{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T15:41:26Z","timestamp":1767109286778,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,19]],"date-time":"2023-03-19T00:00:00Z","timestamp":1679184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hungarian National Research, Development and Innovation Fund","award":["K128152","NKM2018-10"],"award-info":[{"award-number":["K128152","NKM2018-10"]}]},{"name":"Bilateral agreement between the Czech and Hungarian Academy of Sciences","award":["K128152","NKM2018-10"],"award-info":[{"award-number":["K128152","NKM2018-10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since various phenomena produce infrasound, including both man-made and natural sources, the ever-growing dataflow demands automatic processes via machine learning for signal classification. In this study, we demonstrate a single array approach at the Piszk\u00e9s-tet\u0151 (PSZI) infrasound array. Our dataset contains nearly 14,000 manually categorized infrasound detections, processed with the progressive multi channel correlation (PMCC) algorithm from three different sources, such as quarry blasts, storms and signals from a power plant. The dataset was split into a training, a validation and a test subset. Time and frequency domain features as well as PMCC-related features were extracted. Three additional PMCC-related features were constructed in a way to measure the similarity between detections and to be used in single array monitoring. Two different classifiers, support vector machine and random forest, were used for training. Training was performed with three-fold cross validation with grid search. The classifiers were tuned on the training and validation set using the f1 metric (harmonic mean of positive predictive value and true positive rate). Training, validation and testing were performed with and without our three new features that measure similarity between the detections in order to assess their importance in single array monitoring. The selected classifiers reached f1 scores between 0.88 and 0.93. Our results show a promising step toward automatic infrasound event classification.<\/jats:p>","DOI":"10.3390\/rs15061657","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"1657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0183-3667","authenticated-orcid":false,"given":"Marcell","family":"P\u00e1sztor","sequence":"first","affiliation":[{"name":"Department of Geophysics and Space Science, Institute of Geography and Earth Sciences, ELTE E\u00f6tv\u00f6s Lor\u00e1nd University, H-1117 Budapest, Hungary"},{"name":"K\u00f6vesligethy Rad\u00f3 Seismological Observatory, Institute of Earth Physics and Space Science (EPSS KRSO), H-9400 Sopron, Hungary"},{"name":"Institute for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences, ELKH, H-1112 Budapest, Hungary"},{"name":"Research Centre for Astronomy and Earth Sciences, MTA Centre of Excellence, Konkoly Thege Mikl\u00f3s \u00fat 15-17, H-1121 Budapest, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1301-9355","authenticated-orcid":false,"given":"Csenge","family":"Czanik","sequence":"additional","affiliation":[{"name":"Department of Geophysics and Space Science, Institute of Geography and Earth Sciences, ELTE E\u00f6tv\u00f6s Lor\u00e1nd University, H-1117 Budapest, Hungary"},{"name":"K\u00f6vesligethy Rad\u00f3 Seismological Observatory, Institute of Earth Physics and Space Science (EPSS KRSO), H-9400 Sopron, Hungary"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4892-1074","authenticated-orcid":false,"given":"Istv\u00e1n","family":"Bond\u00e1r","sequence":"additional","affiliation":[{"name":"Institute for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences, ELKH, H-1112 Budapest, Hungary"},{"name":"Research Centre for Astronomy and Earth Sciences, MTA Centre of Excellence, Konkoly Thege Mikl\u00f3s \u00fat 15-17, H-1121 Budapest, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10712-017-9444-0","article-title":"Toward an Improved Representation of Middle Atmospheric Dynamics Thanks to the ARISE Project","volume":"39","author":"Blanc","year":"2018","journal-title":"Surv. 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