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Geological modeling of coalbed methane reservoirs in the tectonically deformed coal seam group in the Dahebian block,western Guizhou,China

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Frontiers of Earth Science 2024年第1期18卷 44-67页

作者： Yong SHU Shuxun SANG Xiaozhi ZHOU Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process(Ministry of Education) China University of Mining and TechnologyXuzhou 221116China Carbon Neutrality Institute China University of Mining and TechnologyXuzhou 221008China School of Resources and Geosciences China University of Mining and TechnologyXuzhou 221116China Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization China University of Mining and TechnologyXuzhou 221008China

The widely spread Carboniferous-Permian coal seam group in southern China has great potential for coalbed methane resources,but the extensively developed tectonically deformed coal seriously restricts its *** the Dahebian block in western Guizhou as the study area,the geological model of coalbed methane reservoirs in the tectonically deformed coal seam group was established,and the spatial distribution pattern of model parameters was clarified by clustering algorithms and factor *** facies model suggests that the main coal body structures in Nos.1,4,and 7 coal seams are cataclastic coal and granulated coal,whereas the No.11 coal seam is dominated by granulated coal,which has larger thicknesses and spreads more *** in situ permeability of primary undeformed coal,cataclastic coal,granulated coal,and mylonitized coal reservoirs are 0.333 mD,0.931 mD,0.146 mD,and 0.099 mD,respectively,according to the production performance analysis *** property model constructed by facies-controlled modeling reveals that Nos.1,4,and 7 coal seams have a wider high-permeability area,but the gas content is lower;the high-permeability area in the No.11 coal seam is more limited,but the gas content is *** results of the self-organizing map neural network and K-means clustering indicate that the geological model can be divided into 6 clusters,the model parameter characteristics of the 6 clusters are summarized by data analysis in combination with 6 factors extracted by factor analysis,and the application of data analysis results in multi-layer coalbed methane co-development is *** study provides ideas for the geological modeling in the tectonically deformed coal seam group and its data analysis.

关键词： geological modeling tectonically deformed coal coal seam group clustering algorithm Dahebian block,western Guizhou

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Three-dimensional modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

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Acta Geologica Sinica(English Edition) 2024年第3期98卷 736-752页

作者： DING Gaoming JI Genyuan YAN Guolong XU Yongzhong WANG Kunming XIAO Chun WANG Quanle GUO Dongbao China National Administration of Coal Geology Beijing 100038China MNR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral ResourcesCAGSBeijing 100037China China Gold group Geological Co. Ltd.Beijing 100011China China Gold group ZhongYuan mining Co. Ltd.SanmenxiaHenan 472000China Lanzhou Mineral Exploration Institute of Gansu Nonferrous Metal Geological Exploration Bureau Lanzhou 730046China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep *** approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking *** Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China *** borehole data on oreforming elements underpin deep geochemical *** methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

关键词： 3D geochemical model ore-forming elements geostatistics deep mineralization prediction Yechangping Mo deposit

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A NOVEL STOCHASTIC HEPATITIS B VIRUS EPIDEMIC MODEL WITH SECOND-ORDER MULTIPLICATIVE α-STABLE NOISE AND REAL data

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Acta Mathematica Scientia 2024年第2期44卷 752-788页

作者： Anwarud DIN Yassine SABBAR 吴鹏 Department of Mathematics Sun Yat-Sen UniversityGuangzhou510275China MAIS Laboratory MAMCS GroupFST ErrachidiaMoulay Ismail University of MeknesP.O.Box 509Errachidia52000Morocco School of Science Hangzhou Dianzi UniversityHangzhou310018China School of data Sciences Zhejiang University of Finance and EconomicsHangzhou310018China

This work presents an advanced and detailed analysis of the mechanisms of hepatitis B virus(HBV)propagation in an environment characterized by variability and *** on some biological features of the virus and the assumptions,the corresponding deterministic model is formulated,which takes into consideration the effect of *** deterministic model is extended to a stochastic framework by considering a new form of disturbance which makes it possible to simulate strong and significant *** long-term behaviors of the virus are predicted by using stochastic differential equations with second-order multiplicative α-stable *** developing the assumptions and employing the novel theoretical tools,the threshold parameter responsible for ergodicity(persistence)and extinction is *** theoretical results of the current study are validated by numerical simulations and parameters estimation is also ***,we obtain the following new interesting findings:(a)in each class,the average time depends on the value ofα;(b)the second-order noise has an inverse effect on the spread of the virus;(c)the shapes of population densities at stationary level quickly changes at certain values of α.The last three conclusions can provide a solid research base for further investigation in the field of biological and ecological modeling.

关键词： HBV model nonlinear perturbation probabilistic bifurcation long-run forecast numerical simulation

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Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

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International Journal of Minerals,Metallurgy and Materials 2024年第4期31卷 773-785页

作者： Mengwei Wu Wei Yong Cunqin Fu Chunmei Ma Ruiping Liu Department of Materials Science and Engineering China University of Mining&Technology(Beijing)Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical *** this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored *** modeling and feature modeling were used to predict the phase transition temperature of the shape memory *** experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)*** results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition *** this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between *** work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

关键词： machine learning support vector regression shape memory alloys martensitic transformation temperature

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An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

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工程与科学中的计算机建模(英文) 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： Key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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Enhancing 3D Reconstruction Accuracy of FIB Tomography data Using Multi‑voltage Images and Multimodal Machine Learning

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Nanomanufacturing and Metrology 2024年第1期7卷 48-60页

