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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/66931
899ce217-b82b-4c1e-b5f7-6c212281aa05
アイテムタイプ 会議発表用資料 / Presentation(1)
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 output
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

WEKO 657949

ミロソラフ, ヤニック

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Bossew, Peter

× Bossew, Peter

WEKO 657950

Bossew, Peter

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Kurihara, Osamu

× Kurihara, Osamu

WEKO 657951

Kurihara, Osamu

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ミロソラフ ヤニック

× ミロソラフ ヤニック

WEKO 657952

en ミロソラフ ヤニック

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栗原 治

× 栗原 治

WEKO 657953

en 栗原 治

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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.
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
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