Artificial Intelligence Methods for Scientific Research in Geology
Main Article Content
Abstract
A brief overview of some methods of artificial intelligence in the field of Earth sciences is given. The prospects of using these methods to obtain new knowledge are noted. The results of the authors' first attempts to apply natural language processing methods for processing scientific articles on geology are presented. The possibilities of developing work in this direction are discussed.
Article Details
References
2. Kaplmеan A., Haenlein M. Siri, Siri in my Hand, who’s the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence // Business Horizons. 2019. V. 62, No. 1. P. 15–25.
3. PROSPECTOR // URL: http://www.computing.surrey.ac.uk/ai/PROFILE /prospector.html (дата обращения 18.09.2023)
4. ESRI // URL: https://www.esri.com/en-us/home (дата обращения 18.09.2023)
5. USGS // URL: https://www.usgs.gov/ (дата обращения 18.09.2023)
6. Родионов С.М., Сыркин В.К. Экспертная прогнозирующая система «Олово» // Тихоокеанская геология. 1995. Т. 14, №5. С. 63–71. URL: http://itig.as.khb.ru/POG/archive/1995/N5_1995.pdf
7. SOLSA Expert System // URL: https://solsa-dem-up.eu/en (дата обращения 17.09.2023)
8. GoldSpot // URL: https://www.alsglobal.com/en/consulting-and-analytics (дата обращения 18.09.2023)
9. SRK Consulting // URL: https://www.srk.com/ru/ (дата обращения 18.09.2023)
10. Maptek // URL: https://www.maptek.com/ (дата обращения 18.09.2023)
11. IOS Services Geoscientifiques // URL: https://www.iosgeo.com/en/ (дата обращения 18.09.2023)
12. Orefox // URL: https://orefox.com/ (дата обращения 18.09.2023)
13. Geolearn // URL: https://www.geolearn.ai/ (дата обращения 18.09.2023)
14. Datarock // URL: https://datarock.com.au/platform/ (дата обращения 18.09.2023)
15. Baraboshkin E.E., Ismailova L.S., Orlov D.M., Zhukovskaya E.A., Kalmykov G.A., Khotylev O.V., Baraboshkin E.Yu., Koroteev D.A. Deep Convolutions for In-Depth Automated Rock Typing // Computers & Geosciences. 2020. V. 135. https://doi.org/10.1016/j.cageo.2019.104330
16. Nesteruk S., Agafonova J., Pavlov I., Gerasimov M., Latyshev N., Dimitrov D., Kuznetsov A., Kadurin A., Plechov P. MineralImage5k: A benchmark for zero-shot raw mineral visual recognition and description // Computers & Geosciences. 2023. V. 178. https://doi.org/10.1016/j.cageo.2019.104330
17. Обработка естественного языка // URL: https://ru.wikipedia.org/wiki/Обработка_естественного_языка (дата обращения 18.09.2023)
18. Jurafsky D., Martin J.H. N-gram Language Models // Speech and Language Processing 3rd. 2021.
19. Deng C., Zhang T., He Z., Chen Q., Shi Y., Zhou L., Fu L., Zhang W., Wang X., Zhou C., Lin Z., He J. Learning Foundation Language Models for Geoscience Knowledge Understanding and Utilization // arXiv:2306.05064, 2023. URL: https://arxiv.org/abs/2306.05064v1
20. K2 model // URL: https://github.com/davendw49/k2?ysclid=lmswxywt6i750905070 (дата обращения 18.09.2023)
21. Lawley C.J.M., Raimondo S., Chen T., Brin L., Zakharov A., Kur D., Hui J., Newton G., Burgoyne S.L., Marquis G. Geoscience language models and their intrinsic evaluation // Applied Computing and Geosciences. 2022. V. 14, 100084. P. 1–10.
22. Wang B., Ma K., Wu L., Qiu Q., Xie Z., Tao L. Visual analytics and information extraction of geological content for text-based mineral exploration reports // Ore Geology Reviews. 2022. V. 144, 104818. P. 1–12.
23. Padarian J., Fuentes I. Word embeddings for application in geosciences: development, evaluation, and examples of soil-related concepts // SOIL. 2019. V. 5. P. 177–187.
24. Lawley C.J.M., Gadd M.G., Parsa M., Lederer G.W., Graham G.E., Ford A. Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling // Natural Resources Research. 2023. V. 32, No. 4. P. 1503–1527.
25. Fuentes I., Padarian J., Iwanaga T., Vervoort R.W. 3D lithological mapping of borehole descriptions using word embeddings // Computers & Geosciences. 2020. V. 141, 104516.
