## 2 Data Preprocessing
### Selecting Features  
We applied audio feature extraction methods to this work, treating the seismic signal as audio. We combined the mel frequency cepstral coefficients (MFCCs) and the mel spectrogram of the audio signals to extract features from the denoised signal, and selected some of the most important features, as shown in Figure 3, for the model [39; 40]. MFCC, which stands for mel frequency cepstral coefficient, is a feature representation method for audio signals mainly used in audio processing fields, such as audio signal classification, speech recognition, and music information retrieval [41; 42; 43]. The MFCC was originally designed to simulate the human perception of sound. Our ears are not equally sensitive to loudness--they are more sensitive to signals with higher frequencies than those with lower  
Figure 2: Comparison of denoised seismic signal data. The data used in this figure come from the test dataset, with the left column (**a**,**c**,**e**) showing the original signal in lighter colors and the right column (**b**,**d**,**f**) showing the signal data after wavelet denoising. The denoised seismic signal is smoother compared to the original signal, with significantly reduced noise amplitude and more prominent signal peaks. This makes the signal features more obvious and it is easier to observe the trend and characteristics of the signal.  
frequencies. To simulate this non-linear frequency response, the MFCC uses a set of mel filters to simulate the frequency response characteristics of the human ear. Mel filters can filter audio signals in the frequency domain, allowing for more detailed characterization of frequency details in the low frequency range and coarser characterization of frequency details in the high frequency range. The calculation method of a mel spectrogram is as follows: preprocess the original audio signal, including removing DC bias, framing, and windowing; perform fast Fourier transform (FFT) on each time window to obtain the frequency spectrum of the signal; map the frequency spectrum to the mel scale, which is a non-linear frequency scale related to human auditory characteristics [44; 45]--this process can be achieved by applying a linear transformation and a set of triangular filters; calculate the energy output of each triangular filter on the mel scale, which can weight the energy in different frequency ranges and merge them into one value--these energy values form one row of the mel spectrogram, with each row representing a time window; convert the values on the mel spectrogram from a linear scale to a logarithmic scale by taking the logarithm. Since the frequency range of seismic signals is wide, the mel frequency scale can more finely characterize the high-frequency part of the spectrum and compress the low-frequency part, thereby better exhibiting the characteristics of the seismic signals. Therefore, by observing the mel spectrogram, we can better understand the frequency spectrum characteristics of the seismic signals and be helped to classify and recognize the seismic signals, as shown in Figure 4.  
Figure 3: Comparison of the average value of the mel spectral coefficient on Lightgbm model importance. Adding several features with high importance to the model training greatly improved the score. It should be noted here that the selection of other features also applies this method to the extraction of features with high model importance, such as the number of peaks of at least support 2 on the denoised signal and above the 20% percentile on the std of the rolling window of size 50.