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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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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

Evaluation of GOFP over four forest plots using RAMI and UAV measurements

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International Journal of Digital Earth 2021年第10期14卷 1433-1451页

作者： jun Geng Qian Zhang Feng Qiu J.M.Chen Yongguang Zhang Weiliang Fan lili Tu Jianwei huang Shaoteng Wang lichen Xu Jinchao li School of Civil Engineering Hefei University of TechnologyHefeiPeople’s Republic of China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of TechnologyHefeiPeople’s Republic of China Jiangsu Provincial Key Laboratory of Geographic information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjingPeople’s Republic of China Max Planck Institute for Biogeochemistry JenaGermany Key Laboratory of information Perception and Systems for Public Security of MIIT Nanjing University of Science and TechnologyNanjingChina Department of Geography and Program in Planning University of TorontoTorontoCanada School of Geographical Science Fujian Normal UniversityFuzhouPeople’s Republic of China college of Environmental and Resource Science Zhejiang A&F UniversityLinanPeople’s Republic of China School of Resources and Environment Anhui Agricultural UniversityHefeiPeople’s Republic of China

Comparison and validation of canopy reflectance(CR)models are two important steps to ensure their *** forest plantations are an ideal type of forest for validating CR models because of their simple background and the low variance in the crown structures which are usually assumed to be identical in most CR models.A Geometric Optical Model for Forest Plantations(GOFP)was compared using dataset in two radiation transfer model intercomparison exercise(RAMI)stands and validated using in situ dataset of detailed optical and structural data of two forest plantations in the Saihanba Forestry Center,*** results show that(1)the tree distributions in stands described by the hypergeometric model in GOFP show good consistencies with the dataset in the two RAMI stands and measurements from the two Saihanba forest stands;and(2.the CRs simulated with GOFP are also compared well in the two RAMI stands and validated with measurements collected with unmanned aerial vehicles in the two Saihanba *** shows a better consistency with the CR measurements than those from CR models for natual forestsbecause the tree distribution in forest plantations is described more reasonably in GOFP.

关键词： Validation forest plantation GOFP tree distribution BRF hypergeometric model

Mapping wetland changes in China between 1978 and 2.08

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Chinese Science Bulletin 2012年第22期57卷 2813-2823页

作者： NIU ZhenGuo ZHANG HaiYing WANG XianWei YAO WenBo ZHOU DeMin ZHAO KuiYi ZHAO Hui li NaNa huang HuaBing li CongCong YANG jun liU CaiXia liU Shuang WANG lin li Zhan YANG ZhenZhong QIAO Fei ZHENG YaoMin CHEN YanLei SHENG YongWei GAO XiaoHong ZHU WeiHong WANG WenQing WANG Hong WENG Yongling Zhuang DaFang liU JiYuan LUO ZhiCai CHENG Xiao GUO ZiQi GONG Peng State Key Laboratory of Remote Sensing Science Jointly Sponsored by Institute of Remote Sensing ApplicationsChinese Academy of Sciences and Beijing Normal UniversityBeijing 100101China Ministry of Education Key Laboratory for Earth System Modeling Center for Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Department of Resource Environment and Tourism Capital Normal UniversityBeijing 100037China Northeast Institute of Geography and Agricultural Ecology Chinese Academy of SciencesChangchun 130012China Department of Geography and Remote Sensing Beijing Normal UniversityBeijing 100875China college of Forestry Beijing Forestry UniversityBeijing 100083China Department of Environmental Science Policy and ManagementUniversity of CaliforniaBerkeley94720USA Department of Geography University of CaliforniaLos Angeles90095USA Department of life and Geographic Sciences Qinghai Normal UniversityXining 810008China Department of Geography Yanbian UniversityYanbian 133002China School of Environment and Ecology Xiamen UniversityXiamen 361005China School of Geographical information Science Hohai UniversityNanjing 211100China School of Surveying and Mapping Transportation Engineering Southeast UniversityNanjing 210096China Institute of Geographical Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China School of Surveying and Mapping Wuhan UniversityWuhan 430079China