26. Qiu Qinjun, Xie Zhong, Wu Liang, Li Wenjia. Geoscience keyphrase extraction algorithm using enhanced word embedding // Expert Systems with Applications. 2019. V. 125. P. 157–169.
27. Патук М.И., Наумова В.В., Ерёменко В.С. Цифровой репозиторий "geologyscience.ru": открытый доступ к научным публикациям по геологии России. // Электронные библиотеки. 2020. Т. 23, № 6. С. 1324–1338. https://doi.org/10.26907/1562-5419-2020-23-6-1324-1338
28. Патук М.И., Наумова В.В. Построение цифровой системы управления геологическими знаниями для поддержки научных исследований. // Электронные библиотеки. 2022. Т. 25, № 2. С. 148–158. https://doi.org/10.26907/1562-5419-2022-25-2-148-158
29. Bourke D. 08. Natural Language Processing with TensorFlow. URL: https://dev.mrdbourke.com/tensorflow-deep-learning/08_introduction_to_nlp_in_tensorflow/ (дата обращения 18.09.2023)
30. mrdbourke / tensorflow-deep-learning. URL: https://github.com/mrdbourke/tensorflow-deep-learning (дата обращения 18.09.2023)
31. Pdfreader 0.1.12. URL: https://pypi.org/project/pdfreader/ (дата обращения 18.09.2023)
32. spaCy URL: https://spacy.io/models/ru (дата обращения 18.09.2023)
33. Spacy-stanza. URL: https://spacy.io/universe/project/spacy-stanza (дата обращения 18.09.2023)
34. SberDevice. Как мы анализируем предпочтения пользователей виртуальных ассистентов Салют. URL: https://habr.com/ru/companies/ sberdevices/articles/547568/ (дата обращения 18.09.2023)
35. Zero-shot learning. URL: https://en.wikipedia.org/wiki/Zero-shot_learning (дата обращения 18.09.2023)
36. Ванюшкин А.С., Гращенко Л.А. Методы и алгоритмы извлечения ключевых слов // Новые информационные технологии в автоматизированных системах. 2016. С. 85–93.
37. Pay T., Lucci F., Cox J.L. An Ensemble of Automatic Keyword Extractors: TextRank, RAKE and TAKE // Computación y Sistemas. 2019. V. 23, No. 3. P. 703–710.
https://doi.org/10.13053/CyS-23-3-3234
38. Дале Д. Многозадачная модель T5 для русского языка. URL: https://habr.com/ru/articles/581932/ (дата обращения 18.09.2023)
39. Данил, keyT5 или генерация ключевых слов из текста. URL: https://habr.com/ru/articles/599715/ (дата обращения 18.09.2023)
40. Yandex DataSphere. URL: https://datasphere.yandex.ru/?yc-skip-auth=1 (дата обращения 18.09.2023)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Presenting an article for publication in the Russian Digital Libraries Journal (RDLJ), the authors automatically give consent to grant a limited license to use the materials of the Kazan (Volga) Federal University (KFU) (of course, only if the article is accepted for publication). This means that KFU has the right to publish an article in the next issue of the journal (on the website or in printed form), as well as to reprint this article in the archives of RDLJ CDs or to include in a particular information system or database, produced by KFU.
All copyrighted materials are placed in RDLJ with the consent of the authors. In the event that any of the authors have objected to its publication of materials on this site, the material can be removed, subject to notification to the Editor in writing.
Documents published in RDLJ are protected by copyright and all rights are reserved by the authors. Authors independently monitor compliance with their rights to reproduce or translate their papers published in the journal. If the material is published in RDLJ, reprinted with permission by another publisher or translated into another language, a reference to the original publication.
By submitting an article for publication in RDLJ, authors should take into account that the publication on the Internet, on the one hand, provide unique opportunities for access to their content, but on the other hand, are a new form of information exchange in the global information society where authors and publishers is not always provided with protection against unauthorized copying or other use of materials protected by copyright.
RDLJ is copyrighted. When using materials from the log must indicate the URL: index.phtml page = elbib / rus / journal?. Any change, addition or editing of the author's text are not allowed. Copying individual fragments of articles from the journal is allowed for distribute, remix, adapt, and build upon article, even commercially, as long as they credit that article for the original creation.
Request for the right to reproduce or use any of the materials published in RDLJ should be addressed to the Editor-in-Chief A.M. Elizarov at the following address: amelizarov@gmail.com.
The publishers of RDLJ is not responsible for the view, set out in the published opinion articles.
We suggest the authors of articles downloaded from this page, sign it and send it to the journal publisher's address by e-mail scan copyright agreements on the transfer of non-exclusive rights to use the work.