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深海热液循环系统是理解地球物质循环与极端生态系统演化的关键环节,然而其特殊的环境条件长期制约了有效观测与深入研究的开展。近年来,机器学习技术在地球科学领域的应用不断拓展,为深海热液系统研究提供了全新的思路。本文系统梳理了机器学习技术在该领域中的主要应用进展,重点包括:分类模型在海底热液喷口识别和基底岩性判别中的应用,预测模型在多金属硫化物分布及热液生物栖息模式分析中的探索,以及图像识别技术在海底影像与生态系统结构处理的研究。在总结既有成果的基础上,本文进一步指出当前研究面临的关键问题,包括深海数据稀缺、样本时空偏差明显、模型泛化能力不足与可解释性有限等。结合地学数据与大语言模型的发展趋势,未来可重点关注:(1)完善深海观测与数据共享机制,提升数据获取效率与质量;(2)开发适配深海复杂环境的专用模型,提升算法适应性与稳定性;(3)推动跨学科融合,构建多源数据驱动的一体化研究框架。
Abstract:Deep-sea hydrothermal circulation systems play a crucial role in understanding the Earth's material cycling and the evolution of extreme ecosystems. However, their unique environments have long constrained effective observation and in-depth study. In recent years, the application of machine learning techniques in geosciences has been expanded, providing new perspectives for exploring deep-sea hydrothermal systems. This paper systematically reviews recent advances in this area, focusing on the classification for hydrothermal vent detection and basement lithology identification, prediction for polymetallic sulfide distribution, analysis of hydrothermal biological habitat, and image recognition of subsea imagery, and ecosystem structure investigation. Additional to significant achievements made, challenges in data scarcity, spatiotemporal sampling bias, limited model generalization, and poor interpretability remain. To address these issues, future research should focus on improving deep-sea observation and data-sharing mechanisms, developing models specified for the complex deep-sea environment, and promoting interdisciplinary integration to construct a multi-source, data-driven research framework.
[1]CORLISS J B,LYLE M,DYMOND J,et al. The chemistry of hydrothermal mounds near the Galapagos Rift[J]. Earth and Planetary Science Letters,1978,40(1):12-24.
[2]ZHANG T,LI J B,NIU X W,et al. Highly variable magmatic accretion at the ultraslow-spreading Gakkel Ridge[J]. Nature,2024,633(8028):109-113.
[3]LIANG J, TAO C H, KIM J, et al. Morphology of sulfide structures in the active hydrothermal fields of Indian Ocean ridges and its geological implications[J]. Deep Sea Research Part I:Oceanographic Research Papers,2024,203:104215.
[4]FRÜH-GREEN G L,KELLEY D S,LILLEY M D,et al. Diversity of magmatism, hydrothermal processes and microbial interactions at mid-ocean ridges[J]. Nature Reviews Earth&Environment,2022,3(12):852-871.
[5]PETERSEN S,LEHRMANN B,MURTON B J. Modern seafloor hydrothermal systems:new perspectives on ancient oreforming processes[J]. Elements,2018,14(5):307-312.
[6]DICK H J, LIN J, SCHOUTEN H. An ultraslow-spreading class of ocean ridge[J]. Nature,2003,426(6965):405-412.
[7]LOWELL R P,RONA P A,VON HERZEN R P. Seafloor hydrothermal systems[J]. Journal of Geophysical Research:Solid Earth,1995,100(B1):327-352.
[8]DICK G J. The microbiomes of deep-sea hydrothermal vents:distributed globally,shaped locally[J]. Nature Reviews Microbiology,2019,17(5):271-283.
[9]FISHER A. Marine hydrogeology:recent accomplishments and future opportunities[J]. Hydrogeology Journal, 2005, 13:69-97.
