Processing Time-domain Induced Polarization Data Using Variational Mode Decomposition

TY - JOUR

T1 - Processing Time-domain Induced Polarization Data Using Variational Mode Decomposition

AU - Naveed, Khuram

AU - Madsen, Line Meldgaard

AU - Grombacher, Denys

AU - Larsen, Jakob Juul

PY - 2024

Y1 - 2024

N2 - Time-domain induced polarization (TDIP) data are often perturbed by undesired electrical and electromagnetic responses, i.e., powerline harmonics, spikes etc., which along with the random noise from the instrument mask the measured IP data. This limits access to the spectral content, which is vital for characterizing the electrical properties of rocks, soil and minerals in the subsurface. Therefore, mitigating noise responses is key to gaining access to the spectral content. To this end, existing literature assumes prior noise models to deal with the harmonic, spike, and random noises whereby errors in the estimated model parameters can leave residuals, which are significant enough to question the reliability of the inversion results. To address this problem, we employ variational mode decomposition (VMD) that uses a data driven approach to decompose a TDIP signal into its intrinsic oscillatory modes, i.e., amplitude modulated-frequency modulated (AM-FM) sinusoids. The optimization based decomposition framework in VMD dynamically adjusts filtering properties according to the nature of the input TDIP signal. Since IP decays and noise have different spectral properties, we employ a multi-stage VMD operation to gradually isolate the signal of interest from noise. Through simulations and field examples we demonstrate that the proposed VMD based approach enhances the reconstruction of the IP decay and hence improves its inversion and interpretation.

AB - Time-domain induced polarization (TDIP) data are often perturbed by undesired electrical and electromagnetic responses, i.e., powerline harmonics, spikes etc., which along with the random noise from the instrument mask the measured IP data. This limits access to the spectral content, which is vital for characterizing the electrical properties of rocks, soil and minerals in the subsurface. Therefore, mitigating noise responses is key to gaining access to the spectral content. To this end, existing literature assumes prior noise models to deal with the harmonic, spike, and random noises whereby errors in the estimated model parameters can leave residuals, which are significant enough to question the reliability of the inversion results. To address this problem, we employ variational mode decomposition (VMD) that uses a data driven approach to decompose a TDIP signal into its intrinsic oscillatory modes, i.e., amplitude modulated-frequency modulated (AM-FM) sinusoids. The optimization based decomposition framework in VMD dynamically adjusts filtering properties according to the nature of the input TDIP signal. Since IP decays and noise have different spectral properties, we employ a multi-stage VMD operation to gradually isolate the signal of interest from noise. Through simulations and field examples we demonstrate that the proposed VMD based approach enhances the reconstruction of the IP decay and hence improves its inversion and interpretation.

KW - Current measurement

KW - Full waveform IP decay

KW - Geophysical measurements

KW - Harmonic analysis

KW - Intrinsic mode function (IMF)

KW - IP networks

KW - Logic gates

KW - Noise measurement

KW - Power harmonic filters

KW - Time-domain induced polarization (TDIP)

KW - Variational mode decomposition (VMD)

KW - Full waveform induced polarization (IP) decay

KW - intrinsic mode function (IMF)

KW - time-domain IP (TDIP)

KW - variational mode decomposition (VMD)

UR - http://www.scopus.com/inward/record.url?scp=85184818637&partnerID=8YFLogxK

U2 - 10.1109/TGRS.2024.3362290

DO - 10.1109/TGRS.2024.3362290

M3 - Journal article

AN - SCOPUS:85184818637

SN - 0196-2892

VL - 62

SP - 1

EP - 13

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

M1 - 5905713

ER -