融合多模态数据的地震灾害知识图谱构建及应用

吴麒瑞; 田苗; 谢忠; 邱芹军; 陈占龙; 陶留锋

doi:10.19509/j.cnki.dzkq.tb20240334

融合多模态数据的地震灾害知识图谱构建及应用

doi: 10.19509/j.cnki.dzkq.tb20240334

吴麒瑞^a,,
田苗^b,
谢忠^{a, b, c, d},
邱芹军^{b, c, d, ,},
陈占龙^{b, c, d},
陶留锋^{b, c, d}

a .
中国地质大学（武汉）：未来技术学院
b .
中国地质大学（武汉）：地质探测与评估教育部重点实验室
c .
中国地质大学（武汉）：计算机与信息学院
d .
中国地质大学（武汉）：资源定量评价与信息工程自然资源部重点实验室，武汉 430074

基金项目: 国家自然科学基金项目（42301492）；国家重点研发计划项目（2022YFB3904200；2022YFF0801201；2022YFF0801200）；新疆自治区重点专项（2022A03009-3）；地质探测与评估教育部重点实验室主任基金项目（GLAB2023ZR01；GLAB2024ZR08）

详细信息

作者简介:
吴麒瑞：E-mail：qrwu@cug.edu.cn

通讯作者:
E-mail：qiuqinjun@cug.edu.cn

中图分类号: TP391.1；P315.9
计量
- 文章访问数: 458
- PDF下载量: 66
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-17
- 录用日期: 2025-01-02
- 修回日期: 2024-08-10
- 网络出版日期: 2025-03-07

Construction and application of earthquake disaster knowledge graph fusing with multimodal data

WU Qirui^a
,,
TIAN Miao^b,
XIE Zhong^{a, b, c, d},
QIU Qinjun^{b, c, d
, ,},
CHEN Zhanlong^{b, c, d},
TAO Liufeng^{b, c, d}

a .
School of Future Technology, China University of Geosciences, Wuhan 430074, China
b .
Key Laboratory of Geological Survey and Evaluation, Ministry of Education, China University of Geosciences, Wuhan 430074, China
c .
School of Computer and Science, China University of Geosciences, Wuhan 430074, China
d .
Key Laboratory of Resource Quantitative Evaluation and Information Engineering, Ministry of Natural Resources, China University of Geosciences (Wuhan), Wuhan 430074, China

More Information

Author Bio:
E-mail：qrwu@cug.edu.cn

Corresponding author: E-mail：qiuqinjun@cug.edu.cn

摘要

摘要:
地震灾害观测数据多源异构、蕴含知识分散且关联程度低，导致难以高效利用数据进行信息整合和查询，进而提供风险评估、救援决策辅助支持。知识图谱是一种有效的数据关联和融合的手段。首先，基于自顶向下方法梳理地震灾害领域概念，构建地震灾害数据、地质/地理环境、地震灾害事件、地震灾害应急任务、地震灾害模型本体，形成地震灾害本体层；结合自底向上方法构建高质量数据层，通过卷积神经网络对遥感影像进行灾害前后变化识别，实现从影像信息到文本知识的智能结构化转换；融合微调后通用信息抽取框架（universal information extraction，简称UIE）预训练模型对文本数据进行命名实体及关系属性知识抽取，精确率分别为82.04%和70.66%。通过计算词向量语义相似度实现数据融合与统一表达。以2023年12月18日甘肃省临夏州积石山县地震为例，通过本体构建、数据抽取、统一表达形成高质量地震灾害知识图谱，实现地震灾害多源异构地震数据到统一知识表达的转化。基于所构建的地震灾害知识图谱实现了灾害损失、应急链决策支持的查询展示，及结合相关地质数据推理和查询潜在次生灾害。该方法结合深度学习与预训练技术，融合多模态数据，构建了地震灾害知识图谱构建，为快速准确的地震灾害信息查询与次生灾害发生提供辅助支撑。
- 积石山地震 /
- 地震灾害知识图谱 /
- 信息查询 /
- 预训练模型 /
- 多模态数据
Abstract: Objective
Earthquake disaster observation data is multi-source and heterogeneous, with scattered and poorly correlated knowledge, making it difficult to efficiently utilize the data for information integration and efficient querying, and thus providing support for risk assessment and rescue decision-making.
Methods
Knowledge graphs are an effective means of data association and fusion. Firstly, based on a top-down approach, the concepts in the earthquake disaster domain are sorted out, and the ontologies of earthquake disaster data, geological/geographical environment, earthquake disaster events, earthquake disaster emergency tasks, and earthquake disaster models are constructed to form the earthquake disaster ontology layer. Combined with a bottom-up approach, a high-quality data layer is constructed. Through convolutional neural networks, changes before and after disasters in remote sensing images are identified, achieving intelligent structured conversion from image information to text knowledge. The fine-tuned UIE (universal information extraction) pre-training model is used to extract named entities and relationship attribute knowledge from text data, with precision rates of 82.04% and 70.66% respectively. Data fusion and unified expression are achieved by calculating the semantic similarity of word vectors.
Results
Taking the earthquake in Jishishan County, Linxia Prefecture, Gansu Province on December 18, 2023 as an example, a high-quality earthquake disaster knowledge graph is formed through ontology construction, data extraction, and unified expression, achieving the transformation from multi-source heterogeneous earthquake data to unified knowledge expression.
Conclusion
Based on the constructed earthquake disaster knowledge graph, queries and displays of disaster losses and emergency chain decision support are realized, and potential secondary disasters are inferred and queried in combination with relevant geological data. This method combines deep learning and pre-training techniques, integrates multi-modal data, and constructs an earthquake disaster knowledge graph, providing auxiliary support for rapid and accurate earthquake disaster information queries and the occurrence of secondary disasters.
- Jieshishan earthquake /
- earthquake disaster knowledge graph /
- information querying /
- pre-trained model /
- multimodel date

