首页 | 本学科首页   官方微博 | 高级检索  
     检索      

Reliability Evaluation on Weak Signal Extraction for Airgun Source Surveys
作者姓名:Wu Anxu  Ye Beng
作者单位:Beijing Earthquake Agency, Beijing 100080, China,Western Yunnan Earthquake Prediction Study Area, CEA, Dali 671000, Yunnan, China
基金项目:This project was sponsored by the Spark Program of Earthquake Science and Technology, CEA (XH16003) and the National Natural Science Foundation (NNSF) of China under Grant No.41474087.
摘    要:Based on the data synthesis simulation and the actual processing of the airgun seismic source signal, three quantitative indicators of signal-to-noise ratio, waveform correlation coefficient and phase offset, are superimposed. We systematically evaluate the functions of the following three stack methods including linearity, phase weighting and S-transform in the extraction of weak signals under strong background noise and quantitatively estimate the reliability of the stack results. Through the comprehensive discussion of the above three methods of stack results, the preliminary comparative analysis believes that the linear stack signal-to-noise ratio is low, but the waveform distortion is minimal; the phase-weighted superimposed signal-to-noise ratio is high and the phase offset is small, but the results of the waveform quality and linear stack are larger than the deviation; the S-transform stack has a relatively higher signal-to-noise ratio and a small loss of waveform amplitude, but there is a certain phase shift phenomenon. It is therefore suggested that linear stack technology should be used when the requirements of both waveform quality and time precision are high. However, the selection of the stack method when the airgun source excitation is limited should be emphasized. If high fidelity is required, the S-transform stack method should be selected; if the required time is high, accuracy can be selected by phase-weighted stack method to achieve reasonable extraction of weak signals.

关 键 词:Airgun  source  Weak  signal  extraction  Linear  stack  Phase-weighted  stack  S-transform  stack
收稿时间:2016/4/5 0:00:00
修稿时间:2016/5/23 0:00:00

Reliability Evaluation on Weak Signal Extraction for Airgun Source Surveys
Wu Anxu,Ye Beng.Reliability Evaluation on Weak Signal Extraction for Airgun Source Surveys[J].Earthquake Research in China,2018,32(2):265-279.
Authors:Wu Anxu and Ye Beng
Abstract:Based on the data synthesis simulation and the actual processing of the airgun seismic source signal,three quantitative indicators of signal-to-noise ratio,waveform correlation coefficient and phase offset,are superimposed. We systematically evaluate the functions of the following three stack methods including linearity,phase weighting and S-transform in the extraction of weak signals under strong background noise and quantitatively estimate the reliability of the stack results. Through the comprehensive discussion of the above three methods of stack results,the preliminary comparative analysis believes that the linear stack signal-to-noise ratio is low,but the waveform distortion is minimal; the phase-weighted superimposed signal-to-noise ratio is high and the phase offset is small,but the results of the waveform quality and linear stack are larger than the deviation; the S-transform stack has a relatively higher signal-to-noise ratio and a small loss of waveform amplitude,but there is a certain phase shift phenomenon. It is therefore suggested that linear stack technology should be used when the requirements of both waveform quality and time precision are high. However,the selection of the stack method when the airgun source excitation is limited should be emphasized. If high fidelity is required, the S-transform stack method should be selected; if the required time is high,accuracy can be selected by phase-weighted stack method to achieve reasonable extraction of weak signals.
Keywords:Airgun source  Weak signal extraction  Linear stack  Phase-weighted stack  S-transform stack
本文献已被 CNKI 等数据库收录!
点击此处可从《中国地震研究》浏览原始摘要信息
点击此处可从《中国地震研究》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号