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SIGNAL-NOISE IDENTIFICATION of MAGNETOTELLURIC SIGNALS USING FRACTAL-ENTROPY and CLUSTERING ALGORITHM for TARGETED DE-NOISING

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Version 1 2019-06-27, 15:52
journal contribution
posted on 2024-06-05, 04:30 authored by J Li, X Zhang, James GongJames Gong, J Tang, Z Ren, G Li, Y Deng, J Cai
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. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

History

Journal

Fractals

Volume

26

Article number

ARTN 1840011

Pagination

1 - 18

Location

Singapore

Open access

  • Yes

ISSN

0218-348X

eISSN

1793-6543

Language

English

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2018, The Author(s)

Issue

2

Publisher

WORLD SCIENTIFIC PUBL CO PTE LTD