[10]HANNINGTON M D,DE RONDE C E J,PETERSEN S. Seafloor tectonics and submarine hydrothermal systems[M]//HEDENQUIST J W,THOMPSON J F H,GOLDFARB R J,et al. Economic Geology 100th Anniversary Volume. Littleton:Society of Economic Geologists,2005:111-141
[11]TAO C H,SEYFRIED JR W,LOWELL R,et al. Deep hightemperature hydrothermal circulation in a detachment faulting system on the ultra-slow spreading ridge[J]. Nature Communications,2020,11(1):1300.
[12]陶春辉,郭志馗,梁锦,等.超慢速扩张西南印度洋中脊硫化物成矿模型[J].中国科学:地球科学,2023,66(6):1212-1230.TAO C H, GUO Z K, LIANG J, et al. Sulfide metallogenic model on the ultraslow-spreading Southwest Indian Ridge[J].Science China Earth Sciences,2023,66(6):1212-1230.
[13]李家彪,王叶剑,李小虎.现代海底热液硫化物成矿地质学[M].北京:科学出版社,2017.LI J B,WANG Y J,LI X H. Modern Seafloor Hydrothermal Sulfide Mineralization Geology[M]. Beijing:Science Press.2017.
[14]MONECKE T, PETERSEN S, HANNINGTON M D, et al.The minor element endowment of modern sea-floor massive sulfides and comparison with deposits hosted in ancient volcanic successions[M]//VERPLANCK P L,HITZMAN M W.Rare Earth and Critical Elements in Ore Deposits. Littleton:Society of Economic Geologists,2016.
[15]HANNINGTON M,JAMIESON J,MONECKE T,et al. The abundance of seafloor massive sulfide deposits[J]. Geology,2011,39(12):1155-1158.
[16]侯增谦,韩发,夏林圻,等.现代与古代海底热水成矿作用[M]:北京:地质出版社,2003.HOU Z Q,HAN F,XIA L Q,et al. Hydrothermal Systems and Metallogeny on the Modern and Ancient Sea-Floor[M].Beijing:Geological Publishing House. 2003.
[17]CATHLES L M. What processes at mid-ocean ridges tell us about volcanogenic massive sulfide deposits[J]. Mineralium Deposita,2011,46:639-657.
[18]OHTA Y,GOTO T N,KOIKE K,et al. Correlation between induced polarization and sulfide content of rock samples obtained from seafloor hydrothermal mounds in the Okinawa Trough,Japan[J]. Earth,Planets and Space,2024,76(1):54.
[19]BEAULIEU S E,SZAFRAŃSKI K M. InterRidge global database of active submarine hydrothermal vent fields version 3.4[EB/OL].[2025-03-24]. https://doi.org/10.1594/PANGAEA.917894.
[20]WELDEGHEBRIEL M F,LOWENSTEIN T K. Seafloor hydrothermal systems control long-term changes in seawater[Li+]:evidence from fluid inclusions[J]. Science Advances, 2023,9(30):eadf1605.
[21]LIAO S L,TAO C H,JAMIESON J W,et al. Oxidizing fluids associated with detachment hosted hydrothermal systems:example from the Suye hydrothermal field on the ultraslowspreading Southwest Indian Ridge[J]. Geochimica et Cosmochimica Acta,2022,328:19-36.
[22]LIAO S L,TAO C H,DIASÁA,et al. Sediment geochemistry reveals abundant off-axis hydrothermal fields on the ultraslow-spreading Southwest Indian Ridge[J]. Earth and Planetary Science Letters,2024,643:118916.
[23]POLYMENAKOU P N,NOMIKOU P,HANNINGTON M,et al. Taxonomic diversity of microbial communities in sub-seafloor hydrothermal sediments of the active Santorini-Kolumbo volcanic field[J]. Frontiers in Microbiology, 2023, 14:1188544.
[24]CHOI S K,PAK S J,KIM J,et al. Rare earth element systematics of chimney anhydrite from seafloor hydrothermal vents[J]. Ore Geology Reviews,2024:105984.
[25]RASMUSSEN B,MUHLING J R. Organic carbon generation in 3.5-billion-year-old basalt-hosted seafloor hydrothermal vent systems[J]. Science Advances,2023,9(5):eadd7925.
