[cig-commits] r11569 - seismo/3D/automeasure/latex
alessia at geodynamics.org
alessia at geodynamics.org
Wed Mar 26 06:36:36 PDT 2008
Author: alessia
Date: 2008-03-26 06:36:36 -0700 (Wed, 26 Mar 2008)
New Revision: 11569
Modified:
seismo/3D/automeasure/latex/AM-allcitations.bib
seismo/3D/automeasure/latex/README
seismo/3D/automeasure/latex/abstract.tex
seismo/3D/automeasure/latex/discussion.tex
seismo/3D/automeasure/latex/flexwin_paper.pdf
seismo/3D/automeasure/latex/flexwin_paper.tex
Log:
Tweaked discussion and added abstract
Modified: seismo/3D/automeasure/latex/AM-allcitations.bib
===================================================================
--- seismo/3D/automeasure/latex/AM-allcitations.bib 2008-03-26 10:27:58 UTC (rev 11568)
+++ seismo/3D/automeasure/latex/AM-allcitations.bib 2008-03-26 13:36:36 UTC (rev 11569)
@@ -1,5 +1,5 @@
-%% Created for alessia at 2008-03-25 17:01:48 +0100
+%% Created for alessia at 2008-03-26 13:47:46 +0100
%% Saved with string encoding Occidental (ASCII)
@@ -95,9 +95,9 @@
@article{MegninRomanowicz1999,
- Author = {Megnin, C. and Romanowicz, B.},
+ Author = {M{\'e}gnin, C. and Romanowicz, B.},
Date-Added = {2008-03-25 17:00:27 +0100},
- Date-Modified = {2008-03-25 17:01:46 +0100},
+ Date-Modified = {2008-03-25 17:02:28 +0100},
Journal = {Geophys. J. Int.},
Pages = {366--380},
Title = {The effects of the theoretical formalism and data selection on mantle models derived from waveform tomography},
Modified: seismo/3D/automeasure/latex/README
===================================================================
--- seismo/3D/automeasure/latex/README 2008-03-26 10:27:58 UTC (rev 11568)
+++ seismo/3D/automeasure/latex/README 2008-03-26 13:36:36 UTC (rev 11569)
@@ -1,3 +1,24 @@
+-------------
+Alessia, 16-March-2008
+
+TODOs:
+
+* Alessia:
+ + write concluding paragraph to introduction
+ + add envelopes to Figure 1
+ + write concluding paragraph to discussion
+
+* Min+Carl:
+ + please add your time-dependent functions to appendix A
+ + what would you like to see in the discussion section?
+
+* Carl:
+ + please check the results section to make sure I have not changed your meaning too much with my edits and re-arrangement of figures
+
+
+------------
+
+
Alessia Maggi, 24-march-2008
TODOs:
Modified: seismo/3D/automeasure/latex/abstract.tex
===================================================================
--- seismo/3D/automeasure/latex/abstract.tex 2008-03-26 10:27:58 UTC (rev 11568)
+++ seismo/3D/automeasure/latex/abstract.tex 2008-03-26 13:36:36 UTC (rev 11569)
@@ -1,2 +1,3 @@
\begin{abstract}
+We present an algorithm for the automated selection of measurement windows on pairs of observed and synthetic seismograms. The algorithm was designed specifically to automate window selection and measurement for adjoint tomography studies, but is sufficiently flexible to be adapted to most tomographic applications and seismological scenarios. Adjoint tomography requires a data selection method that maximizes the number of measurements made on each seismic record while avoiding seismic noise. The method must adapt to the features that exist in the seismograms themselves, because 3D wavefield simulations are able to synthesize phases that do not exist in 1D simulations or traditional travel-time curves. The method must also be automated in order to adapt to the changing synthetic seismograms after each iteration of the tomographic inversion. These considerations led us to favor a signal processing approach to the problem of data selection, and to the development of the FLEXWIN algorithm presented here.
\end{abstract}
Modified: seismo/3D/automeasure/latex/discussion.tex
===================================================================
--- seismo/3D/automeasure/latex/discussion.tex 2008-03-26 10:27:58 UTC (rev 11568)
+++ seismo/3D/automeasure/latex/discussion.tex 2008-03-26 13:36:36 UTC (rev 11569)
@@ -1,29 +1,37 @@
-\section{Discussion}
+\section{Discussion and Conclusion}
-The window selection algorithm algorithm we describe in this paper was designed to solve the problem of automatically picking windows for adjoint problems, specifically for 3D-3D tomography as described by \cite{TrompEtal2005} and \cite{TapeEtal2007}. The specificity of adjoint methods is to turn measurements of the differences between observed and synthetic waveforms into adjoint sources that are subsequently used to determine the sensitivity kernels of the measurements themselves to the Earth model parameters. The manner in which the adjoint source is created is specific to each type of measurement (e.g. waveform difference, cross-correlation time-lags, multi-taper phase and amplitude anomalies), but once formulated can be applied indifferently to any part of the seismogram. Adjoint methods have been used to calculate kernels of various body and surface-wave phases with respect to isotropic elastic parameters and interface depths \citep{LiuTromp2006}, but also with respect to anisotropic elastic parameters \cite{SieminskiEtal2007a,SieminskiEtal2007b}. Adjoint methods allow us to calculate kernels for each and every wiggle on a given seismic record, thereby giving us access to virtually all the information contained within.
