2018
DOI: 10.1071/eg16046
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Noise reduction of grounded electrical source airborne transient electromagnetic data using an exponential fitting-adaptive Kalman filter

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Cited by 36 publications
(13 citation statements)
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“…Decay curves were stacked 8 times to suppress random noise. To remove the electromagnetic signal noise, an exponential fitting-adaptive Kalman filter (EF-AKF) was used (Ji et al, 2018). The denoised field data was continuation downward to the ground.…”
Section: Field Data Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Decay curves were stacked 8 times to suppress random noise. To remove the electromagnetic signal noise, an exponential fitting-adaptive Kalman filter (EF-AKF) was used (Ji et al, 2018). The denoised field data was continuation downward to the ground.…”
Section: Field Data Resultsmentioning
confidence: 99%
“…The interpretation method based on artificial neural networks was applied to the field data, and the results were consistent with the geological data. A section interpretation method, the long wire source is solved by splitting into numbers of electric dipoles (Mogi et al, 1998;Ji et al, 2011), is widely used because of its simplicity and high efficiency (Mogi et al, 2009;Li et al, 2017Li et al, , 2019Ji et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Wang et al used the wavelet-based baseline drift correction method for grounded electrical source airborne transient electromagnetic signals; it can improve the signalto-noise ratio (Wang et al 2013). An exponential fittingadaptive Kalman filter was used to remove mixed electromagnetic noises (Ji et al, 2018). It consists of an exponential fitting procedure and an adaptive scalar Kalman filter.…”
Section: Related Workmentioning
confidence: 99%
“…However, deep learning has been used to reduce noise from images, speech and even gravitational waves (Jifara et al, 2017;Grais et al, 2017;Shen et al, 2017). Meanwhile, the autoencoder (AE) (Bengio et al, 2007), the representative model of deep learning, has been successfully applied in many fields (Hwang et al, 2016).…”
Section: Introductionmentioning
confidence: 99%