[26]EVANS G N,COOGAN L A,KAÇAR B,et al. Molybdenum in basalt-hosted seafloor hydrothermal systems:experimental,theoretical, and field sampling approaches[J]. Geochimica et Cosmochimica Acta,2023,353:28-44.
[27]HOU J L,SIEVERT S M,WANG Y,et al. Microbial succession during the transition from active to inactive stages of deepsea hydrothermal vent sulfide chimneys[J]. Microbiome,2020,8:1-18.
[28]LI J T,CUI J M,YANG Q H,et al. Oxidative weathering and microbial diversity of an inactive seafloor hydrothermal sulfide chimney[J]. Frontiers in Microbiology,2017,8:1378.
[29]BEINART R A, ARELLANO S M, CHAKNOVA M, et al.Deep seafloor hydrothermal vent communities buried by volcanic ash from the 2022 Hunga eruption[J]. Communications Earth&Environment,2024,5(1):254.
[30]ZHOU Y,LIU H L,FENG C G,et al. Genetic adaptations of sea anemone to hydrothermal environment[J]. Science advances,2023,9(42):eadh0474.
[31]MACDONALD A, MACDONALD A. Hydrothermal vents:the inhabitants,their way of life and their adaptation to high pressure[J]. Life at High Pressure:In the Deep Sea and Other Environments,2021:231-270.
[32]王国荣,黄泽奇,周守为,等.深海矿产资源开发装备现状及发展方向[J].中国工程科学,2023,25(3):1-12.WANG G R,HUANG Z Q,ZHOU S W,et al. Current status and development direction of deep-sea mineral resource exploitation equipment[J]. Strategic Study of CAE,2023,25(3):1-12.
[33]张鑫,李超伦,李连福.深海极端环境原位探测技术研究现状与对策[J].中国科学院院刊,2022,37(7):932-938.ZHANG X,LI C L,LI L F. In situ detection technology for deep sea extreme environment:research status and strategies[J]. Bulletin of Chinese Academy of Sciences,2022,37(7):932-938.
[34]PELLETER E L,PRINCIPAUD M,ALIX A S,et al. Diversity,spatial distribution and evolution of inactive and weakly active hydrothermal deposits in the TAG hydrothermal field[J]. Frontiers in Earth Science,2024,12:1304993.
[35]YU J Y,TAO C H,LIAO S L,et al. Resource estimation of the sulfide-rich deposits of the Yuhuang-1 hydrothermal field on the ultraslow-spreading Southwest Indian Ridge[J]. Ore Geology Reviews,2021,134:104169.
[36]GRABER S,PETERSEN S,YEO I,et al. Structural control,evolution, and accumulation rates of massive sulfides in the TAG hydrothermal field[J]. Geochemistry, Geophysics, Geosystems,2020,21(9):e2020GC009185.
[37]CHERKASHOV G, POROSHINA I, STEPANOVA T, et al.Seafloor massive sulfides from the northern equatorial Mid-Atlantic Ridge:new discoveries and perspectives[J]. Marine Georesources and Geotechnology,2010,28(3):222-239.
[38]罗洪明,韩喜球,王叶剑,等.全球现代海底块状硫化物战略性金属富集机理及资源前景初探[J].地球科学,2021,46(9):3123-3138.LUO H M,HAN X Q,WANG Y J,et al. Preliminary study on the enrichment mechanism of strategic metals and their resource prospects in global modern seafloor massive sulfide deposits[J]. Earth Sciences,2021,46(9):3123-3138
[39]SHEN C, APPLING A P, GENTINE P, et al. Differentiable modelling to unify machine learning and physical models for geosciences[J]. Nature Reviews Earth&Environment, 2023,4(8):552-567.
[40]TAKAEW P,XIA J C,DOUCET L S. Machine learning and tectonic setting determination:bridging the gap between earth scientists and data scientists[J]. Geoscience Frontiers, 2024,15(1):101726.
