Signal-noise identification of magnetotelluric signals using fractal-entropy and clustering algorithm for targeted de-noising
发布时间：2018-12-26
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所属单位：Cent S Univ, Key Lab Metallogen Predict Nonferrous Met & Geol, Sch Geosci & Infophys, Minist Educ, C
发表刊物：Fractals
项目来源：国家自然科学基金
关键字：Magnetotelluric; Fractal-Entropy; Clustering; Signal-Noise Identification; De-Noising
摘要：A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters - fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) - are extracted from MT time-series.
合写作者：G, ZY;Li, JT ;; Ren, JZ ; Tang, X ; Gong, J; Zhang, Li
论文类型：应用研究
学科门类：地质资源与地质工程
文献类型：J
卷号：2
期号：26
是否译文：否
发表时间：2018-09-01