# [cig-commits] r13318 - in seismo/3D/ADJOINT_TOMO/flexwin: latex scripts user_files/socal_3D

carltape at geodynamics.org carltape at geodynamics.org
Sun Nov 16 00:15:04 PST 2008

Author: carltape
Date: 2008-11-16 00:15:03 -0800 (Sun, 16 Nov 2008)
New Revision: 13318

Modified:
Log:
Carl's FLEXWIN paper updates, and also updated socal files.

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/AM-allcitations.bib	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/AM-allcitations.bib	2008-11-16 08:15:03 UTC (rev 13318)
@@ -2647,10 +2647,20 @@
Date-Modified = {2008-03-24 18:51:05 +0100},
Journal = gji,
Pages = {427--440},
-	Title = {The use of velocity spectrum for stacking receiver                    functions and imaging upper mantle discontinuities},
+	Title = {The use of velocity spectrum for stacking receiver functions and imaging upper mantle discontinuities},
Volume = 117,
Year = 1994}

+ at article{HardebeckShearer2003,
+     AUTHOR = {J. L. Hardebeck and P. M. Shearer},
+     JOURNAL = bssa,
+     PAGES = {2434--2444},
+     TITLE = {{Using $S$/$P$ amplitude ratios to constrain the focal mechanisms of small earthquakes}},
+     VOLUME = {93},
+     NUMBER = {6},
+     YEAR = {2003}
+}
+
@article{HDD+92,
Author = {Haessler, H. and Deschampls, A. and Dufumier, H. and Fuenzalida, H. and Cisternas, A.},
Journal = gji,
@@ -6150,6 +6160,13 @@
Volume = 72,
Year = 2001}

+ at phdthesis{YTan06,
+     AUTHOR = {Y. Tan},
+     TITLE = {{Broadband waveform modeling over a dense seismic network}},
+     SCHOOL = {California Institute of Technology},
+     YEAR = {2006}
+}
+
@article{Tanaka2002,
Author = {Tanaka, S.},
Journal = epsl,

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/abstract.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/abstract.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -1,3 +1,5 @@
\begin{abstract}
-We present an algorithm for the automated selection of time windows on pairs of observed and synthetic seismograms.  {\bf The algorithm was designed specifically to automate window selection and measurement for adjoint tomography studies, but is sufficiently flexible to be adapted to many tomographic applications and seismological scenarios.}  Adjoint tomography utilizes 3D wavefield simulations that capture complex phases that do not necessarily exist in 1D simulations or traditional travel-time curves. {\bf It requires a data selection method that includes these new phases, maximizes the number of measurements made on each seismic record, while avoiding seismic noise. This selection method must also be automated in order to adapt to changes in the synthetic seismograms after each iteration of the tomographic inversion.  These considerations have led us to favor a signal processing approach to the time-window selection problem, and to the development of the FLEXWIN algorithm we present here.} We illustrate the algorithm using datasets from three distinct regions: the entire globe, the Japan subduction zone, and southern California.
+%We present an algorithm for the automated selection of time windows on pairs of observed and synthetic seismograms.  {\bf The algorithm was designed specifically to automate window selection and measurement for adjoint tomography studies, but is sufficiently flexible to be adapted to many tomographic applications and seismological scenarios.}  Adjoint tomography utilizes 3D wavefield simulations that capture complex phases that do not necessarily exist in 1D simulations or traditional travel-time curves. {\bf It requires a data selection method that includes these new phases, maximizes the number of measurements made on each seismic record, while avoiding seismic noise. This selection method must also be automated in order to adapt to changes in the synthetic seismograms after each iteration of the tomographic inversion.  These considerations have led us to favor a signal processing approach to the time-window selection problem, and to the development of the FLEXWIN algorithm we present here.} We illustrate the algorithm using datasets from three distinct regions: the entire globe, the Japan subduction zone, and southern California.
+{\bf CHT modified version:}
+We present FLEXWIN, an algorithm for the automated selection of time windows on pairs of observed and synthetic seismograms.  The algorithm was designed specifically to accommodate synthetic seismograms produced from 3D wavefield simulations that capture complex phases that do not necessarily exist in 1D simulations or traditional travel-time curves. Relying on signal processing tools and several user-tuned parameters, the algorithm is able to include these new phases and to maximize the number of measurements made on each seismic record, while avoiding seismic noise.  Our motivation is to use the algorithm for an iterative tomographic inversion, whereby the synthetic seismograms will change from one iteration to the next. Hence, automation is needed to handle the sheer volume of measurements and to allow for the increasing number of windows for each model iteration. The algorithm is sufficiently flexible to be adapted to many tomographic applications and seismological scenarios, including those based on synthetics generated from 1D models.  We illustrate the algorithm using datasets from three distinct regions: the entire globe, the Japan subduction zone, and southern California.
\end{abstract}

