## 3 Introduction to Algorithms  
### Selection of Characteristics  
Regarding the extraction of the characteristics of seismic signals (such as geometric, kinematic, kinetic, and statistical characteristics), the characteristics or characteristic combinations of seismic signals that reflect the essential characteristics of such signals and are independent from each other are preferentially selected by using human experience or mathematical methods so as to improve the effect of processing and interpretation methods related to seismic signal [18]. At present, there are many selectable characteristics of seismic signals. The direct use of all these characteristics for signal analysis and related calculations will inevitably cause a larger amount of calculation work. Therefore, it is necessary to select the characteristics that can reflect the essential features of seismic signals and are independent from each other.  
A single characteristic index after processing by the GA-SVM model may cause the incorrect identification of components that do not contain effective signals. Several different signal features can reflect differences between effective components and noise components in varied aspects and manifest abundant information of signal components. So, the introduction of a variety of signal features into the model for identification may ensure the accuracy of identification [19]. In this paper, the attributes such as average energy, energy ratio, dominant frequency, skewness, kurtosis, and margin are chosen as the characteristics for the identification of effective IMFs.  
1. Average energy: the average value of energy. The energy used in this paper refers to the average value of energy from the amplitude of seismic signals. The average energy of noise is generally lower, while the average energy of effective signals is relatively higher. This can distinguish between the two to some extent.  
2. Maximum energy ratio: the maximum value of the signal energy ratio within two adjacent time windows. Effective signals will experience a significant change in energy within the time window with the arrival of seismic waves, while the overall energy of noise will not show much change throughout the signal duration.
3. Dominant frequency: the frequency of dominant waves in the signals. Most noises have a higher frequency than effective signals. So, the features of the dominant frequency can help recognize effective signals from the perspective of the frequency domain.
4. Skewness and kurtosis: skewness and kurtosis are dimensionless parameters used to make comparisons with normal distribution curves and reflect the degree of deviation of the signal probability distribution compared with the normal distribution. The third-order central moment of vibration signals is used to calculate the skewness, and the fourth-order central moment is used to calculate the kurtosis. The skewness and kurtosis of effective signals are generally larger than those of noise signals.  
The skewness is defined as follows:  
\[Skew(X)=E\bigg{[}\bigg{(}\frac{X-\mu}{\sigma}\bigg{)}^{3}\bigg{]} \tag{8}\]  
The kurtosis is defined as follows:  
\[Kurt(X)=E\bigg{[}\bigg{(}\frac{X-\mu}{\sigma}\bigg{)}^{4}\bigg{]} \tag{9}\]  
where \(\mu\) refers to the mean value of a seismic signal, and \(\sigma\) is the standard deviation of the signal.
5. Margin: the ratio of the peak value of a signal to the RMS amplitude, which can help detect the presence or absence of shocks in signals. The margin will increase significantly when effective waves appear [20].