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Artificial intelligence for geoscience:Progress,challenges,and perspectives

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Development of a High-Precision Balancing Machine Algorithm Considering Dynamic Reliability Through Adaptive Dbscan

SSRN

引用

SSRN 2022年

作者： Jung, Joon Ha Kim, Byungock Na, Woong Jae Shin, Yun-Ho Department of Industrial Engineerin0g Ajou University Yeongtong-gu Suwon16499 Korea Republic of Department of System Dynamics Korea Institute of Machinery & Materials Yuseong-gu Daejeon34103 Korea Republic of Department of Big Data Chungbuk National University Chungju28644 Korea Republic of Department of Naval Architecture & Ocean Engineering Pusan National University Geumjeong-gu Busan46241 Korea Republic of

Recently, the demand for high-precision balancing of rotors has increased in the automobile industry, as more rotors are designed to rotate at ever-higher speeds to maximize energy efficiency. The accumulation of measurement uncertainty in the balancing process decreases the accuracy of the unbalanced mass estimation, which is the ultimate goal of balancing. Here, the problem of uncertainty is shown through Monte-Carlo simulation of signals acquired from an actual production line. To reduce the effect of measurement uncertainty in the balancing procedures, a signal-processing technique that increases the dynamic reliability of the signal is proposed. The suggested method is based on density-based spatial clustering of applications with noise (DBSCAN) with the use of the orthogonality-based averaging method. Specifically, by adjusting radius values, while clustering samples through the use of the DBSCAN method, the outliers that arise due to uncertainty are successfully removed. In this work, our proposed Adaptive DBSCAN method is validated by applying it to a balancing machine used for blower rotors in fuel cell electric vehicles. The results show that the estimated unbalanced mass derived by the influence coefficient method was as high as 0.71 kg-1s-2, while the proposed method reduced it to less than 0.05 kg-1s-2. In addition, the suggested procedure reduced the deviations of the unbalanced mass phase estimation by 35.2%, as compared to the results found by the conventional method. Consequently, through the validation test, the suggested method was found to have the largest vibration decrease of any method considered in the study. © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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A systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Lin, Tantao Ren, Zhijun Zhu, Linbo Zhu, Yongsheng Feng, Ke Ding, Weiping Yan, Ke Beer, Michael Xi'an Jiaotong University Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University Frontier Institute of Science and Technology Xi'an710049 China Xi'an Jiaotong University School of School of Chemical Engineering and Technology Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science Macau999078 China Leibniz University Institute for Risk and Reliability Hannover30167 Germany University of Liverpool Department of Civil and Environmental Engineering LiverpoolL69 3BX United Kingdom Tongji University International Joint Research Center for Resilient Infrastructure International Joint Research Center for Engineering Reliability and Stochastic Mechanics Shanghai200092 China

In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration. © 1963-2012 IEEE.

关键词： Information fusion

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RadioGAT: A Joint Model-based and data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

引用

arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Evolution of Electronic and Magnetic Properties in Topological Semimetal SmSbxTe2-x

arXiv

引用

arXiv 2022年

作者： Pandey, Krishna Basnet, Rabindra Wang, Jian Da, Bo Hu, Jin Materials Science and Engineering Program Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States Department of Physics University of Arkansas FayettevilleAR72701 United States Department of Chemistry Wichita State University WichitaKS67260 United States Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-1 Namiki Ibaraki Tsukuba305-0044 Japan

The ZrSiS-type materials have attracted intensive attention due to the existence of various topological fermions. The magnetic version of the ZrSiS-type materials, LnSbTe (Ln = lanthanides), is an ideal candidate to explore novel exotic states due to the interaction between magnetism and topology. In this work, we report the experimental study on structural, magnetic, thermodynamic, and electronic properties for SmSbxTe2-xwith various Sb content. The revealed evolutions of these properties with tuning the compositions would provide useful insights for the fundamental topological physics and the future applications. © 2022, CC BY.

关键词： Electronic properties

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking system Activities

Learner Action Patterns in the Problem-Solving Process Relat...

引用

International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media data models Recording data mining Problem-solving

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Performance of polysulfone membrane for biogas upgrading

引用

AIP Conference Proceedings 2024年第1期3071卷

作者： Shalita N. P. Wahyudhie Saiko R. Lusman Alvin R. Widyanto Nurul Widiastuti Triyanda Gunawan Hamzah Fansuri Yuly Kusumawati Syafsir Akhlus Afan Hamzah Azzah D. Pramata Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya 60111 Indonesia Department of Industrial Chemical Engineering Institut Teknologi Sepuluh Nopember 60111 Surabaya Indonesia Department of Material and Metallurgical Engineering Faculty of Industrial and Engineering System Technology Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

Palm oil production process generates abundant waste known as palm oil mill effluent (POME) that can naturally form biogas through the anaerobic digester (AD) process. The use of POME-based biogas as an alternative fuel, despite crucial to control the industry’s environmental impact, are still very limited because of the CO2 impurities in it. In this study polysulfone (PSf) membranes were prepared for CO2/CH4 gas separation. Membranes were prepared by phase inversion method with variations of PSf concentration in the casting solution of 18, 20, and 22 wt%. PSf concentration in casting solution was found to closely affect membranes CO2/CH4 separation performances, where both CO2 and CH4 permeability decreases as PSf concentration increases. However, increasing PSf concentration from 18 to 22 wt% was found to improve CO2/CH4 selectivity from 1.65 to 3.47. The separation performance improvement can be attributed to changes in membrane thickness and pore size as polymer concentration in casting solution changes. Gas permeation test result showed CH4 content in purified biogas to be as high as 77% of volume.

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Record High Temperatures in the Ocean in 2024

引用

Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Neuromodulated Meta-Learning

arXiv

引用

arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

引用

IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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