LS-SVM (Least Squares-Support Vector Machines) are applied to seismic prospecting signals denoising so as to
suppress the stochastic noise in this paper. Firstly, we propose and prove a new admissible support vector kernel-Ricker wavelet kernel, which is superior to the popular RBF (radial basis function) kernel in terms of the waveform
retrieved and SNR (Signal to Noise Ratio) gained when applied to the noise reduction of seismic prospecting signals.
LS-SVM embed two tuning parameters which may diminish the overall performance of LS-SVM if not well chosen, so
we investigate the selection of LS-SVM parameters including kernel parameter and regularization parameter,
respectively. We can conclude that Ricker wavelet kernel parameter should be set as the predominant frequency of
seismic signal and regularization parameter γ can be accepted in a wide range. Our denoising experimental results show
that the performance of Ricker wavelet LS-SVM using the aforementioned parameters setting outperforms Wiener
filtering, median filtering and LS-SVM based on RBF kernel in terms of the definition of seismic prospecting event
retrieved and SNR gained.