ISSN 1009-6248CN 61-1149/P Bimonthly

Supervisor：China Geological Survey

Sponsored by：XI'an Center of China Geological Survey
Geological Society of China

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QIU Qinjun, MA Kai, ZHU Henghua, et al. BERT-based Method and Significance of Constraint Information Extraction for 3D Geological Modelling[J]. Northwestern Geology, 2022, 55(4): 124-132. DOI: 10.19751/j.cnki.61-1149/p.2022.04.011
Citation: QIU Qinjun, MA Kai, ZHU Henghua, et al. BERT-based Method and Significance of Constraint Information Extraction for 3D Geological Modelling[J]. Northwestern Geology, 2022, 55(4): 124-132. DOI: 10.19751/j.cnki.61-1149/p.2022.04.011
shu

BERT-based Method and Significance of Constraint Information Extraction for 3D Geological Modelling

  • 1. School of Computer Sciences, China University of Geosciences, Wuhan 430074, Hubei, China;

  • 2. Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430074, Hubei, China;

  • 3. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, Hubei, China;

  • 4. College of Computer and Information Technology, China Three Gorges University, Yichang 443002, Hubei, China;

  • 5. Shandong Institute of Geological Survey, Jinan 250000, Shandong, China

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  • Received Date: January 25, 2022
  • Revised Date: June 19, 2022
  • Available Online: January 09, 2023
    Graphical Abstract
    • Abstract
  • The geometry, topology and attribute information of geological bodies in geological reports are important constraint information in the 3D geological modeling process. However, the traditional attribute information extraction methods have problems such as limited coverage, limited to artificial design features and poor model generalization ability. Facing the 3D modeling task, the geometry, topology and attribute text characteristics of geological bodies in geological reports are summarized, and a 3D geological modeling information extraction method based on BERT-BiLSTM-CRF is proposed; based on the BERT pre-training model, a deep learning model integrating BiLSTM and CRF is constructed to obtain deep semantic information of dynamic characters through the BERT model to make up for the static word vector cannot solve the problem of multiple meanings of a word, and improve the extraction ability of complex modeling information of geological bodies. The model performance is evaluated with 43 geological reports as the data source, and the experimental results show that the proposed method has an accuracy rate of over 90% for extracting three types of attribute information of geological bodies, which is an important support for 3D geological modeling.
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