1. Application of empirical mode decomposition (EMD) filtering at magnetotelluric time-series data.
- Author
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Setiawan, Nugroho Syarif, Widodo, Amien, Lestari, Wien, Syaifuddin, Firman, Zarkasyi, Ahmad, Warnana, Dwa Desa, Rochman, Juan Pandu Gya Nur, Sunaryono, Sunaryono, Hirt, Ann Marie, Herrin, Jason Scott, Muztaza, Nordiana Mohd, Diantoro, Markus, and Bijaksana, Satria
- Subjects
HILBERT-Huang transform ,FREQUENCY-domain analysis ,FOURIER transforms ,FREQUENCY spectra ,STATISTICAL smoothing ,ACQUISITION of data ,FILTERING software - Abstract
A noise that recorded at magnetotelluric acquisition data makes the data quality not good enough, so the information obtained after data processing might not be correct or not suitable for the subsurface condition. Several characters of noisy magnetotelluric data are the spiky shaped and non-stationarity time-series curves. This non-stationarity character can't be handled by the Fourier Transformation process. This research used Empirical Mode Decomposition (EMD) in the original of Huang as a filtering method in order to overcome the non-stationarity. This method decomposed the signal into a group of oscillation mode called Intrinsic Mode Decomposition (IMF). It is one of the best IMF chosen as the filtering result by spectrum analysis in the frequency domain. This work used magnetotelluric data from a station that had three components with different frequency sampling, which was 15 Hz, 150 Hz, and 2400 Hz. IMF filtering method then applied to the data resulting in a smoother times-series curve with the suppressed non-stationarity character. This research showed that EMD filtering can be implemented at magnetotelluric data processing and emphasized the effect caused by noise. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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