目的 自然界中的大部分现象本质上都是在空间上随时间的流逝不断发展变化的物理或化学过程，可以表述为含有时间变量的数据场，这些数据场称为时变体数据。随着科学计算技术、计算机仿真技术以及现代观测技术的发展，能够以前所未有的精度对自然现象进行仿真或者观测，但同时也面临时变体数据体积大、时间长以及变量数目多的难题。为了更有效地显示时变体数据并挖掘数据中的关键信息，针对时变体数据的可视化，本文提出一种基于数据特征的方法，用于探索时变体数据中感兴趣区域(即特征)的特点与变化。方法 通过将特征提取、特征跟踪、运动检测和提出的3种特征可视化方法(数据帧特征可视化、单个运动过程特征可视化和空间多运动过程特征可视化)置于同一个框架之中，提供一种从时间域和空间域探索多变量时变体数据的一站式解决方案，并突出时变体数据的动力学特性。结果 本文方法在4组不同的时变体数据上应用，对数据中特征各变量的变化以及感兴趣的运动进行了特征可视化。结论 实验结果显示本文方法能以较小的时间成本有效显示数据中的特征以及用户定义的运动，方法的有效性与实用性得到了验证。

Objective Scientific phenomena, such as combustion, ocean currents, and hurricanes are inherently time-varying processes that can be represented as data fields with time variables. Data fields with time variables are often referred to as time-varying volume data. Studying the dynamic aspects of scientific phenomena that change over time is critical to the solution of many scientific problems. With the rapid advancement in computing technologies, time-varying volume data have been created to simulate many physical and chemical processes in their spatial and temporal domains with unprecedented accuracy and complexity. Time-varying volumes usually have large sizes (millions or even billions of voxels), long duration (hundreds or even thousands of timesteps), and contain multiple variables. Presenting time-varying volume data providing a powerful impetus for the research on the visualization of time-varying volume data. It is important to first present the data information efficiently then allow scientists to have direct interaction with the data and glean insights into the simulated scientific phenomena. The ability of scientists to visualize time-varying phenomena is essential to ensuring the correct interpretation and analysis, fostering insights, and communicating those insights to others. Rendering time-varying volume data to achieve interactive visualization has long been of interest to the visualization community. Methods for visualizing time-varying volume data can be classified broadly into two types: time-independent and time-dependent. Time-independent algorithms process each timestep or multiple timesteps of time-varying data independently and display a sequence of timesteps as an animation. Methods generally include encoding data to make it more manageable (e.g., down-sampling in the time domain, data compression, contour extraction), preselecting transfer functions for direct volume rendering, and interactive hardware-accelerated volume rendering. Time-independent algorithms, which do not rely on domain and expert knowledge, have the advantages of easy operation and good flexibility but fail to consider the dynamic and time-varying characteristics of data. Moreover, the methods cannot highlight the information in data important to scientific discovery. Different from time-independent methods, time-dependent methods, which are usually referred to as “feature-based visualization” or “feature visualization”, focus on the features of data and track the variation tendency of data by using the consistency of feature movements and interactions between adjacent timesteps. In this context, a feature can be defined from two aspects: 1) regions of interest that can be extracted from original data, such as shape, structure, variation, and phenomena and 2) some subsets of interest in the original data. Using techniques from image processing and mathematical morphology, feature visualization algorithms extract amorphous regions from scalar or vector fields of data