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/appendix.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/appendix.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -64,15 +64,14 @@
(e.g. {\em for t close to the expected arrival for $P_{\rm diff}$, reduce $r_0(t)$}).

-$c_{0-4}$ : These parameters are active in phase B of the algorithm, phase in which the suite of all possible data windows is pared down using criteria on the shape of the STA:LTA $E(t)$ waveform alone.  Detailed descriptions of the behavior of each parameter are available in section~\ref{sec:phaseB} and will not be repeated here.  We suggest the user start by setting these values to those used in our global example (see Table~\ref{tb:example_params}).  Subsequent minimal tuning should be performed by running the algorithm on a subset of the data and closely examining the lists of windows rejected at each stage to make sure the user agrees with the choices made by the algorithm.
+$c_{0-4}$ : These parameters are active in \stgc\ of the algorithm, the stage in which the suite of all possible data windows is pared down using criteria on the shape of the STA:LTA $E(t)$ waveform alone.  Detailed descriptions of the behavior of each parameter are available in section~\ref{sec:stageC} and will not be repeated here.  We suggest the user start by setting these values to those used in our global example (see Table~\ref{tb:example_params}).  Subsequent minimal tuning should be performed by running the algorithm on a subset of the data and closely examining the lists of windows rejected at each stage to make sure the user agrees with the choices made by the algorithm.

The objective of the tuning process summarily described here should be to maximize the selection of windows around desirable features in the seismogram, while minimizing the selection of undesirable features, bearing in mind that the desirability or undesirability of a given feature is subjective, and depends on how the user subsequently intends to use the information contained within he data windows.

\subsection{Examples of user functions\label{ap:user_fn}}

-As concrete examples of how the time dependence of these tuning parameters can be used, we present here the functional forms of these time dependencies used for the three example tomographic scenarios described in the text (Windowing Examples, section~\ref{sec:results}).
+As concrete examples of how the time dependence of these tuning parameters can be used, we present here the functional forms of these time dependencies used for the three example tomographic scenarios described in the text (Windowing Examples, section~\ref{sec:results}).  CHT modified: In each example we use information (predicted arrival times) derived from 1D Earth models to help guide certain user functions in the windowing algorithm. Note, however, that the actual selection of individual windows is based primarily on the details of the waveforms, and not on information from 1D Earth models.

-
\subsubsection{Global scenario\label{ap:user_global}}

In the following, $h$ indicates earthquake depth, $t_Q$ indicates the approximate start of the Love wave predicted by a group wave speed of 4.2~\kmps, and $t_R$ indicates the approximate end of the Rayleigh wave predicted by a group wave speed of 3.2~\kmps. In order to reduce the number of windows picked beyond R1, and to ensure that those selected beyond R1 are a very good match to the synthetic waveform, we raise the water level on the STA:LTA waveform and impose stricter criteria on the signal-to-noise ratio and the waveform similarity after the approximate end of the surface wave arrivals.  We allow greater flexibility in cross-correlation time lag $\Delta\tau$ for intermediate depth and deep earthquakes.  We lower the cross-correlation value criterion for surface waves in order to retain windows with a slight mismatch in dispersion characteristics.
@@ -148,8 +147,8 @@
\Delta\tau_0(t) & =
\begin{cases}
0.08 \text{$t_P$} & \text{$h \leq$ 70~km}, \\
-    max(0.06 \text{$t_P$}, 1.4\tau_0) & \text{70~km $< h <$ 300~km}, \\
-    max(0.06 \text{$t_P$}, 1.7\tau_0) & \text{$h \geq$ 300~km}.
+    \max(0.06 \text{$t_P$}, 1.4\tau_0) & \text{70~km $< h <$ 300~km}, \\
+    \max(0.06 \text{$t_P$}, 1.7\tau_0) & \text{$h \geq$ 300~km}.
\end{cases}
\end{align}
%--------------------------
@@ -158,25 +157,38 @@