Four wetland maps for all China have been produced,based on Landsat and CBERS-02. remote sensing data between 1978 and 2.08 (1978,1990,2.00 and 2.08).These maps were mainly developed by manual interpretation and validated by substantial field investigation in *** on these maps,we analyzed the 2.08 wetland distribution in China and discussed wetland changes and their drivers over the past 30 years.(i) There were about 32.097 km 2.of wetlands in 2.08,for which inland marshes or swamps were the most common wetland type (35%),with lakes (2.%) *** of the wetlands were in Heilongjiang,Inner Mongolia,Qinghai and Tibet,occupying about 55% of the national wetland area.(ii) From 1978 to 2.08,China's wetland area continually and significantly decreased,by about 33% based on changes in the wetland *** was in sharp contrast to the increase in artificial wetlands,which increased by about 12.%.Inland marshes accounted for the main loss of total wetlands from 1978 to *** 2.00 through 2.08,riverine and lacustrine wetlands constituted the main wetland *** however,the rate of wetland loss decreased from 552. to 831 km 2./a.(iii) The change ratio of lost natural wetlands (including inland and coastal wetlands) to non-wetlands has decreased slightly over the past 30 *** 1978 to 1990,nearly all natural wetlands (98%) lost were transformed into ***,the ratio declined to 86% from 1990 to 2.00,and to 77% from 2.00 to 2.08.(iv) All Chinese provinces were divided into three groups according to patterns of wetland changes,which could relate to the driving forces of such *** was completely different from other provinces,as it was one representative example in which there was a net wetland increase,because of global warming and decreased human activity since *** economic development caused considerable wetland loss in most eastern provinces,and artificial wetlands increased.

关键词：湿地变化中国地图映射地球资源卫星内陆湿地人工湿地全球气候变暖湿地面积

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太湖流域养殖池塘的氮磷污染负荷估算

长江流域资源与环境 2022年第1期31卷 148-155页

作者：徐责茗崔桢高俊峰李国柱黄佳聪吉林师范大学旅游与地理科学学院吉林四平136000 中国科学院南京地理与湖泊研究所江苏南京210008 中国科学院流域地理学重点实验室江苏南京210008

太湖流域平原区河网密集、渔业发达,其中池塘养殖是渔业的主要模式,而养殖池塘的污水排放是其周边河湖富营养化与蓝藻水华的重要原因之一,因此,精细化估算流域养殖池塘的氮磷污染负荷,对区域氮磷污染来源的精准解析与科学防控具有重要... 详细信息

太湖流域平原区河网密集、渔业发达,其中池塘养殖是渔业的主要模式,而养殖池塘的污水排放是其周边河湖富营养化与蓝藻水华的重要原因之一,因此,精细化估算流域养殖池塘的氮磷污染负荷,对区域氮磷污染来源的精准解析与科学防控具有重要意义。以太湖流域养殖池塘为研究对象,通过解译2.19年的高分辨率遥感影像,获取养殖池塘的空间分布,同时发展了基于归一化植被指数区分养殖池塘类型的方法,估算了两种类型养殖池塘(鱼塘和虾蟹塘)的面积与分布;采用氮磷污染排放系数法,估算了养殖池塘的氮磷污染负荷,分析其空间差异。研究结果表明:2.19年,太湖流域养殖池塘总面积为1308.6 km^(2.,其中鱼塘872.6 km^(2.,虾蟹塘436.0 km^(2.,养殖池塘氮排放量为11013.5 t,磷排放量为162..8 t。养殖池塘污染负荷空间分布和养殖池塘规模分布比较一致,太湖南部的湖州市辖区、吴江市和德清县是养殖池塘氮磷污染较为严重地区,其氮污染负荷分别为12.4.8、1004.4和792.2.t,磷污染负荷分别为179.7、146.3和117.7 t,主要原因是当地池塘养殖业发达,并且氮磷污染负荷系数大于虾蟹塘的鱼塘比例较高。

关键词：池塘营养盐归一化植被指数虾蟹

土壤强还原处理对三七连作障碍因子及再植三七生长的影响

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土壤学报 2019年第3期56卷 703-715页

作者：李云龙王宝英常亚锋续勇波黄新琦张金波蔡祖聪赵军南京师范大学地理科学学院南京210023 云南农业大学烟草学院昆明650201 江苏省地理信息资源开发与利用协同创新中心南京210023 南京师范大学江苏省物质循环与污染控制重点实验室南京210023

为研究土壤强还原处理(Reductive soil disinfestation,RSD)对连作三七土壤中障碍因子及再植三七生长的影响,以云南省文山州连作三七土壤为研究对象,设置4个处理:对照(CK);分别添加高碳氮比有机物料(C/N 94,15 t·hm^-2.SB),低碳氮比有... 详细信息

