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The influence of Tethyan evolution on changes of the Earth's past environment

Science China Earth Sciences 2023年第12期66卷 2653-2665页

作者： Bo WAN Fuyuan WU Rixiang ZHU State Key Laboratory of Lithospheric Evolution Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China

Understanding changes in Earth's past can provide valuable insights into prediction of its *** example is the interactions between the internal and external spheres of *** cyclical northward breakup-drift of Gondwana,driven by the opening and closure of Proto-,Paleo-,and Neo-Tethyan oceans,facilitated the transfer of landmasses from the southern to the northern hemisphere,traversing the tropic *** have observed a compelling correlation between episodic increases in landmass area within the tropic regions(those lying at less than 20°latitude)and a subsequent temperature decrease during the three major glacial periods in the last 500 million *** phenomenon can be attributed to low latitude regions receiving more solar energy influx on Earth's surface than high latitude *** addition,an increase of landmass in tropic regions(low latitude)attenuates the net energy absorption by the Earth's surface,consequently impeding the conduction and convection of absorbed energy toward the *** result is a decrease in global surface *** tropic regions,benefiting from abundant sunlight,create an ideal environment for the proliferation of marine plankton *** species are important in the generation of organic-rich *** biological debris is therefore deposited on continental margins when a continent drifts across the tropic *** creates favorable conditions for future hydrocarbon and reservoir *** subduction of organic-rich sediments during the closure of the Tethyan oceans results in the generation of mafic arc magmas with low oxygen *** chemical environment helps the mineralization of reduced-type ore deposits such as tungsten,tin,and ***-driven plate tectonics in the Tethys realm changes the distribution of oceans and landmass,subsequently affecting the balance and distribution of solar energy across Earth's *** changes trigger consequential environmental shif

关键词： Tethyan geodynamics Earth system Change of sea-land distribution Reduced ore deposit Hydrocarbon

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Synthetic information prediction system for crisis mine based on GIS

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Global Geology 2006年第1期9卷 94-99页

作者： Yuxin Ye Ping Yu Shi Wang Shuisheng Ye institute of Ideal Information Technology Northeast Normal University Changchun 130024 Jilin China Foreign Languages Teaching and Researching Center Jilin University Changchun 130012 Jilin China Mineral Resources institute of Comprehensive Information prediction Jilin University Changchun 130026 JilinChina

Reserves of some kinds of the crisis mines will be lack now or from now on, because of lacking seriously reserves of mineral resources and the crisis of exploring bases in support. So that it is urgent to predict, appraise, development and utilize the replaceable resources of the crisis mines. The mineral resources prediction software system of synthetic information is intelligent GIS which is used to quantitative prediction of large-scale synthetic information mineral target. It takes the geological body and the mineral resource body as a unit. And it analyzes the ore deposit genesis and metallotect, knows the spatial distribution laws of the ore deposit and ore body, and establish the prospecting model based on the concept of establishing the three-dimensional space of a mine. This paper will primarily discuss some important problems as follows: the secondary development of various kinds of data(including geology, geophysical prospecting, geochemical prospecting and remote sensing, etc); process synthetically and establish the synthetic information interpretative map base; correspond prospecting model with synthetic information of ore deposit; divided into statistical units of metallogenic information synthetic anomalies based on the synthetic information anomalies of ore control, then research the metallogenic information variable of unit synthetically and make quantitative prediction according to choose the quantitative prediction math model which is suitable to the demands of large-scale precision; at last, finish the target area optimization of ore deposit (body).

关键词：合成信息软件系统危机矿量的预言

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Nanogeology in China:A review

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China Geology 2018年第2期1卷 286-303页