HTML全文

图 1 地震灾害知识图谱构建流程图

LLM. large language model，大语言模型；e. 字符向量；word1，word2，word3和word4. 字符编码

Figure 1. Construction process of the earthquake disaster knowledge graph

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图 2 地震灾害知识图谱本体层划分

Figure 2. Division of ontology layers of the earthquake disaster knowledge graph

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图 3 地震灾害实体间逻辑关系

Figure 3. Logical relationships between entities of earthquake disaster

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图 4 遥感影像分割及变化区域提取（Softmax. 归一化指数函数；1～8. 分别为8个光谱编号）

Figure 4. Remote sensing image segmentation and change area extraction

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图 5 UIE模型结构图

Figure 5. UIE model structure

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图 6 甘肃省临夏州积石山县地震灾害知识图谱（部分）

Figure 6. Earthquake disaster knowledge graph of Jishishan County, Linxia Prefecture, Gansu Province (part)

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图 7 地震灾害受灾情况知识查询展示

Figure 7. Query and display of knowledge on impact information of earthquake disaster

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图 8 地震灾害应急决策链事件知识查询展示

Figure 8. Query and display of knowledge on earthquake disaster emergency decision-making chain events

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图 9 地震灾害地质构造及地貌知识查询展示

Figure 9. Query and display of knowledge on geological structure and landform of earthquake disaster

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表 1 地震灾害对象实体类型分析

Table 1. Analysis of entity types of earthquake disaster objects

地震灾害本体	地震实体类型	二级分类实体	属性
地震灾害事件	灾害对象	震中位置、有感范围、影响范围、余震、断裂方向、震源深度	方位、面积、震级、深度
	灾情对象	经济损失、死亡人口、失踪人口、受伤人口、受灾人口、受损建筑物、安置人口、灾区等级、灾害范围	损失金额、伤亡数目、受损程度、受损面积等
	次生灾害对象	泥石流、崩塌、滑坡、水灾、火灾、爆炸、瘟疫、地裂、毒气泄漏	灾害级别、时间、地点、滑坡长度、滑坡体积、毒气类型等
地质/地理环境	地质/地理环境	地形地貌环境、地质构造环境、岩土体工程地质环境	山脉、平原、河流、湖泊、海洋、峡谷、沙漠、地壳、地震带、断层、构造山脉、盆地、岩浆岩、地质构造图、岩石、土壤、岩土体、岩土层、地下水位
地震灾害数据	基础灾害数据	村镇基础信息、天气、土地覆被、历史地震等	时间、天气、最高气温、最低气温、风向、风速、特殊天气、震级、发震时刻、纬度、经度、震源深度、距震中距离、周边县城名称、人口、距震中方位、距震中距离、总面积、林地、草地、水体、不透水面、裸地、耕地比例等
地震灾害数据	基础地理对象	地形、天气、房屋、道路、河流、基础设施、地名、居民地、机场、火车站	方位、距离、风速、经度、纬度、气温、时间、名称等
地震灾害应急任务	地震灾害应急对象	大中型水库、群体性事件、医疗点、物资发放点、应急避难场所、现场指挥部、撤退路线、救援路线	响应等级、安置人数、物资数目、撤退人数等
地震灾害应急任务	地震预防策略	国家的法律法规、预防措施、震后处理措施、抗震避灾手段	措施内容、使用场景、注意事项、措施类型
地震灾害模型方法	地震灾害模型	地理信息系统、遥感、统计分析、模型模拟	方法名称、类别、功能、详细描述、应用效果和验证地区
地震灾害模型方法	地震理论知识	地震基础概念、地震成因、断层机制	定义、原理、地震波、烈度、震级、饱和现象

