首页|基于大规模预训练模型的地质矿物属性识别方法及应用

基于大规模预训练模型的地质矿物属性识别方法及应用

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地球科学的研究成果通常记录在技术报告、期刊论文、书籍等文献中,但许多详细的地球科学报告未被使用,这为信息提取提供了机遇.为此,我们提出了一种名为GMNER(Geological Minerals named entity recognize,MNER)的深度神经网络模型,用于识别和提取矿物类型、地质构造、岩石与地质时间等关键信息.与传统方法不同,本次采用了大规模预训练模型 BERT(Bidirectional Encoder Representations from Transformers,BERT)和深度神经网络来捕捉上下文信息,并结合条件随机场(Conditional random field,CRF)以获得准确结果.实验结果表明,MNER模型在中文地质文献中表现出色,平均精确度为0.898 4,平均召回率0.922 7,平均Fl分数0.9104.研究不仅为自动矿物信息提取提供了新途径,也有望促进矿产资源管理和可持续利用.
Geological Mineral Attribute Recognition Method Based on Large-Scale Pre-Trained Model and Its Application
Geoscience research results are usually documented in technical reports,journal papers,books,and other lit-erature;however,many detailed geoscience reports are unused,which provides challenges and opportunities for informa-tion extraction.To this end,we propose a deep neural network model called GMNER(Geological Minerals named entity recognize,MNER)for recognizing and extracting key information such as mineral types,geological formations,rocks,and geological time.Unlike traditional methods,we employ a large-scale pre-trained model BERT(Bidirectional Encod-er Representations from Transformers,BERT)and deep neural network to capture contextual information and combine it with a conditional random field(CRF)to obtain more accurate and accurate information.The experimental results show that the MNER model performs well in Chinese geological literature,achieving an average precision of 0.8984,an aver-age recall of 0.9227,and an average Fl score of 0.9104.This study not only provides a new way for automated mineral information extraction but also is expected to promote the progress of mineral resource management and sustainable utili-zation.

Mineral information extractionDeep neural networkMineral documentationNamed entity recognition

王彬彬、周可法、王金林、汪玮、李超、程寅益

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中国科学院新疆生态与地理研究所新疆矿产资源研究中心,新疆乌鲁木齐 830011

中国科学院大学,北京 100049

新疆矿产资源与数字地质重点实验室,新疆 乌鲁木齐 830011

中国科学院空间应用工程与技术中心,北京 100094

中国地质大学(武汉)地质调查研究院湖北武汉 430074

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矿物信息提取 深度神经网络 矿物文献 命名实体识别

新疆维吾尔自治区科技重大专项新疆科学考察项目深空大数据智能建设项目

2021A03001-32022xjkk1306292022000059

2024

新疆地质
新疆维吾尔自治区地质学会

新疆地质

CSTPCD
影响因子:0.879
ISSN:1000-8845
年,卷(期):2024.42(1)
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