In the following, $t_P$ and $t_S$ denote the start of the time windows for the crustal P wave and the crustal S wave, computed from a 1D layered model appropriate to Southern California \citep{Wald95}.  The start and end times for the surface-wave time window, $t_{R0}$ and $t_{R1}$, as well as the criteria for the time shifts $\Delta\tau_0(t)$, are derived from formulas in \cite{KomatitschEtal2004}. The source-receiver distance (in km) is denoted by $\Delta$.

-For the \trange{6}{40} data, we exclude any arrivals before the $P$ wave and reduce the number of windows picked beyond R1 by modulating $w_E(t)$.  We use constant values of $r_0(t)=r_0$, $\mathrm{CC}_0(t)=\mathrm{CC}_0$ and $\Delta \ln A_0(t)=\Delta \ln A_0$, but modulate the cross-correlation time lag criterion so that it is less strict at larger epicentral distances and for surface waves.  We therefore use:
+CHT modified
+
+For the \trange{6}{40} and \trange{3}{40} data, we use constant values of $r_0(t)=r_0$, $\mathrm{CC}_0(t)=\mathrm{CC}_0$, $\Delta\tau_0(t)=\Delta\tau_0$, and $\Delta \ln A_0(t)=\Delta \ln A_0$. We exclude any arrivals before the $P$ wave and after the Rayleigh wave. This is achieved by the box-car function for $w_E(t)$:
%
\begin{align}
w_E(t) & =
\begin{cases}
10 w_E & \text{$t < t_P$}, \\
w_E & \text{$t_P \le t \leq t_{R1}$}, \\
-    2 w_E & \text{$t > t_{R1}$},
+    10 w_E & \text{$t > t_{R1}$},
\end{cases}
-\\
-\Delta\tau_0(t) & =
-  \begin{cases}
-    3.0 + \Delta/80.0 & \text{$t \le t_{R0}$}, \\
-    3.0 + \Delta/50.0 & \text{$t > t_{R0}$},
-  \end{cases}
\end{align}
+%For the \trange{6}{40} data, we exclude any arrivals before the $P$ wave and reduce the number of windows picked beyond R1 by modulating $w_E(t)$.  We use constant values of $r_0(t)=r_0$, $\mathrm{CC}_0(t)=\mathrm{CC}_0$ and $\Delta \ln A_0(t)=\Delta \ln A_0$, but modulate the cross-correlation time lag criterion so that it is less strict at larger epicentral distances and for surface waves.  We therefore use:
+%
+%\begin{align}
+%w_E(t) & =
+%  \begin{cases}
+%    10 w_E & \text{$t < t_P$}, \\
+%    w_E & \text{$t_P \le t \leq t_{R1}$}, \\
+%    2 w_E & \text{$t > t_{R1}$},
+%  \end{cases}
+%\\
+%\Delta\tau_0(t) & =
+%  \begin{cases}
+%    3.0 + \Delta/80.0 & \text{$t \le t_{R0}$}, \\
+%    3.0 + \Delta/50.0 & \text{$t > t_{R0}$},
+%  \end{cases}
+%\end{align}