作者： Yi-wen Ju Cheng Huang Yan Sun Cai-neng Zou Hong-ping He Quan Wan Xue-qiu Wang Xian-cai Lu Shuang-fang Lu Jian-guang Wu Hong-tai Chao Hai-ling Liu Jie-shan Qiu Fei Huang Hong-jian Zhu Jian-chao Cai Yue Sun Key Laboratory of Computational Geodynamics Chinese Academy of SciencesCollege of Earth and Planetary SciencesUniversity of Chinese Academy of SciencesBeijing 100049 China Key Laboratory of Marginal Sea Geology of South China Sea institute of Oceanology Chinese Academy of SciencesCruangzhou 510301China Carbon Research Laboratory School of Chemical EngineeringDalian University of TechnologyDalian 116012China Key Laboratory of Ministry of Education on Safe Mining of Deep MetalMines School of Resources and Civil EngineeringNorthwestern UniversityShenyang 110819China institute of Geophysies and Geomatics China University ofGeosciences(Wuhan)Wuhan 430074China School of Earth Sciences and Engineering State Key Laboratory for Mineral Deposits ResearchNanjing UniversityNanjing 210093China PetroChina Research institute of Petroleum Exploration and Development Beijing 100083China Guangzhou institute of Geochemistry Chinese Academy of SciencesGuangzhou 510640China State Key Laboratory of ore Deposit Geochemistry Institute of GeochemistryChinese Academy of SciencesGuiyang 550081China institute of Geophysical and Geochemical Exploration CAGSKey Laboratory of Geochemical ExplorationMinistry of Land and ResourcesLangfang 065000China Research institute of UnconventionalPetroleum and Renewable Energy Chinese University of Petroleum (East China)Qingdao 266580China China United CoalbedMethane Co. Ltd.Beijing 100011China Earthquake Administration of Shandong Province Jinan 250014China

Nanogeology is a subject that is a combination of geology and nanoscale science,and it has been a frontier field in recent *** is also a new subject with the features of intersectionality and multidisciplinary. Digging deeper into geological problems and nanoscale phenomena helps better revealing the more essential mechanisms and processes in geological science,which is also an evitable path in the development of *** this paper,we elaborate the concept,feature and main subdisciplines,and summarize three stages ofnanogeology development from preliminary research in the 1990s to subject formation in *** summarizing the researchers'achievements in this field,we illustrate some primary research progresses of nanogeology in China as eight *** the basis of the above content,we propose the development prospect of nanogeology in *** are many geologic problems with scientific values and economic benefits,such as research of geologic fundamental problems,resource exploration and development,mechanism study and prediction of geological activities (disasters),mechanism research and management of environmental pollution and *** has a great potential in China to solve all of these *** a result,the theories and methods of nanogeology will become enriching and *** offers important theoretical basis and technological methods to deal with major issues concerning the national economy and the people's livelihoods,such as the prediction of geological activities,as well as resource dislribution and its exploration and utilization.

关键词： Nanogeology Nanoparticle Nanostructure Scientific frontier Research progress Research prospect

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Features and significance of the Yudai porphyry copper deposit in the Kalatag district,Eastern Tianshan,NW China

Features and significance of the Yudai porphyry copper depos...

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第三届全国青年地质大会

作者： Lei Chen Jingbin Wang Xiaohua Deng Xiangbin Wu Qigui Mao Yan Su School of Geosciences and Info-Physics Central South University Key Laboratory of Metallogenic prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education Beijing institute of Geology for Mineral Resources

1 Introduction The eastern Tianshan is located in the eastern of Xinjiang,bordered by Xiaorequanzi to the west and Gansu-Xinjiang border area to the east,with an area of about 60000km,and is an important ore concentration area of Cu,Ni,Au,Fe,Pb and Zn in China(Wang,et al.,2006).The Dannanhu arc belt is one of the most significant metallogenic belts of the eastern *** important porphyry deposits have been

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Influence of Na_(2)SiO_(3)/NaOH Ratio on Calcined Magnesium Silicate Based Geopolymer——Experimental and Predictive Study

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Journal of Wuhan University of Technology(Materials Science) 2023年第5期38卷 1077-1085页

作者： Premkumar R Ramesh Babu Chokkalingam Meyyappan PL Shanmugasundaram M Kalasalingam Academy of Research and Education Krishnan KoilTamil Nadu626126 India Vellore institute of Technology-Chennai Tamilnadu600127 India

This study aims to investigate the behavior of alkali activated mortar,which is made of naturally available magnesium silicate as source *** magnesium silicate,ultrafine natural steatite powder(UFNSP)is used as the primary source of binder,and the activation is initiated through the alkali liquid which is proportioned in various combinations of silicate to hydroxide ratio(Na_(2)SiO_(3)/Na OH)ratio,and this ratio in this study varies from 1 to *** UFNSP is calcined at two difierent temperatures,700 and 1000℃.The mortar mix is proportioned as 1:3 between powder and the fine aggregate,and the mortar is prepared with hydroxide molarity(M)of 10 *** mortar is cured for 48 hours at 60℃and the compressive strength was *** the mix were studied for its microstructural behavior along with compressive *** mix proportion of the mortar,and the results obtained through microstructural characterization were combinedly formed as input for artificial neural network(ANN)predictive *** model is designed to predict the compressive strength,which is trained through Bayesian regularization algorithm with varying hidden neurons of 7 to *** experimental and predictive study shows that the strength is influenced by both Na_(2)SiO_(3)/Na OH ratio and calcination *** the ANN is influenced by mainly calcination temperature and uncorrelation occurs in selected samples of 1000℃calcined UFNSP mix.

