Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling

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dc.contributor.author Lawley C.J.M.
dc.contributor.author Gadd M.G.
dc.contributor.author Parsa M.
dc.contributor.author Lederer G.W.
dc.contributor.author Graham G.E.
dc.contributor.author Ford A.
dc.date.accessioned 2023-07-23T01:39:36Z
dc.date.available 2023-07-23T01:39:36Z
dc.date.issued 2023
dc.identifier.citation Natural Resources Research, 2023, Vol. 32, No. 4, p.1503-1527 ru_RU
dc.identifier.uri https://repository.geologyscience.ru/handle/123456789/41597
dc.description.abstract Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of ‘‘pegmatitic’’ (AUC = 0.962) and ‘‘alkalic’’ (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials. ru_RU
dc.language.iso en ru_RU
dc.subject Natural language processing ru_RU
dc.subject Language model ru_RU
dc.subject Word embedding ru_RU
dc.subject Semantics ru_RU
dc.subject Prospectivity ru_RU
dc.subject Critical mineral ru_RU
dc.title Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling ru_RU
dc.type Article ru_RU
dc.identifier.doi 10.1007/s11053-023-10216-1


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