-For the \trange{2}{40} data, we avoid selecting surface-wave arrivals as the 3D model used to calculate the synthetics cannot produce the required complexity.  We remove the distance dependence on $\Delta\tau_0(t)$, as higher frequency body waves are well behaved in this model, and keep all other criteria the same.  The parameter modulation for these data becomes:
-
+For the \trange{2}{40} data, we avoid selecting surface-wave arrivals as the 3D model used to calculate the synthetics cannot produce the required complexity. The water-level criteria then becomes:
+%We remove the distance dependence on $\Delta\tau_0(t)$, as higher frequency body waves are well behaved in this model, and keep all other criteria the same.
+%The parameter modulation for these data becomes:
%
\begin{align}
w_E(t) & =
@@ -185,8 +197,8 @@
w_E & \text{$t_P \le t \leq t_S$}, \\
10 w_E & \text{$t > t_S$},
\end{cases}
-\\
-\Delta\tau_0(t) & = \Delta\tau_0.
+%\\
+%\Delta\tau_0(t) & = \Delta\tau_0.
\end{align}

}

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/def_base.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/def_base.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -26,5 +26,11 @@
\def\degW{$\rm^{\circ}W$}
\def\degcpkm{${\rm ^{\circ}C\; km}^{-1}$}

+\newcommand{\stga}{Stage~A}
+\newcommand{\stgb}{Stage~B}
+\newcommand{\stgc}{Stage~C}
+\newcommand{\stgd}{Stage~D}
+\newcommand{\stge}{Stage~E}
+
\newcommand{\trange}[2]{\mbox{{#1}--{#2}~s}}
\newcommand{\rmd}{\mathrm{d}}

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/discussion.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/discussion.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -7,7 +7,9 @@
FLEXWIN may also be used to select windows for tomographic problems in which separation of seismic arrivals is necessary and occurs naturally (with certain frequency and epicentral distance conditions) by virtue of differences in travel-times.  It can straightforwardly be adapted to studies of distinct body wave phases \citep[e.g.][]{RitsemaVanHeijst2002} or to emulate the wavepacket selection of \cite{PanningRomanowicz2006} by modulating the $w_E(t)$ water-level using predicted phase arrival times, and selecting appropriate values for the signal-to-noise, cross-correlation and amplitude limits.  The method can also be used to pre-select windows for studies of fundamental mode surface waves \citep[e.g. those based on the methods of][]{TrampertWoodhouse1995, EkstromEtal1997, LevshinRitzwoller2001} by modulating $w_E(t)$ to exclude portions of the waveform that do not correspond to the correct group velocity window or epicentral distance range.  Given the dispersed nature of surface waves, synthetics produced by 1D starting models often are considerably different in shape from the data, so the $CC$ and $\Delta T$ conditions (but not the signal-to-noise or $\Delta \ln A$ conditions) should relaxed in the window selection.  These windows should then be passed on to specific algorithms used to extract the dispersion information.
For this class of natural separation tomographic problems, the advantages of using FLEXWIN over manual or specifically designed automated windowing would be the encapsulation of the selection criteria entirely within the parameters of Table~\ref{tb:params} (and their time-dependent modulation), leading to greater clarity and portability between studies using different inversion methods.

-FLEXWIN is not indicated for tomographic problems in which the extraction and separation of information from overlapping portions of a single timeseries is required, for example studies of higher mode surface wave dispersion for which specific methods -- mode branch stripping \citep{vanHeijstWoodhouse1997}, separation of secondary observables \citep{CaraLeveque1987, Debayle1999}, partitioned waveform and automated multimode inversion \citep{Nolet1990, LebedevEtal2005}, non-linear direct search \citep{YoshizawaKennett2002b, VisserEtal2007}  -- have been developed.
+%FLEXWIN is not indicated for tomographic problems in which the extraction and separation of information from overlapping portions of a single timeseries is required, for example studies of higher mode surface wave dispersion for which specific methods -- mode branch stripping \citep{vanHeijstWoodhouse1997}, separation of secondary observables \citep{CaraLeveque1987, Debayle1999}, partitioned waveform and automated multimode inversion \citep{Nolet1990, LebedevEtal2005}, non-linear direct search \citep{YoshizawaKennett2002b, VisserEtal2007}  -- have been developed.
+{\bf CHT modify}
+FLEXWIN is not intended for tomographic problems in which the extraction and separation of information from overlapping portions of a single timeseries is required, for example studies of higher mode surface wave dispersion for which specific methods have been developed, for example, mode branch stripping \citep{vanHeijstWoodhouse1997}, separation of secondary observables \citep{CaraLeveque1987, Debayle1999}, partitioned waveform and automated multimode inversion \citep{Nolet1990, LebedevEtal2005}, and non-linear direct search \citep{YoshizawaKennett2002b, VisserEtal2007}.

