{"title":"基于haar小波变换的噪声环境下地震检测及到达时间估计","authors":"I. Thanasopoulos, J. Avaritsiotis","doi":"10.1109/SAM.2008.4606906","DOIUrl":null,"url":null,"abstract":"In this paper we propose a discrete wavelet transform (DWT) method for event detection and estimation of the time of arrival (TOA) of seismic signals in a sensor network. The Haar wavelet is selected for its low computational complexity and its good locality in time domain which is essential for the analysis of transient signals. The proposed method requires no a priori knowledge about the spectral characteristics of the signals, because the algorithm defines the optimum scales for the extraction of information by time-domain features as the signal is acquired. The performance of the algorithm is verified using a computer program that simulates the propagation of surface seismic waves. Simulation results corroborate the suitability of the proposed method for source localization applications.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Seismic detection and time of arrival estimation in noisy environments based on the haar wavelet transform\",\"authors\":\"I. Thanasopoulos, J. Avaritsiotis\",\"doi\":\"10.1109/SAM.2008.4606906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a discrete wavelet transform (DWT) method for event detection and estimation of the time of arrival (TOA) of seismic signals in a sensor network. The Haar wavelet is selected for its low computational complexity and its good locality in time domain which is essential for the analysis of transient signals. The proposed method requires no a priori knowledge about the spectral characteristics of the signals, because the algorithm defines the optimum scales for the extraction of information by time-domain features as the signal is acquired. The performance of the algorithm is verified using a computer program that simulates the propagation of surface seismic waves. Simulation results corroborate the suitability of the proposed method for source localization applications.\",\"PeriodicalId\":422747,\"journal\":{\"name\":\"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAM.2008.4606906\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM.2008.4606906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Seismic detection and time of arrival estimation in noisy environments based on the haar wavelet transform
In this paper we propose a discrete wavelet transform (DWT) method for event detection and estimation of the time of arrival (TOA) of seismic signals in a sensor network. The Haar wavelet is selected for its low computational complexity and its good locality in time domain which is essential for the analysis of transient signals. The proposed method requires no a priori knowledge about the spectral characteristics of the signals, because the algorithm defines the optimum scales for the extraction of information by time-domain features as the signal is acquired. The performance of the algorithm is verified using a computer program that simulates the propagation of surface seismic waves. Simulation results corroborate the suitability of the proposed method for source localization applications.