为研究土壤强还原处理(Reductive soil disinfestation,RSD)对连作三七土壤中障碍因子及再植三七生长的影响,以云南省文山州连作三七土壤为研究对象,设置4个处理:对照(CK);分别添加高碳氮比有机物料(C/N 94,15 t·hm^-2.SB),低碳氮比有机物料(C/N 19,15 t·hm^-2.BD),高、低碳氮比有机物料等质量混合(15 t·hm^-2.SB+BD)的RSD处理。利用高效液相色谱测定土壤中皂苷类物质的含量,通过定量PCR和变性梯度凝胶电泳测定土壤微生物数量和群落结构,并统计再植三七的存苗率和发病率。结果表明,与CK相比,RSD能够显著消减土壤中皂苷类物质的含量,其中BD和SB+BD对Rb1和Rh1的降解率分别高达82.1%和85.8%~88.1%。RSD还能有效杀灭尖孢镰刀菌,显著降低其在真菌类群中的比例,其中杀菌率高达99.7%。同时,移栽5个月后,RSD处理再植三七的存苗率提高7.3倍~8.5倍,发病率由89.0%降低至12.9%~16.1%。因此,土壤强还原处理能够显著消减连作三七土壤中积累的障碍因子,改善土壤微生物群落结构,提高存苗率,降低发病率,是一种具有快速缓解三七连作障碍潜力的农业措施。

关键词：土壤强还原处理三七连作障碍化感物质尖孢镰刀菌土壤微生物群落

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基于复合算法的航空发动机磨损故障诊断

航空计算技术 2019年第4期49卷 114-118,122页

作者：黄帆李艳军曹愈远李依林南京航空航天大学民航学院

针对航空发动机油液数据种类多样,所处状态阶段存在矛盾性,且传统三线值法制定状态界限值存在缺陷等问题,结合支持向量机理论(SVM),相对劣化度评估和人工免疫算法(AIS),提出了一种航空发动机磨损故障诊断的方法。利用SVM拟合数据的概率... 详细信息

针对航空发动机油液数据种类多样,所处状态阶段存在矛盾性,且传统三线值法制定状态界限值存在缺陷等问题,结合支持向量机理论(SVM),相对劣化度评估和人工免疫算法(AIS),提出了一种航空发动机磨损故障诊断的方法。利用SVM拟合数据的概率密度函数,根据磨粒数据的概率分布制定正常、预警和警告的界限值;根据相对劣化度评估,分析各状态参数偏离正常状态的程度;利用人工免疫算法对待测数据进行故障模式识别。通过实例验证,方法对油液监测数据反映的航空发动机磨损故障具有出色的识别能力,相较于直接归一化原始数据进行故障诊断,有利于故障类别的分离,并且可以有效降低诊断时间,提高识别效率。

关键词：航空发动机故障诊断支持向量机相对劣化度免疫系统油液分析

Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2.18

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Science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao li Han liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing huang Xiaochun huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong li Xiaoting li Xun li Yuechen li Zhilin li Zhongde li Chong liu Chunxia liu Maochou liu Shuguang liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China college of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China college of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2..However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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四川白河国家级自然保护区鸟兽红外相机监测

生物多样性 2021年第4期29卷 554-559页

作者：黄凯万雅琼李佳琦朱宇静孙治宇夏万才黎大勇任宝平西华师范大学生命科学学院四川南充637009 生态环境部南京环境科学研究所南京210042 四川省林业科学研究院成都610081 中国科学院动物研究所动物生态与保护生物学重点实验室北京100101

为了调查四川白河国家级自然保护区及其周边区域兽类及鸟类多样性,2.18年11月至2.19年9月,我们利用GIS将调查区域划分成1km×1 km的网格,并布设了110台红外相机,每3–6个月回收一次数据。累计获得19,748个相机工作日,采集可辨别鸟兽照... 详细信息