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/figures_paper.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/figures_paper.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -16,6 +16,8 @@
$\mathrm{CC}_0(t)$          & acceptance level for normalized cross-correlation\\
$\Delta\tau_0(t)$  & acceptance level for time lag \\
$\Delta\ln{A}_0(t)$   & acceptance level for amplitude ratio \\
+$\Delta\tau_{\rm ref}$ & reference time lag measurement \\
+$\Delta\ln{A}_{\rm ref}$ & reference amplitude ratio \\
\hline
\multicolumn{2}{l}{Fine tuning parameters:} \\ [5pt]
$c_0$ & for rejection of internal minima \\
@@ -67,25 +69,27 @@
\clearpage
\begin{table}
\begin{center}
-\begin{tabular}{lccccc}
+\begin{tabular}{lcccccc}
\hline
-		& Global	& \multicolumn{2}{c}{Japan}	& \multicolumn{2}{c}{S. California} \\
+			& Global	& \multicolumn{2}{c}{Japan}	& \multicolumn{3}{c}{S. California} \\
\hline
-$T_{0,1}$	& 50, 150	& 24, 120 	& 6, 30		& 6, 40		& 2, 40		\\
-$r_{P,A}$	& 3.5, 3.0	& 3.5, 3.0	& 3.5, 3.0	& 3.5, 3.0	& 3.5, 2.5	\\
-$r_0$		& 2.5		& 1.5		& 3.0		& 2.5		& 4.0		\\
-$w_E$		& 0.08		& 0.10		& 0.12		& 0.22		& 0.07		\\
-$\mathrm{CC}_0$		& 0.85		& 0.70		& 0.70		& 0.74		& 0.85		\\
-$\Delta\tau_0$	& 15		& 12.0		& 3.0		& 3.0		& 2.0		\\
-$\Delta\ln{A}_0$& 1.0 		& 1.0		& 1.0		& 1.5		& 1.0		\\
+$T_{0,1}$		& 50, 150	& 24, 120 	& 6, 30		& 6, 30		& 3, 30		& 2, 30		\\
+$r_{P,A}$		& 3.5, 3.0	& 3.5, 3.0	& 3.5, 3.0	& 3.0, 2.5	& 2.5, 3.5	& 2.5, 3.5	\\
+$r_0$			& 2.5		& 1.5		& 3.0		& 3.0		& 4.0		& 4.0		\\
+$w_E$			& 0.08		& 0.10		& 0.12		& 0.18		& 0.11		& 0.07		\\
+$\mathrm{CC}_0$		& 0.85		& 0.70		& 0.70		& 0.71		& 0.80		& 0.85		\\
+$\Delta\tau_0$		& 15		& 12.0		& 3.0		& 8.0		& 4.0		& 3.0		\\
+$\Delta\ln{A}_0$	& 1.0 		& 1.0		& 1.0		& 1.5		& 1.0		& 1.0		\\
+$\Delta\tau_{\rm ref}$	& 0.0		& 0.0		& 0.0		& 4.0		& 2.0 		& 1.0		\\
+$\Delta\ln{A}_{\rm ref}$& 0.0		& 0.0		& 0.0		& 0.0		& 0.0		& 0.0		\\
\hline
-$c_0$		& 0.7		& 0.7		& 0.7		& 0.7		& 1.0		\\
-$c_1$		& 4.0		& 3.0		& 3.0		& 2.0		& 4.0		\\
-$c_2$		& 0.3		& 0.0		& 1.0		& 0.0		& 0.0		\\
-$c_{3a,b}$	& 1.0, 2.0	& 1.0, 2.0	& 1.0, 2.0	& 3.0, 2.0	& 4.0, 2.5	\\
-$c_{4a,b}$	& 3.0, 10.0	& 3.0, 25.0	& 3.0, 12.0	& 2.5, 12.0	& 2.0, 6.0	\\
+$c_0$			& 0.7		& 0.7		& 0.7		& 0.7		& 1.3		& 1.0		\\
+$c_1$			& 4.0		& 3.0		& 3.0		& 2.0		& 4.0		& 5.0		\\
+$c_2$			& 0.3		& 0.0		& 1.0		& 0.0		& 0.0		& 0.0		\\
+$c_{3a,b}$		& 1.0, 2.0	& 1.0, 2.0	& 1.0, 2.0	& 3.0, 2.0	& 4.0, 2.5	& 4.0, 2.5	\\
+$c_{4a,b}$		& 3.0, 10.0	& 3.0, 25.0	& 3.0, 12.0	& 2.5, 12.0	& 2.0, 6.0	& 2.0, 6.0	\\
$w_{\mathrm{CC}}, w_{\rm len}, w_{\rm nwin}$
-		& 1, 1, 1 	& 1, 1, 1	& 1, 1, 1	& 1, 0, 0	& 1, 0, 0.5	\\
+			& 1, 1, 1 	& 1, 1, 1	& 1, 1, 1	& 0.5,1.0,0.7	& 0.70,0.25,0.05 & 1,1,1	\\
\hline
\end{tabular}
\caption{\label{tb:example_params}
@@ -166,8 +170,8 @@
Window rejection applied to real data.
Top panel: observed (black) and synthetic (red) seismograms for the 050295B event
recorded at ABKT (see Figure~\ref{fg:stalta}).
-Subsequent panels: candidate windows at different stages, separated into Phase B (shape based rejection) and
-Phase C (fit based rejection).  Each candidate window is indicated by a black
