Geological Survey Data and the Move from 2‐<scp>D</scp>to 4‐<scp>D</scp>

Culshaw, M. G.; Jackson, Ian; Peach, Denis; Meulen, Michiel J. van der; Berg, Richard C.; Thorleifson, Harvey

doi:10.1002/9781119163091.ch2

Cited by 3 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geological maps also provide a high value to society, with an estimated 4:1 to 100:1 benefit-to-cost ratios (Berg et al, 2019). The observations and technical knowledge encoded in these databases over many decades represents a massive human effort, with contributions from hundreds of geoscientists, and are probably the most important contribution of geological survey organizations to society (Howard et al, 2009;Lebel 2020;Culshaw et al, 2021). Most geological map databases, and geoscience more generally, contain an untapped wealth of information in the form of unstructured and semi-structured text data.…”

Section: Natural Language Processing For Knowledge Discoverymentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Natural Language Processing For Knowledge Discoverymentioning

confidence: 99%