# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 5 Conclusions  
The selection of wavelet quantization has always been a subject to be studied. Therefore, we proposed a wavelet selection method based on decomposition stability for the actual needs of seismic data processing. For a specific wavelet, we performed wavelet  
Figure 9: When calculating \(w\), the influence of different wavelet packet decomposition levels.  
packet decomposition based on the wavelet, reconstructed the signal with each wavelet packet coefficient separately, calculated the correlation coefficient with the original waveform, and calculated the variance contribution rate between the wavelet packet coefficients. The relevance was calculated by a signal's correlation coefficient and variance contribution rate. Finally, the mean and variance of relevance were calculated on a dataset, and then the decomposition stability \(w\) was calculated according to Formula (10). Among the wavelets to be selected, the wavelet that maximizes the decomposition stability \(w\) of the dataset is the optimal wavelet.  
To verify the feasibility of our method, we conducted experiments on automatic mining-induced microseismic events' classification and automatic P arrival picking. In both types of experiments, we used the wavelet packet coefficients obtained after wavelet packet transformation as the input of the deep learning network. The wavelet rbio3.1 selected by our method was compared with other randomly selected wavelets in these experiments. The experimental results show that the selection of wavelets can affect the results' accuracy, and rbio3.1 makes the optimal accuracy. In the classification experiments, the mean accuracy is 93.13% using rbio3.1, 2.22% more accurate than other wavelets generated, and in the picking experiments, the mean picking error is 0.59 s using rbio3.1, but this is 0.71 s using others. In addition, we also analyzed the effect of the wavelet packet decomposition level on the results, showing that the level does not affect the selection of wavelets. In general, for the current development status of seismology, we propose a wavelet selection method suitable for intelligent seismic data processing, and we have proved our method's effectiveness through experiments.  
Conceptualization, Z.H. and S.M.; methodology, Z.H. and S.M.; validation, S.M., L.W., and P.P.; writing--original draft preparation, Z.H.; writing--review and editing, S.M.; supervision, L.W.; project administration, L.W.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No 52104170), the National Key Research and Development Program of China under grant 2019 YFC0605304, the Postgraduate Scientific Research Innovation Project of Hunan Province under grant CX20200113, and the Fundamental Research Funds for the Central Universities of Central South University under grant 2020 zzts192.  
Not applicable.  
All seismic data used in this study are uploaded in Figshare, the DOI is 10.6084/m9.figshare.19426145.  
We are grateful for the technical support from the High Performance Computing Center of Central South University.  
The authors declare no conflict of interest.