# A Novel Method of Seismic Signal Detection Using Waveform Features
## 1 Introduction
However, the generality of this method is limited by the dependence on source assumptions, and this method requires a huge number of historical earthquakes to form a template library. It will not be applied to some areas where earthquakes have never happened [20; 22; 23; 24]. Machine learning (ML) and neural network have been widely used in seismology, especially for detecting and identifying P phase, S phase, and noise. With the rapid growth of seismic data, machine learning and deep learning plays a significant role in processing vast amounts of seismic data [25; 26; 27; 28]. However, these algorithms often require a lot of work to deal with massive data in the previous stage to prepare data sets, and the precision of classification depends on the trained model [29; 30; 31]. In this paper, we propose a novel method based on location information and waveform features to detect the seismic signal. A signal model combining multiple features was built. The waveform features are the spectral energy average about the specific frequency band of the signal, the energy of the signal component decomposing by empirical mode decomposition, the peak and the ratio of STA/LTA trace. The Gaussian Process(GP) was used to build the model distribution. GP [32; 33; 34; 35] is a popular method for Bayesian non-linear non-parametric regression and classification in machine learning. It is a collection of real-valued functions and any finite number of random variables have a joint Gaussian distribution. Three years of CTBTO International Monitoring System (IMS) KSRS array data were used to train the model. Seismic signal detection is constituted of four main steps in this paper. Firstly, data preparation is an essential step in the whole process, and it is used for selecting some observed seismic events for researching. Secondly, the feature extraction step obtains a lot of valuable information on original signals. By analyzing the signal, we choose some helpful features in the seismic signal detection, such as the peak and ratio of the STA/LTA trace, the average value of energy in a specific frequency band, and the energy value after empirical mode decomposition (EMD), which are independent of each other and introduced in detail later. Thirdly, the single-feature model is constructed by location and waveform information. Each feature obeys the Gaussian distribution generated from GP. Lastly, we calculate the posterior probability of the joint features to predict the possibility that new signal is a seismic signal. During the process, the optimal hyperparameters of GP are obtained by maximizing the marginal likelihood function \(L(\theta)\)[32]. In summary, the innovations and contributions of this work have several points:  
* Focusing on a specific region, waveform features that are suitable for seismic signal detection are chosen and extracted, such as the envelop features of STA/LTA trace and frequency-domain energy features of the signal. * The Gaussian process with event location information in a specific region as a kernel function is used to build the signal features model. The seismic detection is achieved using joint features probability model. In the following sections, first of all, a brief introduction of GP is introduced. Next, waveform features used in this paper are described in detail, and a joint multi-features probability model is constructed. Then the implementation of seismic signal detection method proposed in this paper is elaborated, including data preparation, feature extraction, model training, and etc. Finally, we analyze and discuss the results.