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表 2 地震灾害实体间关系分析

Table 2. Analysis of relationships of earthquake disaster entities

关系类型	关系定义	子类型		三元组
并列关系	如果在同一属概念之中存在同层次的种概念，则这些种概念之间是并列关系	伴随		（山脉，伴随，断裂）
并列关系	如果在同一属概念之中存在同层次的种概念，则这些种概念之间是并列关系	并列		（居民地防震，并列，医院防震）
属性关系	对象的性质与对象之间关系的统称	致灾因子		（地震，致灾因子，逆冲型地震）
		对应任务		（地震仪，对应任务，监测地震）
		发生地点		（甘肃地震，发生地点，甘肃省临夏州积石山县）
因果关系	在地震灾害中一个事件和第二个事件之间的作用关系	引发		（断裂，引发，地震）（地震，引发，泥石流）
		造成		（地震，造成，经济损失）
		导致		（泥石流，导致，人员伤亡）
相关关系	存在逻辑关系的地震灾害实体之间的关系	隶属关系		（地震监测数据，来源，地震监测台站）
		包含关系		（直接经济损失，包含，农牧渔业）
		衍生关系		（防震减灾法，衍生，地方防震减灾规划）
时间关系	地震概念或实例在时间上的相互关系	起始时间		（甘肃地震，起始时间，2023年12月18日）
		结束关系		（甘肃地震，结束时间，2023年12月28日）
		先后关系		（地震释放，先于，地震发生）（地震发生，先于，地震波传播）（地震发生，先于，次生灾害）
		同时关系		（初级波，同时，次级波）
		时间拓扑关系	包含关系	（震中时间，包含，余震）
			相交关系	（A地余震发生时间，相交，B地余震发生时间）
			相邻关系	（A地余震发生时间，相邻，B地余震发生时间）
空间关系	地震概念或地震实例在空间上的相互关系	空间拓扑关系	相等关系	（地震震源，相等，断层）
			包含关系	（震中区域，包含，震源区域）
			邻近关系	（地震震源，相邻，附近地质构造）（A地震断层，相邻，B地震断层）
			相离关系	（震中区域，相离，非震中区域）
		方位关系		（地球板块交界，上面，地震活动区域）
		距离关系		（震源位置，距离，观测点）
		接触关系	整合接触	（索拉克组，整合接触，中奥陶统环形组）
			不整合接触	（索拉克组，不整合接触，下二叠统因格布拉克组）
			断层接触	（索拉克组，断层接触，上奥陶统拉配泉群）
			假整合接触	（童子岩组，假整合接触，茅口组）
组成关系	聚合关系系指弱的整体和部分关系，整体和部分可以相互独立；组合关系系指一种强的整体和部分，整体和部分具有相同的生命周期	聚合关系		（A板块，聚合，B板块）（A断层带，聚合，B断层带）
组成关系		组合关系		（地堑，组合，断裂）
作用关系	地震对象与形成原因之间的关系	成因关系		（断裂，导致，地层错位）
作用关系	地震对象与形成原因之间的关系	影响关系		（地震，影响，人民生活）
演化关系	地壳和地球内部岩石体系发生变化的过程	板块构造演化		（板块，碰撞，地震）（岩石，拗断，断裂）
演化关系	地壳和地球内部岩石体系发生变化的过程	岩浆活动		（岩浆，断裂，火山岩）

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表 3 UIE模型抽取信息精度

Table 3. Accuracy of information extraction of UIE model

类型	类别	精确率	召回率	F₁
实体	地震灾害事件	0.887	0.764	0.823
	地质/地理环境	0.875	0.798	0.848
	地震灾害数据	0.767	0.668	0.708
	地震灾害应急任务	0.898	0.805	0.835
	地震灾害模型方法	0.675	0.421	0.622
关系	并列关系	0.700	0.677	0.691
	属性关系	0.621	0.61	0.652
	因果关系	0.563	0.465	0.533
	相关关系	0.785	0.671	0.726
	时间关系	0.917	0.885	0.862
	空间关系	0.879	0.758	0.841
	组成关系	0.431	0.365	0.422
	作用关系	0.752	0.698	0.673
	演化关系	0.711	0.68	0.600

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表 4 实体-关系类型数量统计

Table 4. Entity-relationship type quantity statistics

实体类型	实体数量	关系类型	关系数量
地震理论知识	360	并列关系	53
地震预防策略	366	属性关系	68178
地质/地理环境	12577	因果关系	132
基础灾害数据	282	相关关系	79
基础地理对象	38947	时间关系	122
灾害对象	24	空间关系	34627
灾情对象	72	组成关系	180
次生灾害对象	30	作用关系	28
地震灾害应急对象	230	演化关系	10
地震灾害模型	30

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