Abstract
The purpose of seismic data processing in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from complex ambient noises. Accurate detection and identification of seismic phases are of great significance to the detection and parameter estimation of seismic events. In seismic phase identification, discriminating between noise signals and real seismic signals is essential. Accurate identification of noise signals helps reduce false detections, improves the accuracy of automatic bulletins, and relieves the workload of analysts. At the same time, in seismic exploration, the prime objective in data processing is also to enhance the signal and suppress the noises. In this study, we combined a generative adversarial network (GAN) with a long short-term memory network (LSTM) to discriminate between noise and phases in seismic waveforms recorded by the International Monitoring System (IMS) array MKAR. First, using the beamforming data of the array as the input, we obtained the signal features of seismic phases through the learning of the GAN discriminator network. Then, we input these features and trained the joint network on mixed seismic phase and noise data, and successfully classified seismic phases and noise signals with a recall of 95.28% and 97.64%, respectively. Based on this model, we established a real-time data processing method, then validated the effectiveness of this method with real 2019 data of MKAR. We also verified whether improved noise signal identification improves the quality of phase association and event detection.
Highlights
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
We can see that all classification results are correct, demonstrating the ability to identify seismic phases and noise signals accurately and to give the arrival times of seismic phases accurately
We used the generative adversarial network (GAN) discriminator network to learn the features of seismic phases, input these features into the long short-term memory network (LSTM) network and, by training the joint network, successfully identified and classified seismic phases and noise signals
Summary
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. 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 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 IDC’s seismic data processing includes station processing and network association processing [3]
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