1 / 5

The Importance of Prefiltering: Managing Biased Estimates in Seismic Noise Analysis

Learn about the impacts of prefiltering on biased estimates in seismic noise analysis and how to manage them effectively. Explore techniques to enhance coherence estimation and analyze horizontal components recorded at UCB. Visualize data with R programming to improve understanding and make informed decisions.

harlow
Download Presentation

The Importance of Prefiltering: Managing Biased Estimates in Seismic Noise Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The importance of prefiltering. The estimates are generally biased E{ fT()} =  WT (-) f() d Seismic noise horizontal components recorded at UCB Estimated coherence can be 1,0 when no relation Estimated coherence can be 0.0 when linearly time invariantly related

  2. postscript(file="seismic.eps") junkx<-scan("ts_drb.16.dat") junky<-scan("ts_drb.17.dat") par(mfrow=c(2,1)) xaxis<-c(1:2500)*.08 plot(xaxis,junkx,type="l",main="Vertical component seismic noise at Berkeley",xlab="time (sec)",ylab="",las=1) plot(xaxis,junky,type="l",main="West component",xlab="time(sec)",ylab="",las=1) par(mfrow=c(2,3)) junk<-spec.pgram(cbind(junkx,junky),spans=15,taper=0,detrend=F,demean=T,plot=F) junk$freq<-junk$freq/.08 plot(junk$freq,junk$spec[,1],type="l",las=1,main="Vertical noise",xlab="frequency (hertz)",log="y") plot(junk$freq,junk$spec[,2],type="l",las=1,main="West noise",log="y") plot(junk$freq,junk$coh,type="l",ylim=c(0,1),main="Raw data",las=1) abline(h=1-(1-.95)^(1/(.5*(junk$df-2))))

  3. junkxx<-ar(junkx,order.max=2) junkyy<-ar(junky,order.max=2) Junkx<-junkxx$resid Junky<-junkyy$resid Junkx<-Junkx[3:length(Junkx)];Junky<-Junky[3:length(Junky)] Junk<-spec.pgram(cbind(Junkx,Junky),spans=15,taper=0,detrend=F,demean=T,plot=F) Junk$freq<-Junk$freq/.08 plot(Junk$freq,Junk$spec[,1],type="l",las=1,log="y") plot(Junk$freq,Junk$spec[,2],type="l",las=1,log="y") plot(Junk$freq,Junk$coh,type="l",ylim=c(0,1),main="AR(2) residuals",las=1) abline(h=1-(1-.95)^(1/(.5*(junk$df-2)))) graphics.off()

More Related