Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li
{"title":"基于稀疏自动编码器和卡尔曼滤波器的地表微震数据去噪","authors":"Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li","doi":"10.1080/21642583.2022.2087786","DOIUrl":null,"url":null,"abstract":"Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Surface microseismic data denoising based on sparse autoencoder and Kalman filter\",\"authors\":\"Xuegui Li, Shuo Feng, Nan Hou, Ruyi Wang, Hanyang Li, Ming Gao, Siyuan Li\",\"doi\":\"10.1080/21642583.2022.2087786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2022-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2022.2087786\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2087786","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Surface microseismic data denoising based on sparse autoencoder and Kalman filter
Microseismic technology is widely used in unconventional oil and gas production. Microseismic noise reduction is of great significance for the identification of microseismic events, the location of seismic sources and the improvement of unconventional oil and gas production. In this paper, a denoising filter is proposed based on sparse autoencoder and Kalman filtering. Firstly, a sparse autoencoder is pre-trained to learn the feature of the microseismic data. Sparse autoencoding is a back-propagation neural network algorithm based on unsupervised learning, in which there are three layers: the input layer, the hidden layer and the output layer. The hidden layer is the spare, which makes the algorithm learn features better, represents samples in harsh environments and reduces dimensionality effectively. Besides, Kalman filter is used to deal with the uncertainty factors. Using a dataset of 600 surface microseismic synthesis traces and simulation noise. Sparse autoencoders and Kalman filtering are trained to suppress noise. The denoising filter based on sparse autoencoder and Kalman filter model obtains a higher signal noise ratio than the conventional model. The experiment results for the filtering of surface microseismic signals show the feasibility and effectiveness of the proposed method.
期刊介绍:
Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory