Audio magnetotelluric denoising via variational mode decomposition and adaptive dictionary learning

Abstract

The audio magnetotelluric (AMT) data are inevitably corrupted by cultural noise in the AMT exploration. This noise will distort apparent resistivity and phase, which affects the geophysical interpretation. The current AMT denoising technologies can not perform well when the cultural noise is severe. In order to improve the denoising accuracy, we propose a denoising technology called V-DDTF, which combines the variational mode decomposition (VMD) and the data-driven tight frame (DDTF). In the V-DDTF, The instantaneous frequency of the AMT signal envelope is first used to determine the number of decomposition layers. Then, the AMT signal is decomposed to several intrinsic mode functions (IMFs) and a residual by the VMD, after which the IMFs and a residual are rearranged as the input to learn dictionaries based on a tight frame constraint. To optimize the selection of parameters and improve denoising accuracy, we adopt an adaptive dictionary learning strategy. Finally, the inverse transforms are applied to the coefficients to achieve the AMT denoising. In our experiments, we validate the V-DDTF by synthetic data. We compare the V-DDTF with some other denoising methods and use the signal-to-noise ratio (SNR) and the Nyquist diagram as the assessments. Consequently, the V-DDTF not only creates dependable denoising results but also achieves the best performance among these mentioned methods. Furthermore, the field data experiments show the potential of the V-DDTF in industrial applications.