{"title":"离散小波变换作为一种自动相位采集工具","authors":"P. Ooninex","doi":"10.1109/TFSA.1998.721396","DOIUrl":null,"url":null,"abstract":"Seismic data consist of traces, which contain information about a seismic event, but in some period of time may virtually be noise. A trace, which contains seismic information, is called a seismic signal. Seismic signals consist of several typically short energy bursts, called phases, exhibiting several patterns in terms of dominant frequency, amplitude and polarization. Amongst others, significant phases are the P-phase and the S-phase. We present a fast algorithm to detect the S-phase in a seismic signal. In this method we use a combination of traditional S-phase detection methods from seismology and the discrete wavelet transform. First results are presented to demonstrate our new approach.","PeriodicalId":395542,"journal":{"name":"Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The discrete wavelet transform as a tool for automatic phase pickers\",\"authors\":\"P. Ooninex\",\"doi\":\"10.1109/TFSA.1998.721396\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Seismic data consist of traces, which contain information about a seismic event, but in some period of time may virtually be noise. A trace, which contains seismic information, is called a seismic signal. Seismic signals consist of several typically short energy bursts, called phases, exhibiting several patterns in terms of dominant frequency, amplitude and polarization. Amongst others, significant phases are the P-phase and the S-phase. We present a fast algorithm to detect the S-phase in a seismic signal. In this method we use a combination of traditional S-phase detection methods from seismology and the discrete wavelet transform. First results are presented to demonstrate our new approach.\",\"PeriodicalId\":395542,\"journal\":{\"name\":\"Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TFSA.1998.721396\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TFSA.1998.721396","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The discrete wavelet transform as a tool for automatic phase pickers
Seismic data consist of traces, which contain information about a seismic event, but in some period of time may virtually be noise. A trace, which contains seismic information, is called a seismic signal. Seismic signals consist of several typically short energy bursts, called phases, exhibiting several patterns in terms of dominant frequency, amplitude and polarization. Amongst others, significant phases are the P-phase and the S-phase. We present a fast algorithm to detect the S-phase in a seismic signal. In this method we use a combination of traditional S-phase detection methods from seismology and the discrete wavelet transform. First results are presented to demonstrate our new approach.