[41]KONG Y H, CHEN G D, LIU B L, et al. 3D mineral prospectivity mapping of Zaozigou gold deposit, west Qinling,China:machine learning-based mineral prediction[J]. Minerals,2022,12(11):1361.
[42]SHIRMARD H,FARAHBAKHSH E,MÜLLER R D,et al. A review of machine learning in processing remote sensing data for mineral exploration[J]. Remote Sensing of Environment,2022,268:112750.
[43]ZHU G D,NIU Y Y,LIAO S B,et al. Discrimination of quartz genesis based on explainable machine learning[J]. Minerals,2023,13(8):997.
[44]SUN G,ZENG Q,ZHOU J X. Machine learning coupled with mineral geochemistry reveals the origin of ore deposits[J]. Ore Geology Reviews,2022,142:104753.
[45]陶春辉,梁锦,王汉闯,等.洋中脊多金属硫化物勘查方法与技术[M].北京:科学出版社,2018.TAO C H,LIANG J,WANG H C,et al. Exploration Methods and Techniques for Polumetallic Sulfide on the Mid-Ocean Ridges[M]. Beijing:Science Press. 2018.
[46]WANG X,CAO Y P,WU S J,et al. Real-time detection of deep-sea hydrothermal plume based on machine vision and deep learning[J]. Frontiers in Marine Science, 2023, 10:1124185.
[47]KOIKE K,YONO O,DE SÁV R,et al. Effectiveness of neural kriging for three-dimensional modeling of sparse and strongly biased distribution of geological data with application to seafloor hydrothermal mineralization[J]. Mathematical Geosciences,2022,54(7):1183-1206.
[48]ZHU Z R, CUI X D, ZHANG K, et al. DNN-based seabed classification using differently weighted MBES multifeatures[J]. Marine Geology,2021,438:106519.
[49]郭鹏.机器学习揭示玄武岩构造背景与源区性质[J].矿物岩石地球化学通报,2023,42(1):26-33.GUO P. Tectonic setting and source characteristics of basaltic rocks revealed by the machine learning[J]. Bulletin of Mineralogy,Petrology and Geochemistry,2023,42(1):26-33
[50]MØLLER T E,LE MOINE BAUER S,HANNISDAL B,et al.Mapping microbial abundance and prevalence to changing oxygen concentration in deep-sea sediments using machine learning and differential abundance[J]. Frontiers in Microbiology,2022,13:804575.
[51]GUO P,YANG T,XU W L,et al. Machine learning reveals source compositions of intraplate basaltic rocks[J]. Geochemistry,Geophysics,Geosystems,2021,22(9):e2021GC009946.
[52]VAN DOVER C L. Inactive sulfide ecosystems in the deep sea:a review[J]. Frontiers in Marine Science,2019,6:461.
[53]PETERSEN S,KRÄTSCHELL A,AUGUSTIN N,et al. News from the seabed:geological characteristics and resource potential of deep-sea mineral resources[J]. Marine Policy,2016,70:175-187.
[54]HOLZHEID A,ZHAO H B,CABUS T,et al. Deep-sea mining of massive sulfides:balancing impacts on biodiversity and ecosystem, technological challenges and law of the sea[J].Marine Policy,2024,167:106289.
[55]LUSTY P A, MURTON B J. Deep-ocean mineral deposits:metal resources and windows into earth processes[J]. Elements:an International Magazine of Mineralogy, Geochemistry, and Petrology,2018,14(5):301-306.
[56]ZHENG C Y,ZHAO Q Y,FAN G Y,et al. Comparative study on isolation forest, extended isolation forest and generalized isolation forest in detection of multivariate geochemical anomalies[J]. Global Geology,2023,26(3):167-176.
[57]ZHANG S, CARRANZA E J M, XIAO K Y, et al. Mineral prospectivity mapping based on isolation forest and random forest:implication for the existence of spatial signature of mineralization in outliers[J]. Natural Resources Research, 2022,31(4):1981-1999.
