# GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
## 1 Introduction  
Seismic monitoring, as one of the verifications means identified by the \"Comprehensive Test Ban Treaty\", is widely applied in the event location, nature discrimination, and yield estimation in nuclear test monitoring due to its sensitivity. The International Data Center (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) collects and processes International Monitoring System (IMS) data in real time and generates a series of seismic events bulletins within the term specified by the treaty [1]. For IMS station data, the IDC first generates a series of standard event lists (SELs) through automatic processing, which is then reviewed and corrected by analysts to generate a reviewed event bulletin (REB). The purpose of seismic data monitoring in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from ambient noises. This processing mainly involves detection, association, location, discrimination, and magnitude/yield estimation [2]. The IMS of CTBTO includes 50 primary seismic stations and 120 auxiliary seismic stations, most of which record seismic signals by the seismic array. The IDC's seismic data processing includes station processing and network association processing [3]. The former aims at data space-time filtering, signal detection, feature extraction, and seismic phase identification. Beamforming and filtering are performed to improve the signal-to-noise ratio (SNR) of array signals. Beamforming [4; 5] can improve the signal-to-noise ratio (SNR) and the detection performance by delaying and summing the records of subsites of the seismic array using signal correlation and simultaneously eliminate the effects of non-coherent noise. The short-term average/long-term average (STA/LTA) method capable of reflecting instantaneous array signal energy variations is used to detect seismic signals [6; 7]. Frequency-wavenumber (f-k) analysis is applied to estimate the array slowness vector [8]. Polarization analysis is applied to estimate the azimuth and slowness of three-component stations [9]. The Autoregressive Akaike information criterion (AR-AIC) method is applied to accurately estimate the arrival time of seismic phases [10; 11]. A method combining rules and a traditional neural network is used to identify seismic phases [3]. Accurate detection and recognition of seismic phases are of great significance to the detection and parameter estimation of final seismic events. In seismic phase recognition, discriminating between noise signals and real seismic signals is an essential step. High false detection rate, as one of the major problems facing the IDC's automatic processing results [12], is mainly attributable to the inaccurate picking and recognition of seismic phases resulting, for example, from the interference of noise signals with real seismic phases. In seismic exploration, although there are different data processing sequences, the prime objective in data processing is still to enhance the signal and suppress the noises [13]. When recording seismic signals, seismic stations also record various noise signals, including instrument noises, man-made noises, wind noises, marine noises, and other noise sources, some being very similar to seismic signals [14]. A list of specific seismic features is extracted from the detected signals. Based on these features, the seismic and noise signals are classified using a rule-based expert system or the traditional BP (backpropagation) neural network algorithm [3]. The rule-based expert system uses signal frequency, horizontal and vertical amplitude ratios, polarization features, and, for arrays, the horizontal slowness, f-k quality, and F-score distributions. It may also use a cascade binary-class classification BP neural network to classify signals according to the above features. It first distinguishes seismic phases and noise, then further differentiates between teleseismic and regional phases, and finally identifies the regional P or S waves.