关键词： magnesium silicate silicate alkali activation geopolymer UFNSP prediction

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基于频域稀疏分解的大地电磁工频干扰压制（英文）

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Journal of Central South University 2018年第9期25卷 2150-2163页

作者：汤井田李广周聪李晋刘晓琼朱会杰 School of Geosciences and Info-Physics Central South UniversityChangsha 410083China Key Laboratory of Metallogenic prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education(Central South University)Changsha 410083China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China The First Engineering Scientific Research institute of General Armaments Department Wuxi 214035China

工频干扰是指由电网产生的基频及其奇次谐波干扰,它是大地电磁信号采集过程中最为普遍的干扰之一。尽管大部分采集设备都设计有抑制工频干扰的陷波电路,但由于电网中电流的实际频率会随着负载的变化有所波动,而陷波器的中心频率是固定的... 详细信息

工频干扰是指由电网产生的基频及其奇次谐波干扰,它是大地电磁信号采集过程中最为普遍的干扰之一。尽管大部分采集设备都设计有抑制工频干扰的陷波电路,但由于电网中电流的实际频率会随着负载的变化有所波动,而陷波器的中心频率是固定的,因此在实际采集时,大地电磁信号依然受到工频噪声的严重影响。实践经验表明,当受工频干扰影响时,远参考法时常难以奏效;工频干扰奇次谐波的幅值随着频率的增大而骤减,在时间域难以准确识别,因此时间域编辑法效果不佳;此外,由于干扰源是固定的,工频干扰通常存在于整个采集过程中,通过数据段筛选也无法去除噪声。本文基于频域稀疏分解,先对采集的大地电磁信号进行傅里叶变换。然后设计与干扰信号相匹配而对有用信号不敏感的冗余字典原子,结合IOMP算法分离出频域信号中的工频干扰成分。最后将处理后的频域信号进行傅里叶逆变换。仿真实验及案例分析表明,所述方法能够在较好地保留有用信号的前提下有效压制工频干扰,在大大降低时间消耗的基础上取得比时域稀疏分解更好的效果,改善大地电磁数据质量。

关键词：稀疏分解大地电磁信号去噪工频干扰改进的正交匹配追踪冗余字典

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Online prediction of network-level public transport demand based on principle component analysis

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Communications in Transportation Research 2023年第1期3卷 62-71页

作者： Cheng Zhong Peiling Wu Qi Zhang Zhenliang Ma Department of Civil and Architectural Engineering KTH Royal Institute of TechnologyStockholm10044Sweden

Online demand prediction plays an important role in transport network services from operations,controls to management,and information ***,the online prediction models are impacted by streaming data quality issues with noise measurements and missing *** address these,we develop a robust prediction method for online network-level demand prediction in public *** consists of a PCA method to extract eigen demand images and an optimization-based pattern recognition model to predict the weights of eigen demand images by making use of the partially observed real-time data up to the prediction time in a *** prediction model is robust to data quality issues given that the eigen demand images are stable and the predicted weights of them are optimized using the network level data(less impacted by local data quality issues).In the case study,we validate the accuracy and transferability of the model by comparing it with benchmark models and evaluate the robustness in tolerating data quality issues of the proposed *** experimental results demonstrate that the proposed Pattern Recognition prediction based on PCA(PRP-PCA)consistently outperforms other benchmark models in accuracy and ***,the model shows high robustness in accommodating data quality *** example,the PRP-PCA model is robust to missing data up to 50%regardless of the noise *** also discuss the hidden patterns behind the network level *** visualization analysis shows that eigen demand images are significantly connected to the network structure and station activity *** the demand changes dramatically before and after the pandemic,the eigen demand images are consistent over time in Stockholm.

