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Anomaly detection in the long-term data of radon and thoron measurement in soil using machine learning methods
https://repo.qst.go.jp/records/66931
https://repo.qst.go.jp/records/66931899ce217-b82b-4c1e-b5f7-6c212281aa05
Item type | 会議発表用資料 / Presentation(1) | |||||
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公開日 | 2018-09-28 | |||||
タイトル | ||||||
タイトル | Anomaly detection in the long-term data of radon and thoron measurement in soil using machine learning methods | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_c94f | |||||
資源タイプ | conference object | |||||
アクセス権 | ||||||
アクセス権 | metadata only access | |||||
アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
著者 |
ミロソラフ, ヤニック
× ミロソラフ, ヤニック× Bossew, Peter× Kurihara, Osamu× ミロソラフ ヤニック× 栗原 治 |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Anomaly detection, in data mining, is a process to identification of events that do not match the overall or “background” pattern of items presents in a dataset. Anomalous observations can principally have two origins: 1) A true anomalous process of the observed quantity, distinct from the “background process”, caused by anomalous changes of controlling environmental quantities; 2) A perturbation of the observation process (sampling, measurement), due to device malfunction, statistical outliers, or unplanned response of the device to environmental conditions. Anomaly detection is a hot topic in radon measurement, especially in relation to geospatial analysis of Rn response to seismic activity. In this work, we first discuss shortly the concept of anomaly vs. background. Second, machine learning algorithms and statistical methods were implemented to detecting and classifying anomalies in long-term data series. The study was performed using over 2 years’ data of continuous monitoring of radon, thoron and CO2 concentration in soil gas collected at the QST/NIRS site, Chiba, Japan. The metrological and seismic data were obtained from the Japan Metrological Agency. |
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会議概要(会議名, 開催地, 会期, 主催者等) | ||||||
内容記述タイプ | Other | |||||
内容記述 | 14th International Workshop ? GARRM (GEOLOGICAL ASPECTS OF RADON RISK MAPPING)における発表 | |||||
発表年月日 | ||||||
日付 | 2018-09-20 | |||||
日付タイプ | Issued |