# Research on Seismic Signal Analysis Based on Machine Learning
## 1 Introduction  
In addition to natural earthquakes, the seismic sensing signals observed by the seismic observation also includes waveform information of artificial blasts, collapses, and other events (collectively referred to as non-natural earthquakes). If not handled properly, these waveform records mixed with unnatural seismic events can affect the research work of seismology [1]. The inclusion of artificial blast information in the regional earthquake catalogue leads to inaccurate seismic risk assessment [2]. Furthermore, the blast source is shallow and mainly occurs in the personnel activity area, so it has the characteristics of high intensity, which has a significant impact on the local production and life of local people [3]. In the recording of seismic sensing signals, non-natural seismic events such as blasts and collapses have certain commonalities with natural earthquakes. Especially in recent years, the emergence of various combined blast and mining area collapse events including multiple small collapses has made the waveforms recorded by seismometers extremely complex. It is difficult for seismic analysts to discriminate the specific types of seismic events only through the intuitive characteristics of signals exhibited in waveforms. Analysts mayhave different judgments about the types of given seismic event, which makes it difficult to identify non-natural seismic events such as blasts and collapses in a timely and effective manner [4]. Since the 1950s, the identification of natural earthquakes and artificial blast events has been widely and deeply studied. A variety of identification criteria have been proposed, including body wave magnitude: surface wave magnitude (mb:Ms) [5; 6; 7], complexity [8], cepstrum analysis [9], maximum ratio of S to P waves (S/P) [10; 11; 12; 13], time-frequency analysis [14], the criteria extracted from the frequency domain, etc. [15]. These single eigenvectors are usually used alone at first. However, when the blast equivalent is small, the recognition effect is not good. What is more, the feature extraction methods used in these studies have been complicated, and developing an analytical solution of the overall relationship of these features proved to be difficult [16]. It is worth noting that seismic waveform data carry all the source information of seismic sensing signals and can be directly used to classify different types of earthquakes [17]. With the improvement of computer hardware level, a large amount of research on earthquake classification based on machine learning methods directly using a large number of waveform data has sprung up in the field of seismology. In recent years, however, as a multilayer neural network learning algorithm, machine learning has been widely used in the field of seismology because of its powerful ability to automatically extract features from input sample data [18; 19; 20; 21]. In fact, the application of machine learning based methods in other fields can also provide us with reference, so that we can better carry out seismic signal research. EvoGAN extracts expression features through evolutionary algorithm(EA), and then inputs the features into GAN to accurately learn expression information [22]. GHNN based on graph signal processing theory uses two-channel filter bank to construct convolutional layers to process the characteristics of graph signals, and achieves better performances on the task of semi-supervised node classification [23]. In the practical application of natural earthquake and artificial blast classification and recognition, machine learning organically integrates the functions of feature extraction, feature selection, and feature classification and realizes the overall optimization of performance and efficiency. A convolutional neural network in a machine learning model effectively overcomes the problem of overfitting and has low training difficulty, so it has been widely applied in the field of seismic event research in recent years [18; 19; 20; 21].