| Summary: | Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance <i>r</i> is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of <i>r</i> are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value <i>μ</i> and variance value <i>σ</i><sup>2</sup> of that set, we define the decomposition stability <i>w</i> as <i>μ</i>/<i>σ</i><sup>2</sup>. Then, the wavelet that maximizes <i>w</i> for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.
|
|---|