###### Abstract  
The grounded electrical source airborne transient electromagnetic (GREATEM) system is widely used in groundwater resources detection, geothermal resource detection, geological structure detection, and other fields due to its wide detection range, high detection efficiency, and high resolution. The field data received by the GREATEM system is easily affected by various noises, such as instrument system noise, power frequency noise, series noise, and other noise, which reduce the data signal-to-noise ratio (SNR) and affects the data interpretation accuracy. This paper proposes a singular spectrum analysis (SSA) for the GREATEM data de-noising in response to this problem. First, we calculate the electromagnetic response of a uniform half-space using a GREATEM system with an electrical source to verify the effectiveness of the SSA algorithm for GREATEM data de-noising. To determine the appropriate parameters for SSA, we propose a particle swarm optimization algorithm to choose the window length. Later, SSA is used to decompose a synthetic quasi-two-dimensional earth model of GREATEM data. After SSA, the SNR of the reconstructed signal increased by 36 dB, and the RMSE does not exceed 4.9 \(\times\) 10\({}^{-6}\), which verifies the feasibility of the SSA for de-noising GREATEM data. Finally, through field measurement data processing, the effectiveness of the method is further confirmed.  
de-noising; GREATEM; SSA +
Footnote †: journal: Medical Imaging and Image Analysis  
ATEM signals and achieved good results. [PERSON] et al. [14] adopted the EEMD-based adaptive filtering method, which suppresses the baseline drift in the GREATEM signal. [PERSON] et al. [15] proposed a deep learning method based on a de-noising autoencoder (DAE). By constructing a training set of a large amount of simulated data, this can simultaneously deal with multiple types of noise in AEM data and reduce the influence of human subjective factors. [PERSON] et al. [16] proposed a variational Bayesian-based adaptive Kalman filter (VBAKF) to de-noise the GATEM data. The method is based on the results of an exponential fit, which is suitable for exponentially decaying signals. With the development of computer science, many researchers began to use artificial intelligence algorithms to invert electromagnetic data. [PERSON] et al. [8] developed a three-dimensional inversion of semi-airborne transient electromagnetic data based on a particle swarm optimization-gradient (PSO) descent algorithm. [PERSON] et al. [17] proposed a deep learning method to estimate the earth resistivity model for ATEM observation. These methods perform the de-noising and inversion functions at the same time.  
Adaptive filtering methods require the collection of noisy data near the measured data for adaptive cancellation in the above methods. The correlation between the noise data and the original data is often too significant, resulting in poor noise elimination. The wavelet de-noising method needs to select a suitable wavelet base and threshold according to the characteristics of the noise, which is not universal [4; 9; 10; 11]. The EMD may miss helpful information and has a modal aliasing problem due to its frequency band segmentation characteristics [12; 14; 18; 19].  
Singular spectrum analysis (SSA) was first proposed by [PERSON] and [PERSON] [20] and developed from the Karhumen-Leove decomposition theory [21]. It is a new time series analysis method, which is used chiefly in signal de-noising and prediction. It shows excellent gravity and magnetic potential field separation and magnetic resonance sounding (MRS) signal de-noising [22; 23]. Therefore, we proposed to use singular spectrum analysis to remove noises in GREATEM data.