+The window selection algorithm we describe in this paper was designed to solve the problem of automatically picking windows for adjoint problems, specifically for 3D-3D tomography as described by \cite{TrompEtal2005} and \cite{TapeEtal2007}. The specificity of adjoint methods is to turn measurements of the differences between observed and synthetic waveforms into adjoint sources that are subsequently used to determine the sensitivity kernels of the measurements themselves to the Earth model parameters. The manner in which the adjoint source is created is specific to each type of measurement (e.g. waveform difference, cross-correlation time-lags, multi-taper phase and amplitude anomalies), but once formulated can be applied indifferently to any part of the seismogram. Adjoint methods have been used to calculate kernels of various body and surface-wave phases with respect to isotropic elastic parameters and interface depths \citep{LiuTromp2006}, but also with respect to anisotropic elastic parameters \cite{SieminskiEtal2007a,SieminskiEtal2007b}. Adjoint methods allow us to calculate kernels for each and every wiggle on a given seismic record, thereby giving us access to virtually all the information contained within.
-It is becoming clear, as more and more finite-frequency tomography models are published, that better kernels on their own are not the answer to the problems of improving the resolution of tomographic stdies. \cite{TrampertSpetzler2006} and \cite{BoschiEtal2007} investigate the factors limiting the quality of finite-frequency tomography images, and conclude that incomplete and inhomogenous data coverage limit in practice the improvement in resolution that accurate finite-frequency kernels can provide. The current frustration with the data-induced limitations to the improvements in wave-propagation theory is well summarized by \cite{Romanowicz2008} The ability of adjoint methods to deal with all parts of the seismogram indifferently means we can incorporate a much greater amount of information from each seismogram into a tomographic problem, leading to a much improved data coverage.
+It is becoming clear, as more and more finite-frequency tomography models are published, that better kernels on their own are not the answer to the problems of improving the resolution of tomographic stdies. \cite{TrampertSpetzler2006} and \cite{BoschiEtal2007} investigate the factors limiting the quality of finite-frequency tomography images, and conclude that incomplete and inhomogenous data coverage limit in practice the improvement in resolution that accurate finite-frequency kernels can provide. The current frustration with the data-induced limitations to the improvements in wave-propagation theory is well summarized by \cite{Romanowicz2008}. The ability of adjoint methods to deal with all parts of the seismogram indifferently means we can incorporate a much greater amount of information from each seismogram into a tomographic problem, leading to a much improved data coverage.
-The computational cost of constructing an adjoint kernel is independent of the number of portions of each seismogram we choose to measure, and also of the number of records of a given event we choose to work with (REF). It is therefore to our advantage to make measurements on as many records as possible, while covering as much as possible of each record. There are, however, certain limits we must be aware of. As mentioned in the introduction, there is nothing in the adjoint method itself that prevents us from constructing a kernel from noise-dominated portions of the data. As the purpose of 3D-3D tomography is to improve the fine details of Earth models, it would be counterproductive to pollute the inversion process with such kernels.
+The computational cost of constructing an adjoint kernel is independent of the number of portions of each seismogram we choose to measure, and also of the number of records of a given event we choose to work with \citep{TapeEtal2007}. It is therefore to our advantage to make measurements on as many records as possible, while covering as much as possible of each record. There are, however, certain limits we must be aware of. As mentioned in the introduction, there is nothing in the adjoint method itself that prevents us from constructing a kernel from noise-dominated portions of the data. As the purpose of 3D-3D tomography is to improve the fine details of Earth models, it would be counterproductive to pollute the inversion process with such kernels.
-The use of adjoint methods for tomography requires a method of selecting and windowing seismograms that avoids seismic noise while at the same time extracting as much information as possible from the signals. The method must also be adaptable to the features that exist in the seismograms themselves, because 3D wavefield simulations are able to synthesize phases that do not exist in 1D simulations or traditional travel-time curves. These considerations led us to favour a signal processing approach to the problem of data selection, approach which in turn led to the developement of the FLEXWIN algorithm we present here.
+DO A BETTER JOB IN THIS PARAGRAPH DESCRIBING THE DATA SELECTION ISSUES SPECIFIC TO ADJOINT TOMOGRAPHY.
+The use of adjoint methods for tomography requires a method of selecting and windowing seismograms that avoids seismic noise while at the same time extracting as much information as possible from the signals. The method must be automated in order to adapt to the changing synthetic seismograms at each iteration of the tomographic inversion. The method must also be adaptable to the features that exist in the seismograms themselves, because 3D wavefield simulations are able to synthesize phases that do not exist in 1D simulations or traditional travel-time curves. These considerations led us to favor a signal processing approach to the problem of data selection, approach which in turn led to the development of the FLEXWIN algorithm we present here.