and create correspondences between consecutive timesteps with certain matching criteria. A major advantage of feature visualization over other methods is that it exploits the data coherence between consecutive timesteps and focus on just those regions of interest so that users can ignore redundant, unimportant, or noninterest regions. The resulting significant reduction in the storage requirement of data and rendering cost of visualization tasks makes feature visualization most suitable for investigating a temporal-spatial variation and motion process. Feature visualization of time-varying volume data generally includes four major steps: 1) defining features of data according to domain knowledge or research need; 2) extracting and quantifying features from data; 3) tracking the extracted features step by step; and 4) presenting features by isosurface rendering or direct volume rendering. Method In this paper, a method based on feature visualization is proposed to help scientists explore the characteristics and variations of regions of interest in time-varying volume data. The proposed method includes a feature-based data processing part which combines feature extraction, feature tracking over time, and event query and isolation in one workflow and three interactive visualizations: feature visualization of a data frame, feature visualization of an individual event, and feature visualization of multiple events in th瑥栠敳?灡牴潩灡潬猠散摯?浴敥瑸桴漮搠???扴??漠湦捥污畴獵楲潥渭??扳???敤湡整牡愠汰汲祯??瑳桳敩?灧爠潰灡潲獴攬搠?洠敲瑥桧潩摯?漭晧?瑯桷楩獮?瀠慡灬敧牯?桩慴獨?映楷癩整?洠慡樠潴牨?扥敳湨敯晬楤琠獰?????灥牤漠癢楹搠極湳来?慳?潩湳攠?獰瑰潬灩?獤漠汴畯琠楴潨湥?瑳潣?敬硡灲氠潦物敥?瑤栠敯?猠灴慨瑥椠慰汲??瑡敲浹瀠潶牡慲汩??慬湥搠?灥慴爠慢浹攠瑵敳牥?獳瀠愨捯敲猠?潨晥?瑯楮浬敹?癶慡牲祩楡湢杬?瘩漠汩畮洠敥?摥慲瑹愠?????桦楲条桭汥椮朠桃瑯楮湮来?瑴桥敤?摣祯湭慰浯楮捥?慴獳瀠敯捦琠獰?潩普?瑳椠海敩?癨愠牤祡楴湡朠?癡潬汵略浳攠?摢慯瑶慥??睨桥椠捴桨?桥敳汨灯獬?猠捡楲敥渠瑥楸獴瑲獡?畴湥摤攠牦獲瑯慭渠摥?督桨攠湤?慴湡搠?睲桡敭牥攠?楳渠瑦敥牡整獵瑲楥湳朮?敇癥敯湭瑥獴?潩捣挠異牲?楰湥?慴?摥慳琬愠獳敵瑣?????瑶桯敬?晭敥愬琠畭牡敳?洬攠瑡慮摤愠瑣慥?杴敲湯敩牤愠瑡敲摥?扣祡?瑣桵敬?晴敥慤琠畦牯敲?扥慡獣敨搠?摸慴瑲慡?灴牥潤挠敦獥獡楴湵杲?愠汤杵潲物楮瑧栠浴?敥渠獰畲牯散獥?敳映景楦挠楥數湴瑲?汣潴慩摯楮渮朠?慳湩摮?瀠牴潨捥攠獰獯楩湮杴?慭?汴慡牤条整?愠浧潥畮湥瑲?潴晥?搠慦瑲慯?????瑴桵敲?瘠楥數睴敲牡?摴潩敯獮?渠潴瑨?爠敥煸畴楲牡散?慥?挠汦楥敡湴瑵?獥楳搠敩?椠湥獡瑣慨氠汴慩瑭楥潳湴?慰渠摡?潥映晣敯牲獲?捬敡湴瑥牤愠汯楶穥敲搠?浩慭楥渠瑷敩湴慨渠捡攠??浡慴歵楲湥朠?楲瑡?浫潩牮敧?慡捬捧敯獲獩楴扨汭攠?瑡潳?浤漠牯敮?當獯敬牵獭??慯湶摥????琠桴敯?癤楥獴略慲汭楩穮慥琠楯潣湣?牲敲獥畮汣瑥獳?慯湦搠?瑨桥攠?癡業敥眠敦牥?楴瑵獲敥氠晩?挠慣湯?扳敥?摵楴獩瑶牥椠扴畩瑭敥搠?整慥獰楳氮礠?愠浦潥湡杴?浲略氠瑭楡灹氠敧?甠獴敨牲獯??瑨栠敦物敶扥礠?灴牡潴浥潳琠楩湮朠?捴潳氠汥慶扯潬牵慴瑩楯癮攺?牢敩獲整慨爬挠档?ntinuation, merge, split, and death. With numerous features spanning dozens or even hundreds of timesteps, it is necessary to isolate all occurrences of the same features from the tracking history to help understand the dynamics in the data. In this context, the temporal and spatial evolution of a feature is referred to as an event. A state graph-based event query algorithm is utilized to capture events defined by scientists. After the event query, a list is created to record all isolated events as a sequence of the triplet: timestep, index of the feature in this timestep, and the state in the evolution process. In the visualization part of the method, a web-based viewer is developed to provide a user interface to explore the feature metadata generated from the feature-based data processing program with three interactive visualizations. Result The proposed method is applied to four time-varying volume datasets: turbulent vortex, hurricane Isabel, ocean simulation, and hydrothermal plume. The visualization results demonstrate the events of interest from each dataset and further allow users to explore the data from different perspectives from an instance to the entire dataset, which confirmed the usability and effectiveness of