关键词： Network-level demand prediction Data quality issues Eigen demand image Pattern recognition Principle component analysis

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Novel hybrid method to predict the ground-displacement field caused by shallow tunnel excavation

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Science China(Technological Sciences) 2023年第1期66卷 101-114页

作者： KONG FanChao LU DeChun MA YiDing TIAN Tao YU HaiTao DU XiuLi institute of Geotechnical and Underground Engineering Bejing University of TechnologyBejing 100124China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji UniversityShanghai 200092China

Based on machine-learning(ML) and analytical methods, a hybrid method is developed herein to predict the ground-displacement field(GDF) caused by tunneling. The extreme learning machine(ELM), as a single hidden layer feedforward neural network, is used as an ML model to predict maximum settlement smaxof the ground surface. The particle swarm optimization(PSO) algorithm is applied to optimize the parameters for the ELM method, namely, weight and bias values from the input layer to the hidden layer. The mean square error of the k-fold cross validation sets is considered the fitness function of the PSO algorithm. For 38 data samples from published papers, 30 samples are used as the training set, and 8 samples are used as the test set. For the test samples, the error of five samples ranges between-5 and 5 mm. The error of only one sample is slightly greater than 10 mm. The proposed PSO-ELM method demonstrates good prediction performance of smax. A deformation parameter of the nonuniform displacement mode for the tunnel cross-section is calibrated based on predicted smax. When the determined nonuniform displacement mode is used as the boundary condition of the tunnel cross-section, the GDF of a shallow circular tunnel is analytically predicted based on the complex-variable method prior to tunnel excavation. For a specific engineering case,i.e., the Heathrow Express tunnel, the proposed PSO-ELM-analytical method can well predict the surface-settlement trough curve, horizontal displacements at different depths, and vertical displacements above the tunnel.

关键词： extreme learning machine particle swarm optimization complex-variable method analytical solution of a shallow tunnel prediction method of ground-displacement field

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Artificial neural network approach for prediction of stress–strain curve of near b titanium alloy

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Rare Metals 2014年第3期33卷 249-257页

作者： Srinivasu Gangi Setti R.N.Rao Mechanical Engineering National Institute of Technology

In the present study, artificial neural network（ANN） approach was used to predict the stress-strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network（NN） to predict the stress-strain curve. In order to achieve this, three different NN architectures（feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network）, three different transfer functions（purelin, Log-Sigmoid, and Tan-Sigmoid）, number of hidden layers（1 and 2）, number of neurons in the hidden layer（s）,and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an *** system was trained using the prepared training set（a,16 % a, 40 % a, and b stress-strain curves）. After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress-strain curve of near b titanium alloy.

关键词： Artificial neural network (ANN) Stressstrain curve Titanium alloys

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Improved deep mixed kernel randomized network for wind speed prediction

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Clean Energy 2023年第5期7卷 1006-1031页

作者： Vijaya Krishna Rayi Ranjeeta Bisoi S.P.Mishra P.K.Dash Department of EEE GMR Institute of TechnologyRajamAndhra PradeshIndia Multidisciplinary Research Cell Siksha‘O’Anusandhan Deemed to be UniversityBhubaneswarOdishaIndia

Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric *** there are several intelligent techniques in the literature for wind speed prediction,their accuracies are not yet very ***,in this paper,a new hybrid intelligent technique named the deep mixed kernel random vector functional-link network auto-encoder(AE)is proposed for wind speed *** proposed method eliminates manual tuning of hidden nodes with random weights and biases,providing prediction model generalization and representation *** reduces reconstruction error due to the exact inversion of the kernel matrix,unlike the pseudo-inverse in a random vector functional-link network,and short-ens the execution ***,the presence of a direct link from the input to the output reduces the complexity of the prediction model and improves the prediction *** kernel parameters and coefficients of the mixed kernel system are optimized using a new chaotic sine–cosine Levy flight optimization *** lowest errors in terms of mean absolute error(0.4139),mean absolute percentage error(4.0081),root mean square error(0.4843),standard deviation error(1.1431)and index of agreement(0.9733)prove the efficiency of the proposed model in comparison with other deep learning models such as deep AEs,deep kernel extreme learning ma-chine AEs,deep kernel random vector functional-link network AEs,benchmark models such as least square support vector machine,autoregressive integrated moving average,extreme learning machines and their hybrid models along with different state-of-the-art methods.

关键词： deep neural network mixed kernel random vector functional network auto-encoder chaotic sine-cosine Levy flight optimization single and multistep wind speed prediction

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