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1.北京建筑大学土木与交通工程学院,北京 100044
2.北京科技大学城镇化与城市安全研究院,北京 100083
3.清华大学土木工程安全与耐久教育部重点试验室,北京 100084
Received:12 March 2024,
Revised:2024-05-31,
Published:15 February 2025
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程庆乐,任昊天,田源等.考虑时域和频域特征的图片时序数据解析方法[J].防灾减灾工程学报,2025,45(01):110-118.
CHENG Qingle,REN Haotian,TIAN Yuan,et al.Image Time‑series Data Analysis Method Considering Time and Frequency Domain Characteristics: A Case Study of Ground Motion Data[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(01):110-118.
程庆乐,任昊天,田源等.考虑时域和频域特征的图片时序数据解析方法[J].防灾减灾工程学报,2025,45(01):110-118. DOI: 10.13409/j.cnki.jdpme.20240312002.
CHENG Qingle,REN Haotian,TIAN Yuan,et al.Image Time‑series Data Analysis Method Considering Time and Frequency Domain Characteristics: A Case Study of Ground Motion Data[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(01):110-118. DOI: 10.13409/j.cnki.jdpme.20240312002.
时序数据对于理解和应对地震等自然灾害具有重要意义,但由于保密、获取途径的限制等原因,需要从时域、频域数据的图片中获取时序数据。传统方式下通过直接对图片中时序数据数字化得到的时序数据在时域和频域上均存在较大误差。针对该问题,提出了考虑时域和频域特征的图片时序数据解析方法。该方法首先从图片中通过数字化方法获取时域和频域数据,以识别得到的较为准确的频域数据曲线作为目标频域数据,采用连续小波变换修正从图片中直接识别得到的时序数据,从而提高图片时序数据的解析精度。以地震动这一典型时序数据为例对所提出的方法进行了展示与说明,并与传统直接从图片识别的数字化地震动方法和基于反应谱匹配的频域方法进行了对比。进一步将该方法应用于2022年台湾省台东县6.5级地震的破坏力评估中。主要结论有:提出的图片时序数据解析方法能够兼顾图片中时域和频域的信息,从而能够解析得到更为准确的时域数据,用于后续工程防灾分析时的误差也更小,为从时序数据图片中识别数据提供了重要方法。
Time-series data is crucial for understanding and responding to natural disasters such as earthquakes. However
due to confidentiality
access restrictions
and other reasons
time-series data are often extracted from images containing time and frequency domain data. Traditional methods of directly digitizing time-series data from these images tend to have significant errors in both the time and the frequency domains. To address this issue
an image time-series data analysis method with the inclusion of time and frequency domain characteristics was proposed. First
the time and frequency domain data were extracted from the images using digital method
with the more accurate frequency domain data curve identified as the target frequency domain data. Continuous wavelet transform was then used to correct the time-series data directly extracted from the images
so as to improve the accuracy of the time-series data analysis. The proposed method was demonstrated and explained using the typical time-series data of ground motion as an example. A comparison was made with the traditional method of digitizing earthquake motion directly from images and frequency domain method based on response spectrum matching. Furthermore
the method was applied to the damage assessment of the 6.5-magnitude earthquake in Taitung County
Taiwan Province in 2022. The primary conclusions are as follows: the proposed image time-series data analysis method can take into account both the time and frequency domain information
allowing for more accurate extraction of time domain data. This results in smaller errors when used in subsequent engineering disaster prevention analysis
providing an important method for data identification from time-series images.
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