On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data

TY - ABST

T1 - On The Presence of Correlated Noise in Transient Electromagnetic (Tem) Monitoring Data

AU - McLachlan, Paul Jack

AU - Khare, Smith Kashiram

AU - Grombacher, Denys

AU - Larsen, Jakob Juul

AU - A. Christiensen, A.

AU - Luria, J.C. Zamora

PY - 2022/9

Y1 - 2022/9

N2 - This work investigates a novel approach for investigating correlated measurement errors in TEM data. Generally, measurement errors are assumed uncorrelated. However, noise sources include very-low-frequency (VLF) signals such as radio waves, which are not random. TEM monitoring, where systems are installed semi-permanently, offers the unique opportunity to investigate measurement errors more thoroughly. The signal-to-noise ratio in TEM data is generally improved by data stacking; however, continued stacking of correlated noise sources may introduce bias. Such biases are significant when the target of interest is subtle variations such as groundwater table variations. This work provides some background on the general treatment of errors in TEM data before an approach utilizing covariance matrices for characterizing correlated noise is introduced. The noise in a monitoring TEM data set indicated that errors were strongly correlated; furthermore, patterns in covariance matrices change between daily surveys showing that the proportions of VLF signals are not stable over time.

AB - This work investigates a novel approach for investigating correlated measurement errors in TEM data. Generally, measurement errors are assumed uncorrelated. However, noise sources include very-low-frequency (VLF) signals such as radio waves, which are not random. TEM monitoring, where systems are installed semi-permanently, offers the unique opportunity to investigate measurement errors more thoroughly. The signal-to-noise ratio in TEM data is generally improved by data stacking; however, continued stacking of correlated noise sources may introduce bias. Such biases are significant when the target of interest is subtle variations such as groundwater table variations. This work provides some background on the general treatment of errors in TEM data before an approach utilizing covariance matrices for characterizing correlated noise is introduced. The noise in a monitoring TEM data set indicated that errors were strongly correlated; furthermore, patterns in covariance matrices change between daily surveys showing that the proportions of VLF signals are not stable over time.

U2 - 10.3997/2214-4609.202220119

DO - 10.3997/2214-4609.202220119

M3 - Conference abstract for conference

SP - 1

EP - 5

T2 - NSG2022 28th European Meeting of Environmental and Engineering Geophysics

Y2 - 18 September 2022 through 22 September 2022

ER -