# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 1 Introduction  
Seismic signals are the most important data source for humans to be able to understand seismic activity. However, the seismic signal is a non-linear, non-stationary, and noisy signal. If seismologists want to obtain more information from the seismic signal, they must process it, such as through event detection, phase picking, and noise filtering [1]. The proposal of wavelet transform (or a more nuanced approach called wavelet packet transform) gives a better solution and is extensively applied in seismic signal processing [2; 3]. Nevertheless, the selection of wavelets has become a new research problem. In the past few decades, wavelets have already been a powerful tool used for many applications [4]. In their application for the processing of data, wavelets can be used for signal processing, data compression, and data smoothing and denoising. [PERSON] et al. [5] proposed the improved empirical wavelet transform (EWT) to process vibration signals and to realize fault diagnosis in motor bearings. [PERSON] et al. [6] proposed a near-lossless compression method for remote sensing data utilizing regression wavelet analysis (RWA). [PERSON] et al. [7] combined a wavelet with deep learning to reconstruct and denoise remote sensing images. In bioinformatic analysis, wavelets are widely used in fingerprint verification, biology for cell membrane recognition, protein analysis, electrocardiogram analysis, and so on. [PERSON] et al. [8] developed an algorithm using a circular Gabor wavelet to enhance fingerprint verification. [PERSON] et al. [9] realized subcellular localization classification based on wavelet decomposition. [PERSON] et al. [10] predicted protein-protein interactions using the wavelet denoising approach. [PERSON] et al. [11] utilized thepattern-adapted wavelet technique for R-peak identification in electrocardiogram signal analysis. It seems that wavelet has nothing to do with finance, but in the financial field, wavelet has also been widely used. For example, [PERSON] [12] used wavelet correlation analysis to study the relationship between coronavirus (COVID-19) and economic uncertainty. Wavelets are also widely used in computer science, such as for Internet traffic description, speech recognition, computer graphic and multifractal analysis, etc. [PERSON] et al. [13] combined wavelet transform and a convolutional neural network for content recognition of Internet traffic. Wavelet is also used to denoise speech during speech recognition [14]. Wavelets and multifractal systems have also been widely used in recent years. For example, [PERSON] et al. [15] applied them in retinal vessel segmentation, [PERSON] et al. [16] applied them in geomagnetically induced currents, and [PERSON] et al. [17] applied them in the detection of geochemical anomalies. There are many similar applications of wavelets in various fields. Wavelet transform and wavelet packet transform have been extensively applied in various fields [18; 19; 20; 21; 22]. Researchers have proposed many wavelet selection strategies in the wavelet transform process [23]. [PERSON] [24] proposed a measurement method using the statistical theory: energy-Shannon entropy ratio. [PERSON] et al. [25] presented a scale-dependent wavelet selection strategy based on the energy of the signal. This approach generated minor waveform distortion and denoised the partial discharge signal magnitude errors. [PERSON] et al. [26] selected the appropriate wavelet to process the electrocardiogram by comparing the efficiency of the compression algorithm using different wavelets. [PERSON] [27] made the final selection by calculating the minimum mean square error (MSE) between the original and constructed signals. [PERSON] et al. [28] utilized two quantitative indicators: the classic peak signal-to-noise ratio and the edge preservation of the filtered image to evaluate each wavelet, and they used this method to process real high-quality ultrasound images to verify reliability. [PERSON] et al.