作者： Trushal Sardhara Alexander Shkurmanov Yong Li Lukas Riedel Shan Shi Christian J.Cyron Roland C.Aydin Martin Ritter Institute for Continuum and Material Mechanics Hamburg University of TechnologyHamburgGermany Electron Microscopy Unit Hamburg University of TechnologyHamburgGermany Institute of Materials Physics and Technology Hamburg University of TechnologyHamburgGermany Institute of Materials Mechanics Helmholtz-Zentrum HereonGeesthachtGermany Research group of Integrated Metallic Nanomaterials Systems Hamburg University of TechnologyHamburgGermany Institute of Material Systems modeling Helmholtz-Zentrum HereonGeesthachtGermany

FIB-SEM tomography is a powerful technique that integrates a focused ion beam(FIB)and a scanning electron microscope(SEM)to capture high-resolution imaging data of *** approach involves collecting in-plane SEM imagesand using FIB to remove material layers for imaging subsequent planes,thereby producing image ***,theseimage stacks in FIB-SEM tomography are subject to the shine-through effect,which makes structures visible from theposterior regions of the current *** artifact introduces an ambiguity between image intensity and structures in thecurrent plane,making conventional segmentation methods such as thresholding or the k-means algorithm *** study,we propose a multimodal machine learning approach that combines intensity information obtained at differentelectron beam accelerating voltages to improve the three-dimensional(3D)reconstruction of *** treatingthe increased shine-through effect at higher accelerating voltages as a form of additional information,the proposed methodsignificantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.

关键词： Multimodal machine learning Multi-voltage images FIB-SEM Overdeterministic systems 3D reconstruction FIB tomography

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A novel complex-high-order graph convolutional network paradigm:ChyGCN

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Chinese Physics B 2024年第5期33卷 665-672页

作者：郑和翔苗书宇顾长贵 Business School University of Shanghai for Science and TechnologyShanghai 200093China Ant group Shanghai 200000China

In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical *** limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of *** address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation *** model is realized by establishing a three-tier structure of ***,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential *** this second-order structure,truncation methods are used to assess and generate a three-layer composite *** the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different *** evaluate this model on several popular datasets and compare it with recent state-of-the-art *** comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.

关键词： raph convolutional network complex modeling complex hypergraph

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Enhancing Geological Understanding and Identifying Gold Anomalies in the Ailaoshan Orogen

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Acta Geologica Sinica(English Edition) 2024年第2期98卷 441-453页

作者： ZHAO Shiyu YANG Lin SONG Yiwei DONG Yuntao FENG Lihao LI Huajian WU Junyu WANG Qingfei State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesBeijing 100083China Xi’an Center of Mineral Resources Survey China Geological SurveyXi’an 710010China Yunnan Gold mining Industry group Kunming 650299China Jiangxi Provincial Key Laboratory of Genesis and Prospect for Strategic Minerals East China University of TechnologyNanchang 330013China Frontiers Science Center for Deep-time Digital Earth China University of Geosciences(Beijing)Beijing 100083China

The Ailaoshan Orogen in the southeastern Tibet Plateau,situated between the Yangtze and Simao blocks,underwent a complex structural,magmatic,and metamorphic evolution resulting in different tectonic subzones with varying structural lineaments and elemental *** elements can conceal or reduce anomalies due to the mutual effect between different anomaly *** the whole zone into subzones based on tectonic settings,ore cluster areas,or sample catchment basins(Scb),geochemical and structural anomalies associated with gold(Au)mineralization have been identified utilizing mean plus twice standard deviations(Mean+2STD),factor analysis(FA),concentration-area(CA)modeling of stream sediment geochemical data,and lineament density in both the Ailaoshan Orogen and the individual *** FA in the divided 98 Scbs with 6 Scbs containing Au deposits can roughly ascertain unknown rock types,identify specific element associations of known rocks and discern the porphyry or skarn-type Au *** with methods of Mean+2STD and C-A model of data in the whole orogen,which mistake the anomalies as background or act the background as anomalies,the combined methods of FA and C-A in the separate subzones or Scbs works well in regional metallogenic potential *** of lineament densities with a 10-km circle diameter is not suitable to locate Au deposits because of the delineated large areas of medium-high lineament *** contrast,the use of circle diameters of 1.3 km or 1.7 km in the ore cluster scale delineates areas with a higher concentration of lineament density,consistent with the locations of known Au *** analyzing the map of faults and Au anomalies,two potential prospecting targets,Scbs 1 and 63 with a sandstone as a potential host rock for Au,have been identified in the Ailaoshan *** use of combined methods in the divided subzones proved to be more effective in improving geological understanding and identifying

关键词： minerology geochemistry gold stream sediment sample catchment basin concentration-area model lineament density Ailaoshan Orogen

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A Probabilistic Approach for Predicting Vessel Motion

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1877-1879页

作者： Qi Hu Jingyi Liu Zongyu Zuo the 7th Research Division Beihang University (BUAA) the 54th Research Institute of China Electronics Technology group Corporation

Dear Editor,This letter addresses the challenge of forecasting the motion of real-world vessels over an extended period with a limited amount of available data. By employing stochastic differential equation (SDE)modeling, we integrate both deterministic and stochastic components of the available information. Subsequently, we establish a recursive prediction methodology based on Bayes’ rule to update the model state when new measurements are received. Furthermore, we develop a stochastic model tailored specifically to vessel dynamics and introduce an approximation method to tackle computational complexities.

关键词： Stochastic models

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping grouping of Decision Variables

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Computer modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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