GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition

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...

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Autores principales:	Jian Li, Dongwei Hei, Gaofeng Cui, Mengmin He, Juan Wang, Zhehan Liu, Jie Shang, Xiaoming Wang, Weidong Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GAN LSTM seismic array noise signal recognition Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/86d5f9d9cec54eb193d519007c563a0d
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id	oai:doaj.org-article:86d5f9d9cec54eb193d519007c563a0d
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spelling	oai:doaj.org-article:86d5f9d9cec54eb193d519007c563a0d2021-11-11T15:04:25ZGAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition10.3390/app112199872076-3417https://doaj.org/article/86d5f9d9cec54eb193d519007c563a0d2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9987https://doaj.org/toc/2076-3417The 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.Jian LiDongwei HeiGaofeng CuiMengmin HeJuan WangZhehan LiuJie ShangXiaoming WangWeidong WangMDPI AGarticleGANLSTMseismic arraynoise signal recognitionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9987, p 9987 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GAN LSTM seismic array noise signal recognition Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	GAN LSTM seismic array noise signal recognition Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jian Li Dongwei Hei Gaofeng Cui Mengmin He Juan Wang Zhehan Liu Jie Shang Xiaoming Wang Weidong Wang GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
description	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.
format	article
author	Jian Li Dongwei Hei Gaofeng Cui Mengmin He Juan Wang Zhehan Liu Jie Shang Xiaoming Wang Weidong Wang
author_facet	Jian Li Dongwei Hei Gaofeng Cui Mengmin He Juan Wang Zhehan Liu Jie Shang Xiaoming Wang Weidong Wang
author_sort	Jian Li
title	GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
title_short	GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
title_full	GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
title_fullStr	GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
title_full_unstemmed	GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition
title_sort	gan-lstm joint network applied to seismic array noise signal recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/86d5f9d9cec54eb193d519007c563a0d
work_keys_str_mv	AT jianli ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT dongweihei ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT gaofengcui ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT mengminhe ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT juanwang ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT zhehanliu ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT jieshang ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT xiaomingwang ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition AT weidongwang ganlstmjointnetworkappliedtoseismicarraynoisesignalrecognition
_version_	1718437154252652544

GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition

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