TY - GEN
T1 - Application of empirical mode decomposition (EMD) filtering at magnetotelluric time-series data
AU - Setiawan, Nugroho Syarif
AU - Widodo, Amien
AU - Lestari, Wien
AU - Syaifuddin, Firman
AU - Zarkasyi, Ahmad
AU - Warnana, Dwa Desa
AU - Rochman, Juan Pandu Gya Nur
N1 - Publisher Copyright:
© 2020 Author(s).
PY - 2020/8/18
Y1 - 2020/8/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85091603193&partnerID=8YFLogxK
U2 - 10.1063/5.0015767
DO - 10.1063/5.0015767
M3 - Conference contribution
AN - SCOPUS:85091603193
T3 - AIP Conference Proceedings
BT - International Conference on Electromagnetism, Rock Magnetism and Magnetic Material, ICE-R3M 2019
A2 - Sunaryono, Sunaryono
A2 - Hirt, Ann Marie
A2 - Herrin, Jason Scott
A2 - Muztaza, Nordiana Mohd
A2 - Diantoro, Markus
A2 - Bijaksana, Satria
PB - American Institute of Physics Inc.
T2 - 2019 International Conference on Electromagnetism, Rock Magnetism and Magnetic Material, ICE-R3M 2019
Y2 - 18 September 2019 through 19 September 2019
ER -