+Subsequent panels: candidate windows at different stages, separated into \stgc\ (shape based rejection) and
+\stgd\ (fit based rejection).  Each candidate window is indicated by a black
segment.  The number of windows at each stage is shown to the left of the
panel.
}
@@ -181,7 +185,7 @@
\caption{\label{fg:criteria}
Time dependent fit based criteria
for the 050295B event recorded at ABKT. The time-dependence of these criteria
-is given by the formulae in Appendix~\ref{ap:user_global}. The lower limit on
+is given by the formulas in Appendix~\ref{ap:user_global}. The lower limit on
acceptable cross-correlation value, $\mathrm{CC}_0$ (solid line), is
0.85 for most of the duration of the seismogram; it is lowered to 0.75 during
the approximate surface wave window  defined by the group velocities 4.2\kmps\
@@ -195,7 +199,7 @@
\clearpage
\begin{figure}
\center \includegraphics[width=5in]{figures/fig/window_overlap.pdf}
-\caption{\label{fg:phaseD}
+\caption{\label{fg:stageE}
The selection of the best non-overlapping window
combinations.  Each grey box represents a distinct group of windows.
Non-overlapping subsets of windows are shown on separate lines.  Only one
@@ -263,7 +267,7 @@
%CMT focal mechanism, station ERM is marked as red triangles and all the other stations
%which recorded this event are marked by grey triangles.
%(b)~Results for station ERM for the period range \trange{24}{120}.
-%Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA/LTA records (right column) used to produce the window picks.
+%Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA:LTA records (right column) used to produce the window picks.
%(c)~Results for station ERM for the period range \trange{6}{30}.
%}
%\end{figure}
@@ -279,7 +283,7 @@
(a)~Map showing all stations with at least one measurement window for the period range \trange{24}{120} for this event.  ({\bf MIN: IS THIS CORRECT?})
Red triangle denotes station KIS.
(b)~Results for station KIS for the period range \trange{24}{120}.
-Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA/LTA records (right column) used to produce the window picks.
+Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA:LTA records (right column) used to produce the window picks.
(c)~Results for station KIS for the period range \trange{6}{30}.
}
\end{figure}
@@ -294,7 +298,7 @@
(a)~Map showing all stations with at least one measurement window for the period range \trange{24}{120} for this event.  ({\bf MIN: IS THIS CORRECT?})
Red triangle denotes station SHR.
(b)~Results for station SHR for the period range \trange{24}{120}.
-Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA/LTA records (right column) used to produce the window picks.
+Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA:LTA records (right column) used to produce the window picks.
(c)~Results for station SHR for the period range \trange{6}{30}.
Note that the corresponding low-frequency band-passed filtered version (b) has longer record length (800~s).
}
@@ -372,17 +376,17 @@
\caption{\label{fg:socal_CLC}
Window selection results for event 9818433 from Table~\ref{tb:events} recorded at station CLC ($\Delta = 211.7$~km).
