# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 1 Introduction
From 2018 to 2019, 4.5 million noise signals were identified by the IDC automatic processing system, accounting for 54% of all detected signals. In the interactive analysis, there are many arrivals detected at stations that do not associate to the interactive analysis bulletin LEB. Among these arrivals, most of them are noise signals, partly were corrected as \"N\" signals by analysts. This shows that there are still large numbers of noise signals erroneously identified as seismic phase signals in IDC's automatic processing. Accurate removal of all noise signals is an urgent problem to solve to improve the reliability of IDC's automatic bulletins. In recent years, machine learning, including deep learning, has been widely applied across the fields of computer vision, speech recognition, and natural language processing. The method of deep neural learning was used by many researchers to develop new techniques for seismic phase picking and event detection. For example, [PERSON] used a convolutional neural network (CNN) to detect and locate seismic events in Oklahoma and detected more events than previously cataloged [15]. [PERSON] trained CNNs on picked network data for the Southern California region and proved that this method could estimate P-wave arrival times and first-motion polarities more accurately [16]. [PERSON] detected P and S phases and noises with U-net and estimated arrival times with probability distribution outputs [17]. [PERSON] detected seismic signals by combining CNN with recurrent neural network (RNN) and proved better performance than STA/LTA, template matching, or FAST [18]. [PERSON] recognized noise signals and P-wave signals in earthquake early warning systems using GAN and random forest (RF) processes. Their methods can effectively reduce false system alarms [19]. [PERSON] compared the performances of five machine learning methods in recognizing seismic phases from noise signals in earthquake early warning systemsand showed that their complex deep neural network model based on raw data learning could strongly improve the performance of noise signal recognition [20]. [PERSON] designed a cascaded convolutional neural network capable of detecting different scales of seismic events [21]. [PERSON] designed a deep convolutional network to recognize volcano-seismic events [22]. [PERSON] applied CNN and U-net to the detection and recognition of Wenchuan earthquake aftershocks and the seismic networks in the capital circle [23; 24]. [PERSON] applied CNN to locate induced seismicity in Oklahoma [25]. For the global IMS network, [PERSON] proposed a network association method called NET-VISA based on machine learning to achieve the station's phases association and events detection. NET-VISA mainly uses a probabilistic model and the Bayesian method to find the best set of events explaining the parametric detection observations [26; 27; 28]. Noise signal discrimination is a binary classification problem. Deep learning is suitable for solving such classification problems. To execute this, we first need to extract the features from waveform data, then classify the seismic phases and noise signals based on these features. Traditional feature extraction methods require complicated feature engineering. Deep learning provides another solution avoiding feature engineering and has shown good performance on classification problems [29]. At the same time, because beamforming can improve the SNR of seismic array records, we use beamforming data of seismic array as the input to train and test the deep learning network to improve the performance for weak signals further. In this study, we present a noise discrimination method suitable for array data across the global IMS seismic networks, which combines a generative adversarial network (GAN) with a long short-term memory network (LSTM). First, using beamformed IMS array data as the input, we trained the GAN on a dataset comprised of real seismic P and S phases (Pn, Sn, Pg, Lg, P, PKP) data.