[58]BERGEN K J,JOHNSON P A,DE HOOP M V,et al. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science,2019,363(6433):eaau0323.
[59]RODRIGUEZ-GALIANO V, SANCHEZ-CASTILLO M,CHICA-OLMO M,et al. Machine learning predictive models for mineral prospectivity:an evaluation of neural networks,random forest,regression trees and support vector machines[J].Ore Geology Reviews,2015,71:804-818.
[60]赵秋魁,李传顺,闫仕娟,等.基于卷积神经网络的深海摄像资料智能识别研究[J].海洋科学进展,2023,41(2):344-356.ZHAO Q K,LI C S,YAN S J,et al. Recognizing seabed images taken from the Mid-Atlantic Ridge based on convolution neural network[J]. Advances in Marine Science,2023,41(2):344-356.
[61]VEGA P J S,PAPADAKIS P,MATABOS M,et al. Convolutional neural networks for hydrothermal vents substratum classification:an introspective study[J]. Ecological Informatics,2024,80:102535.
[62]LOWE S C,MISIUK B,XU I,et al. BenthicNet:a global compilation of seafloor images for deep learning applications[J].Scientific Data,2025,12(1):230.
[63]MIMURA K,NAKAMURA K,TAKAO K,et al. Automated detection of hydrothermal emission signatures from multibeam echo sounder images using deep learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2023,16:2703-2710.
[64]李若飞,柯志新,李开枝,等.基于ZooScan图像分析"海马"冷泉区浮游动物垂直分布特征[J].热带海洋学报,2023,42(2):87-96.LI R F, KE Z X, LI K Z, et al. Vertical distribution of zooplankton in the“Haima” cold seep region based on ZooScan image analysis[J]. Journal of Tropical Oceanography, 2023,42(2):87-96.
[65]DUMKE I,LUDVIGSEN M,ELLEFMO S L,et al. Underwater hyperspectral imaging using a stationary platform in the Trans-Atlantic Geotraverse hydrothermal field[J]. IEEE Transactions on Geoscience and Remote Sensing,2018,57(5):2947-2962.
[66]KEOHANE I,WHITE S. Chimney identification tool for automated detection of hydrothermal chimneys from high-resolution bathymetry using machine learning[J]. Geosciences,2022,12(4):176.
[67]COHEN S. The Evolution of Machine Learning:Past,Present,and Future[M]//CHAUHAN C,COHEN S. Artificial Intelligence in Pathology. Amsterdam:Elsevier,2025:3-14.
[68]ZHAO T J,WANG S,OUYANG C J,et al. Artificial intelligence for geoscience:progress,challenges and perspectives[J].The Innovation,2024,5(5):100691.
[69]MODENESI M C, SANTAMARINA J C. Hydrothermal metalliferous sediments in Red Sea deeps:formation,characterization and properties[J]. Engineering Geology,2022,305:106720.
[70]PARADA J, FENG X, HAUERHOF E, et al. The Deep Sea Energy Park:Harvesting Hydrothermal Energy for Seabed Exploration[M]//SHENOI R A,WILSON P A,BENNETT S S.The LRET Collegium 2012 Series. Southampton:University of Southampton,2012.
[71]ZHU Z R,TAO C H,ZHOU J P,et al. Seafloor classification combining shipboard low-frequency and auv high-frequency acoustic data:a case study of duanqiao hydrothermal field,southwest Indian ridge[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-15.
[72]YUE X H, LI H M, REN J Y, et al. Seafloor hydrothermal activity along mid-ocean ridge with strong melt supply:study from segment 27, Southwest Indian Ridge[J]. Scientific Reports,2019,9(1):9874.
[73]TAO C,LIN J,GUO S,et al. First active hydrothermal vents on an ultraslow-spreading center:Southwest Indian Ridge[J].Geology,2012,40(1):47-50.
