patients who underwent radical gastrectomy at Zhejiang Cancer Hospital.The prognostic value of LODDS was compared with that of other lymph node-based metrics,including the pathologic N stage,number of positive lymph nodes,number of total lymph nodes,and lymph node ratio,stratified by tumor differentiation.RESULTS LODDS was identified as an independent prognostic factor for overall survival in moderately to poorly differentiated GC patients.LODDS demonstrated superior predictive accuracy over other lymph node metrics.A nomogram incorporating LODDS,age,carbohydrate antigen(CA)125,carcinoembryonic antigen,and tumor differentiation showed good predictive accuracy(C-index=0.703).A higher LODDS was significantly associated with an increased risk of recurrence or metastasis,poorly differentiated tumors,advanced cancer,mucinous gastric adenocarcinoma,nerve invasion,and vascular tumor thrombus.Additionally,LODDS was positively correlated with the tumor markers CA19-9,CA72-4,CA125,and CA242(all P<0.05).CONCLUSION LODDS is an independent prognostic indicator for patients with moderately and poorly differentiated GC,and its predictive performance is superior to that of other models.