为了调查四川白河国家级自然保护区及其周边区域兽类及鸟类多样性,2.18年11月至2.19年9月,我们利用GIS将调查区域划分成1km×1 km的网格,并布设了110台红外相机,每3–6个月回收一次数据。累计获得19,748个相机工作日,采集可辨别鸟兽照片和视频55,858份,独立有效探测6,856次,共记录到野生兽类4目12.17种,鸟类5目12.33种。其中,国家I级重点保护野生动物5种,即川金丝猴(Rhinopithecus roxellana)、林麝(Moschusberezovskii)、四川羚牛(Budorcas tibetanus)、红喉雉鹑(Tetraophasisobscurus)和绿尾虹雉(Lophophoruslhuysii);国家II级重点保护野生动物有黑熊(Ursusthibetanus)、黄喉貂(Martesflavigula)和中华斑羚(Naemorhedusgriseus)等15种,合计占野生物种总数的40.0%;被IUCN红色名录评估为濒危(EN)的2.,易危(VU)的5种,近危(NT)的2.,合计占野生物种总数的18.0%;被《中国脊椎动物红色名录》评估为极危(CR)的1种,濒危(EN)的1种,易危(VU)的8种,近危(NT)的9种,合计占野生物种总数的38.0%。相对多度指数较高的前5种兽类为毛冠鹿(Elaphodus cephalophus)、猪獾(Arctonyx collaris)、岩松鼠(Sciurotamias davidianus)、野猪(Sus scrofa)和川金丝猴,相对多度指数较高的前5种鸟类为红腹角雉(Tragopan temminckii)、红嘴蓝鹊(Urocissa erythrorhyncha)、紫啸鸫(Myophonus caeruleus)、勺鸡(Pucrasiamacrolopha)和橙翅噪鹛(Trochalopteronelliotii)。本调查为白河国家级自然保护区鸟兽多样性研究提供了基础数据,为后续保护区的保护和管理工作提供了支持。

关键词：红外相机技术鸟兽多样性生物编目相对多度指数白河国家级自然保护区

Advances in nuclear detection and readout techniques

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Nuclear Science and Techniques 2023年第12期34卷 281-358页

作者： Rui He Xiao‑Yang Niu Yi Wang Hong‑Wei liang Hong‑Bang liu Ye Tian Hong‑lin Zhang Chao‑Jie Zou Zhi‑Yi liu Yun‑Long Zhang Hai‑Bo Yang Ju huang Hong‑Kai Wang Wei‑Jia Han Bei Cao Gang Chen Cong Dai li‑Min Duan Rui‑Rui Fan Fang‑Fa Fu Jian‑Hua Guo Dong Han Wei Jiang Xian‑Qin li Xin li Zhuo‑Dai li Yu‑Tie liang Shun liao De‑Xu lin Cheng‑Ming liu Guo‑Rui liu jun‑Tao liu Ze Long Meng‑Chen Niu Hao Qiu Hu Ran Xiang‑Ming Sun Bo‑Tan Wang Jia Wang Jin‑Xiang Wang Qi‑lin Wang Yong‑Sheng Wang Xiao‑Chuan Xia Hao‑Qing Xie He‑Run Yang Hong Yin Hong Yuan Chun‑Hui Zhang Rui‑Guang Zhao Ran Zheng Cheng‑Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China Key Laboratory of Particle and Radiation Imaging Department of Engineering PhysicsTsinghua UniversityBeijing 100084China School of Integrated Circuits Dalian University of TechnologyDalian 116020China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Electronic Engineering School Heilongjiang UniversityHarbin 150080China Spallation Neutron Source Science Center Dongguan 523803China Micro‑electronic Department Harbin Institute of TechnologyHarbin 150001China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210034China liaoning Academy of Materials Shenyang 110167China Istituto Nazionale di Fisica Nucleare Sezione di PerugiaPerugiaItaly School of mathematics and Physics Chengdu University of TechnologyChengdu 620035China PLAC Key Laboratory of Quark and Lepton Physics(MOE)Central China Normal UniversityWuhan 430079China School of Electronics and information Northwestern Polytechnical UniversityXi’an 710129China State Key Lab of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China

“A Craftsman Must Sharpen His Tools to Do His Job,”said *** detection and readout techniques are the foundation of particle physics,nuclear physics,and particle astrophysics to reveal the nature of the ***,they are being increasingly used in other disciplines like nuclear power generation,life sciences,environmental sciences,medical sciences,*** article reviews the short history,recent development,and trend of nuclear detection and readout techniques,covering Semiconductor Detector,Gaseous Detector,Scintillation Detector,Cherenkov Detector,Transition Radiation Detector,and Readout *** explaining the principle and using examples,we hope to help the interested reader underst and this research field and bring exciting information to the community.

关键词： Silicon detector Wide bandgap detector Time projection chamber(TPC) Multigap resistive plate chamber detector(MRPC) Scintillation detector Calorimeters Cherenkov detector Transition radiation detector Artificial intelligence Trigger-less readout