[74]HAROON A, PAASCHE H, GRABER S, et al. Automated seafloor massive sulfide detection through integrated image segmentation and geophysical data analysis:revisiting the TAG hydrothermal field[J]. Geochemistry, Geophysics, Geosystems,2023,24(12):e2023GC011250.
[75]LIU C,WANG W L,TANG J X,et al. A deep-learning-based mineral prospectivity modeling framework and workflow in prediction of porphyry–epithermal mineralization in the Duolong ore District,Tibet[J]. Ore Geology Reviews,2023,157:105419.
[76]PETRELLI M, PERUGINI D. Solving petrological problems through machine learning:the study case of tectonic discrimination using geochemical and isotopic data[J]. Contributions to Mineralogy and Petrology,2016,171:1-15.
[77]UEKI K,HINO H,KUWATANI T. Geochemical discrimination and characteristics of magmatic tectonic settings:a machine-learning-based approach[J]. Geochemistry,Geophysics,Geosystems,2018,19(4):1327-1347.
[78]STRACKE A,WILLIG M,GENSKE F,et al. Chemical geodynamics insights from a machine learning approach[J]. Geochemistry, Geophysics, Geosystems, 2022, 23(10):e2022GC010606.
[79]刘露诗.基于机器学习与缺失值插补技术的海底硫化物成矿定量预测[D].长春:吉林大学,2022.LIU L S. Prospectivity mapping for seafloor massive sulfide based on machine learning and missing value imputation techniques[D]. Changchun:Jilin University,2022.
[80]D'ERCOLE C, GROVES D, KNOX-ROBINSON C. Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley-type mineralisation[J]. Australian Journal of Earth Sciences,2000,47(5):913-927.
[81]CRONAN D S. A synthesis of applied geochemistry research group and consequent research at the Imperial College of Science and Technology, London, into establishing geochemical exploration techniques for marine minerals[J]. Geochemistry:Exploration, Environment, Analysis,2010,10(3):279-287.
[82]LIU L S,LU J L,TAO C H,et al. Prospectivity mapping for magmatic-related seafloor massive sulfide on the Mid-Atlantic Ridge applying weights-of-evidence method based on GIS[J].Minerals,2021,11(1):83.
[83]KOIKE K,SAKAMOTO H,OHMI M. Detection and hydrologic modeling of aquifers in unconsolidated alluvial plains through combination of borehole data sets:a case study of the Arao area, Southwest Japan[J]. Engineering Geology, 2001,62(4):301-317.
[84]KALLMEYER J, POCKALNY R, ADHIKARI R R, et al.Global distribution of microbial abundance and biomass in subseafloor sediment[J]. Proceedings of the National Academy of Sciences,2012,109(40):16213-16216.
[85]D’HONDT S,INAGAKI F,ZARIKIAN C A,et al. Presence of oxygen and aerobic communities from sea floor to basement in deep-sea sediments[J]. Nature Geoscience,2015,8(4):299-304.
[86]JØRGENSEN B B,MARSHALL I P. Slow microbial life in the seabed[J]. Annual review of marine science, 2016, 8(1):311-332.
[87]LIU L S,LU J L,TAO C H,et al. Fuzzy forest machine learning predictive model for mineral prospectivity:a case study on Southwest Indian Ridge 48.7°E-50.5°E[J]. Natural Resources Research,2022,31(1):99-116.
[88]郑袁明,黄元耕,王洋,等. 2024年度地质学学科基金项目评审与成果分析[J].地球科学进展,2024,39(10):1032-1039.ZHENG Y M,HUANG Y G,WANG Y,et al. Introduction to the Projects Managed by the Discipline of Geology, Department of Earth Science,National Natural Science Foundation of China in 2024[J]. Frontiers in Marine Science,2024,39(10):1032-1039.
[89]SARBAS B,NOHL U. The GEOROC database:a decade of“online geochemistry”[J]. Geochimica et Cosmochimica Acta Supplement,2009,73:A1158.
