{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:29:18Z","timestamp":1770748158665,"version":"3.49.0"},"reference-count":89,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T00:00:00Z","timestamp":1604620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2018YFC1503505"],"award-info":[{"award-number":["2018YFC1503505"]}]},{"name":"the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration","award":["2020IEF0510"],"award-info":[{"award-number":["2020IEF0510"]}]},{"name":"the Special Fund of the Institute of Earthquake Forecasting, China Earthquake Administration","award":["2020IEF0705"],"award-info":[{"award-number":["2020IEF0705"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The low-altitude satellite DEMETER recorded many cases of ionospheric perturbations observed on occasion of large seismic events. In this paper, we explore 16 spot-checking classification algorithms, among which, the top classifier with low-frequency power spectra of electric and magnetic fields was used for ionospheric perturbation analysis. This study included the analysis of satellite data spanning over six years, during which about 8760 earthquakes with magnitude greater than or equal to 5.0 occurred in the world. We discover that among these methods, a gradient boosting-based method called LightGBM outperforms others and achieves superior performance in a five-fold cross-validation test on the benchmarking datasets, which shows a strong capability in discriminating electromagnetic pre-earthquake perturbations. The results show that the electromagnetic pre-earthquake data within a circular region with its center at the epicenter and its radius given by the Dobrovolsky\u2019s formula and the time window of about a few hours before shocks are much better at discriminating electromagnetic pre-earthquake perturbations. Moreover, by investigating different earthquake databases, we confirm that some low-frequency electric and magnetic fields\u2019 frequency bands are the dominant features for electromagnetic pre-earthquake perturbations identification. We have also found that the choice of the geographical region used to simulate the training set of non-seismic data influences, to a certain extent, the performance of the LightGBM model, by reducing its capability in discriminating electromagnetic pre-earthquake perturbations.<\/jats:p>","DOI":"10.3390\/rs12213643","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T09:09:30Z","timestamp":1604653770000},"page":"3643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Identification of Electromagnetic Pre-Earthquake Perturbations from the DEMETER Data by Machine Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1447-7806","authenticated-orcid":false,"given":"Pan","family":"Xiong","sequence":"first","affiliation":[{"name":"Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China"}]},{"given":"Cheng","family":"Long","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Informatics, University of Leicester, Leicester LE1 7RH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5808-7239","authenticated-orcid":false,"given":"Roberto","family":"Battiston","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Trento, 38123 Trento, Italy"},{"name":"National Institute for Nuclear Physics, The Trento Institute for Fundamental Physics and Applications, 38123 Trento, Italy"}]},{"given":"Xuemin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China"}]},{"given":"Xuhui","family":"Shen","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s11589-013-0053-9","article-title":"Progress and development on multi-parameters remote sensing application in earthquake monitoring in China","volume":"26","author":"Shen","year":"2013","journal-title":"Earthq. 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