The FLEXWIN algorithm is independent of input model, geographic scale and frequency range. It is a configurable process that can be applied to different seismic scenarios simply by changing the parameters in Table~\ref{tb:params}. We have configured the algorithm separatelly for each of the tomographic scenarios presented in Section~\ref{sec:results}. The configuration process is data-driven: starting from the description of how each parameter influences the window selection (Section~\ref{sec:algorithm}), the user tunes the parameters using a representative subset of the full dataset until the algorithm produces an adequate set of windows, then applies the tuned algorithm to the full dataset. The choice of what makes an adequate set of windows remains subjective, as it depends strongly on the quality of the input model, the quality of the data, and the region of the Earth the tomographic inversion aims to constrain. We consider the algorithm to be correctly tuned when false positives (windows around undesirable features of the seismogram) are minimized, and true positives (window around desirable features) are maximized. For a given dataset, the set of tuned parameters and their user-defined time dependencies completely determine the window selection results.
Use of the FLEXWIN algorithm need not be limited to tomography studies, nor to studies using 3D synthetics. It can be made to mimic travel-time based selection algorithms by including the travel-time based moveouts into the time-dependence of the STA:LTA water-level $w_E$ and of the rejection parameters $CC_0$, $\Delta \tau_0$ and $\Delta\ln{A}_0$.
-Compare with data strategy used by Chen for LA basin crustal structure??
+STILL REQUIRE A CONCLUDING PARAGRAPH.
-Full records vs selected phases. Relative phase weighting is one advantage of phase selection that has been used for some time (see \cite{LiRomanowicz1996} and \cite{MegninRomanowic1999}). It also applies to us (the measurement kernels are already automatically weighted to enhance the contribution to the small amplitude phases and the less well fitting phases). The noise argument again (only valid should we choose to use waveform difference on full waveforms). Measurement ease. Greater linearity of inversion (hence fewer expensive inversion steps) if we use observables that have more closely linear dependence on model parameters (each inversion step takes bigger steps down the misfit curve).
-
-
-\begin{itemize}
-
-\item Do we need to compare our selection strategy explicitly with Po Chen's (his is not explicitly stated - he uses P and S direct waves and isolates them using isolation filters obtained by windowing the synthetics)?
-
-
-\item Compare the three datasets. What is the main point we want to make from such a comparison?
-
-
-\item Ultimately, it should not be necessary to rotate horizontal components into the radial-transverse basis. Synthetics are computed for east and north, and so are the data, so for extreme 3D structure, it might be best to run the windowing code simply on the east-north records. Should this not go earlier in the paper?
-
-\end{itemize}
+%Compare with data strategy used by Chen for LA basin crustal structure??
+%
+%Full records vs selected phases.
+%The adjoint kernel calculation procedure allows us to measure and use for
+%tomographic inversion almost any part of the seismic signal. We do not even
+%need to identify specific seismic phases, as the kernel will take care of
+%defining the sensitivities.
+%Relative phase weighting is one advantage of phase selection that has been used for some time (see \cite{LiRomanowicz1996} and \cite{MegninRomanowic1999}). It also applies to us (the measurement kernels are already automatically weighted to enhance the contribution to the small amplitude phases and the less well fitting phases). The noise argument again (only valid should we choose to use waveform difference on full waveforms). Measurement ease. Greater linearity of inversion (hence fewer expensive inversion steps) if we use observables that have more closely linear dependence on model parameters (each inversion step takes bigger steps down the misfit curve).
+%
+%
+%\begin{itemize}
+%
+%\item Do we need to compare our selection strategy explicitly with Po Chen's (his is not explicitly stated - he uses P and S direct waves and isolates them using isolation filters obtained by windowing the synthetics)?
+%
+%
+%\item Compare the three datasets. What is the main point we want to make from such a comparison?
+%
+%
+%\item Ultimately, it should not be necessary to rotate horizontal components into the radial-transverse basis. Synthetics are computed for east and north, and so are the data, so for extreme 3D structure, it might be best to run the windowing code simply on the east-north records. Should this not go earlier in the paper?
+%
+%\end{itemize}
Modified: seismo/3D/automeasure/latex/flexwin_paper.pdf
===================================================================
(Binary files differ)
Modified: seismo/3D/automeasure/latex/flexwin_paper.tex
===================================================================
--- seismo/3D/automeasure/latex/flexwin_paper.tex 2008-03-26 10:27:58 UTC (rev 11568)
+++ seismo/3D/automeasure/latex/flexwin_paper.tex 2008-03-26 13:36:36 UTC (rev 11569)
@@ -18,8 +18,8 @@
\input{def_base}
\begin{document}
-\title{FLEXWIN: A flexible automated data-window selection algorithm}
-\author{Alessia Maggi, Carl Tape, Daniel Chao, Min Chen, Qinya Liu, Jeoren Tromp}
+\title{An automated data-window selection algorithm for adjoint tomography}
+\author{Alessia Maggi, Carl Tape, Min Chen, Daniel Chao, Jeoren Tromp}
\date{}
\maketitle
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