[90]LEHNERT K,SU Y,LANGMUIR C,et al. A global geochemical database structure for rocks[J]. Geochemistry, Geophysics,Geosystems,2000,1(5):1012.
[91]WALKER J,LEHNERT K,HOFMANN A,et al. EarthChem:international collaboration for solid earth geochemistry in geoinformatics[C]//AGU Fall Meeting 2025 Abstracts. San Francisco:American Geophysical Union,2005,IN44A-03.
[92]CHIAMA K,GABOR M,LUPINI I,et al. The secret life of garnets:a comprehensive,standardized dataset of garnet geochemical analyses integrating localities and petrogenesis[J].Earth System Science Data Discussions,2023,2023:1-42.
[93]李维禄,高思宇,杨锦坤,等.面向多金属结核资源评价的大数据挖掘与融合[J].吉林大学学报(地球科学版),2025,55(1):340-350.LI W L,GAO S Y,YANG J K,et al. Big data mining and fusion towards resources evaluation of polymetallic nodules[J].Journal of Jilin University(Earth Science Edition),2025,55(1):340-350.
[94]余星.海底岩石地球化学研究中的“大数据”:PetDB及其应用[J].地球科学进展,2014,29(2):306-314.YU X. The big data tool for seabed petrogeochemistry researchPetDB and its application in geoscience[J]. Frontiers in Marine Science,2014,29(2):306-314.
[95]HOWELL K L,HILÁRIO A,ALLCOCK A L,et al. A blueprint for an inclusive,global deep-sea ocean decade field program[J]. Frontiers in Marine Science,2020,7:584861.
[96]VON DAMM K. Controls on the chemistry and temporal variability of seafloor hydrothermal fluids[J]. Seafloor Hydrothermal Systems:Physical, Chemical, Biological, and Geological Interactions,1995,91:222-247.
[97]FRANK M,DRIKAKIS D,CHARISSIS V. Machine-learning methods for computational science and engineering[J]. Computation,2020,8(1):15.
[98]DRAMSCH J S. 70 years of machine learning in geoscience in review[J]. Advances in Geophysics,2020,61:1-55.
[99]谢玉芝,汪洋.机器学习在岩矿地球化学研究中的应用:综述与思考[J].地质论评,2023,69(4):1465-1474.XIE Y J, WANG Y. A review on the machine learning approach to rock and mineral geochemistry research[J]. Geological Review,2023,69(4):1465-1474.
[100]DIESING M, GREEN S L, STEPHENS D, et al. Mapping seabed sediments:comparison of manual,geostatistical,objectbased image analysis and machine learning approaches[J].Continental Shelf Research,2014,84:107-119.
[101]BI X, CHEN D L, CHEN G T, et al. Deepseek llm:scaling open-source language models with longtermism[EB/OL].[2025-03-24]. https://arxiv.org/abs/2401.02954.
[102]LIU Y H,HAN T L,MA S Y,et al. Summary of ChatGPT-related research and perspective towards the future of large language models[J]. Meta-radiology,2023,1(2):100017.
[103]RAY P P. ChatGPT:a comprehensive review on background,applications, key challenges, bias, ethics, limitations and future scope[J]. Internet of Things and Cyber-Physical Systems,2023,3:121-154.
[104]COOPER G. Examining science education in ChatGPT:an exploratory study of generative artificial intelligence[J]. Journal of Science Education and Technology,2023,32(3):444-452.
[105]ZHANG Y F,WEI C,WU S Y,et al. Geogpt:understanding and processing geospatial tasks through an autonomous gpt[EB/OL].[2025-03-24]. https://arxiv.org/abs/2307.07930.
[106]SI C L,YANG D Y,HASHIMOTO T. Can llms generate novel research ideas? a large-scale human study with 100+nlp researchers[EB/OL].[2025-03-24]. https://arxiv.org/abs/2409.04109.
