Signal-noise identification of magnetotelluric signals using fractal-entropy and clustering algorithm for targeted de-noising

Release time:2018-12-26

Affiliation of Author(s):Cent S Univ, Key Lab Metallogen Predict Nonferrous Met & Geol, Sch Geosci & Infophys, Minist Educ, C

Journal:Fractals

Funded by:国家自然科学基金

Key Words:Magnetotelluric; Fractal-Entropy; Clustering; Signal-Noise Identification; De-Noising

Abstract: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.

Co-author:G, ZY;Li, JT ;; Ren, JZ ; Tang, X ; Gong, J; Zhang, Li

Indexed by:Applied Research

Discipline:地质资源与地质工程

Document Type:J

Volume:2

Issue:26

Translation or Not:no

Date of Publication:2018-09-01