{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T04:31:16Z","timestamp":1778301076327,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T00:00:00Z","timestamp":1595980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.<\/jats:p>","DOI":"10.3390\/s20154228","type":"journal-article","created":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T03:36:38Z","timestamp":1596080198000},"page":"4228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Machine Learning Modelling and Feature Engineering in Seismology Experiment"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8158-1278","authenticated-orcid":false,"given":"Michail Nikolaevich","family":"Brykov","sequence":"first","affiliation":[{"name":"Zaporizhzhia Polytechnic National University, 69063 Zaporizhzhia, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Petryshynets","sequence":"additional","affiliation":[{"name":"Institute of Materials Research, Slovak Academy of Sciences, 04001 Kosice, Slovak"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4926-2189","authenticated-orcid":false,"given":"Catalin Iulian","family":"Pruncu","sequence":"additional","affiliation":[{"name":"Mechanical Engineering, Imperial College London, Exhibition Rd., London SW7 2AZ, UK"},{"name":"Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasily Georgievich","family":"Efremenko","sequence":"additional","affiliation":[{"name":"Pryazovskyi State Technical University, Physics department, 87555 Mariupol, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5568-8928","authenticated-orcid":false,"given":"Danil Yurievich","family":"Pimenov","sequence":"additional","affiliation":[{"name":"Department of Automated Mechanical Engineering, South Ural State University, Lenin Prosp. 76, 454080 Chelyabinsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khaled","family":"Giasin","sequence":"additional","affiliation":[{"name":"School of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serhii Anatolievich","family":"Sylenko","sequence":"additional","affiliation":[{"name":"Zaporizhzhia Polytechnic National University, 69063 Zaporizhzhia, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3380-4588","authenticated-orcid":false,"given":"Szymon","family":"Wojciechowski","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Poznan University of Technology, Piotrowo 3, 60\u2013965 Poznan, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ranjan, J., Patra, K., Szalay, T., Mia, M., Gupta, M.K., Song, Q., Krolczyk, G., Chudy, R., Pashnyov, V.A., and Pimenov, D.Y. 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