Noise reduction of grounded electrical source airborne transient electromagnetic data using an exponential fitting-adaptive Kalman filter

Abstract

The grounded electrical source airborne transient electromagnetic (GREATEM) system, which uses a grounded electrical transmitter and an aircraft for the receiver, offers deep exploration capability and detection efficiency. However, GREATEM field data usually includes mixed varied noises (white noise, sferics noise and human noise), which make identifying the exponential decaying signal too difficult. Traditional filtering methods mainly focus on suppressing specific noise types, which may cause the distortion of GREATEM signal, especially when the signal is affected by high residual sferics noise. This paper presents an exponential fitting-adaptive Kalman filter (EF-AKF) to remove mixed electromagnetic noises, while preserving the signal characteristics. The EF-AKF consists of an exponential fitting procedure and an adaptive scalar Kalman filter (SKF). The adaptive SKF uses the exponential fitting results in the weighting coefficients calculation. The EF-AKF is verified on an analytical three-layer model. It is compared with the SKF and wavelet threshold-exponential adaptive window width-fitting denoising algorithm (WEF) in synthetic data. The results showed that the EF-AKF outperformed the other methods in the noise reduction of GREATEM data. The EF-AKF is also tested on a synthetic quasi-2D earth model and applied to GREATEM field data in Huaide, Jilin province, China. Application of the EF-AKF allowed considerable improvement of the quality of the GREATEM field data.