Recognition and application of geological entities related to ore-forming conditions in the Kaiyang phosphate mine based on the XLNET model
| Autor: | Bin PENG, Yiping TIAN, Bin ZENG, Xuechao WU, Wenming WU |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2024 |
| Předmět: |
geological entity recognition
extreme learning machine network(xlnet)-bidirectional long short term memory(bilstm)-self attention layer(attention)-conditional random field(crf) metallogenic model of phosphate ore pre-training model sequence labeling Geology QE1-996.5 Engineering geology. Rock mechanics. Soil mechanics. Underground construction TA703-712 |
| Zdroj: | 地质科技通报, Vol 43, Iss 4, Pp 224-234 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2096-8523 |
| DOI: | 10.19509/j.cnki.dzkq.tb20230543 |
| Popis: | Objective With increasing difficulty in phosphate ore prospecting, there are an increasing number of geological exploration reports. The manual recognition of geological information related to phosphate rock mineralization in massive documents is time-consuming and inefficient. It cannot meet the needs of knowledge sharing, dissemination and intelligent management of geological reports. Methods To quickly obtain the ore-forming geological knowledge hidden in the phosphate ore reports, this work intends to establish an automatic recognition method for ore-forming geological entities based on the extreme learning machine network(XLNET) model. First, BIO labelling of entities was carried out to establish a geological entity dictionary, and XLNET was used as the underlying preprocessing model to learn the bidirectional semantics of sentences. Then, the BILSTM-Attention-CRF(bidirectional long short term memory(BILSTM)-self attention layer(Attention)-conditional random field(CRF)) model was used to realize intelligent classification of multiple text labels. Finally, the ore-forming conditions and ore-forming model of phosphate ore in the reports were roughly predicted by locating the distribution position of phosphate ore entities in the report. Results Comparing this model with the other three models, these results show that the accuracy rate, recall rate and F1 value of this model are close to 90%, which are 2%, 5% and 6% higher than those of the previous three models, respectively. Conclusion This study provides a more efficient method for automatic geological entity recognition for geological researchers in the Kaiyang phosphate mine. |
| Databáze: | Directory of Open Access Journals |
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