[107]WU J Y,GAN W S,CHEN Z F,et al. Multimodal large language models:a survey[C]//2023 IEEE International Conference on Big Data(BigData). Sorrento:IEEE,2023:2247-2256.
[108]ZHANGZHOU J,HE C,SUN J,et al. Geochemistryπ:automated machine learning Python framework for tabular data[J].Geochemistry, Geophysics, Geosystems, 2024, 25(1):e2023GC011324.
[109]KLÖCKING M,WYBORN L,LEHNERT K A,et al. Community recommendations for geochemical data, services and analytical capabilities in the 21st century[J]. Geochimica et Cosmochimica Acta,2023,351:192-205.
[110]PRAKASH M,RAMAGE S,KAVVADA A,et al. Open earth observations for sustainable urban development[J]. Remote Sensing,2020,12(10):1646.
[111]VAN KONINGSVELD M, DE BOER G, BAART F, et al.OpenEarth-inter-company management of data, models, tools&knowledge[C]//WODCON XIX:Dredging Makes the World a Better Place. Delft:CEDA,2010:1-14.
[112]KHAZ’ALI A,NICK H. A VTK-based workflow for carbon storage modeling and risk evaluation[C]//The Fourth EAGE Global Energy Transition Conference and Exhibition,Bunnik:European Association of Geoscientists&Engineers,2023:1-5.
[113]FRENKEL M. Global information systems in science:application to the field of thermodynamics[J]. Journal of Chemical&Engineering Data,2009,54(9):2411-2428.
[114]GORING S,MARSICEK J,YE S,et al. A model workflow for GeoDeepDive:locating Pliocene and Pleistocene ice-rafted debris[EB/OL].[2025-03-24]. https://doi.org/10.31223/X54312.
[115]ZHANG M M,WANG C B,ZHANG Q,et al. Temporal-spatial analysis of alkaline rocks based on GEOROC[J]. Applied Geochemistry,2021,124:104853.
[116]刘博,翟明国,彭澎,等.大数据驱动下变质岩岩石学研究展望[J].高校地质学报,2020,26(4):411-423.LIU B,ZHAI M G,PENG P,et al. Prospects on big data-driven metamorphic petrology[J]. Geological Journal of China Universities,2020,26(4):411-423.
[117]ZHANG W,CAI M X,ZHANG T,et al. Earthgpt:a universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain[J]. IEEE Transactions on Geoscience and Remote Sensing,2024,62:1-20.
[118]BI Z,ZHANG N Y,XUE Y D,et al. Oceangpt:a large language model for ocean science tasks[EB/OL].[2025-03-24].https://aclanthology.org/2024.acl-long.184/.
[119]DENG C,ZHANG T H,HE Z M,et al. K2:a foundation language model for geoscience knowledge understanding and utilization[C]//ANGELICA L,LATTANZI S,MUNOZ M A,et al. WSDM'24:Proceedings of the 17th ACM International Conference on Web Search and Data Mining. New York:Machinery. 2024:161-170.
[120]GOECKS V G, WAYTOWICH N R. Disaster responsegpt:large language models for accelerated plan of action development in disaster response scenarios[EB/OL].[2025-03-24].https://doi.org/10.48550/arXiv.2306.17271.
[121]BAUCON A,DE CARVALHO C N. Can AI get a degree in geoscience? performance analysis of a gpt-based artificial intelligence system trained for earth science(Geology Oracle)[J].Geoheritage,2024,16(4):121.
基本信息:
DOI:10.16028/j.1009-2722.2025.076
中图分类号:P744
引用信息:
[1]贺文霄,梁锦,陶春辉.基于机器学习的深海热液循环系统研究进展[J].海洋地质前沿,2026,24(04):1-15.DOI:10.16028/j.1009-2722.2025.076.
基金信息:
国家重点研发计划项目“深海硫化物资源移动式高效钻测技术与示范”“深海硫化物资源评估方法研究与总体设计”(2023YFC28-11100,2023YFC2811101)
2026-04-28
2026-04-28