%(a)~Source and station information for event 9818433 and station CLC.
-%(b)~Paths to each station with at least one measurement window for the period range \trange{6}{40}.
+%(b)~Paths to each station with at least one measurement window for the period range \trange{6}{30}.
%There are a total of 341 windows picked within 310 records.
%Triangle denotes station CLC.
-%(c)~Paths to each station with at least one measurement window for the period range \trange{2}{40}.
+%(c)~Paths to each station with at least one measurement window for the period range \trange{2}{30}.
%There are a total of 190 windows picked within 193 records.
%Triangle denotes station CLC.
-(a)~Map showing all stations with at least one measurement window for the period range \trange{6}{40} for this event.
+(a)~Map showing all stations with at least one measurement window for the period range \trange{6}{30} for this event.
Red triangle denotes station CLC.
-(b)~Results for station CLC for the period range \trange{6}{40}.
+(b)~Results for station CLC for the period range \trange{6}{30}.
Vertical (Z), radial (R), and transverse (T) records of data (black, left column) and synthetics (red, left column), as well as the STA:LTA records (right column) used to produce the window picks.
-(c)~Results for station CLC for the period range \trange{2}{40}.
+(c)~Results for station CLC for the period range \trange{2}{30}.
Note that corresponding lower-passed filtered versions are shown in (b).
}
\end{figure}
@@ -402,7 +406,7 @@
%\center
\includegraphics[width=6in]{figures/socal/9983429_T06_rs.pdf}
\caption{\label{fg:socal_rs_T06}
-Summary plots of windowing results for event 9983429 in Table~\ref{tb:events}, for the period range \trange{6}{40}.
+Summary plots of windowing results for event 9983429 in Table~\ref{tb:events}, for the period range \trange{6}{30}.
(a)~Map showing paths to each station with at least one measurement window.
(b)-(d)~Histograms of number of windows as a function of normalised cross-correlation $\mathrm{CC}$, time-lag $\tau$ and amplitude ratio $\Delta \ln A$.
(e)-(g)~Record sections of selected windows for the vertical, radial and transverse components.
@@ -416,7 +420,7 @@
%\includegraphics[width=7in]{figures/socal/9983429_T02_rs.pdf}
%\caption{\label{fg:socal_rs_T02}
%(THIS FIGURE COULD IN THEORY BE CUT OUT, IF SPACE IS SHORT.)
-%Summary plots of windowing results for event 9983429 in Table~\ref{tb:events}, for the period range \trange{2}{40}.
+%Summary plots of windowing results for event 9983429 in Table~\ref{tb:events}, for the period range \trange{2}{30}.
%Same as Figure~\ref{fg:socal_rs_T06}, only the windowing code has been run using a different set of parameters (Table~\ref{tb:example_params}), so that primarily only the body-wave arrivals are selected.
%}
%\end{figure}

===================================================================
(Binary files differ)

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/introduction.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/introduction.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -19,7 +19,9 @@
only with high-frequency body wave arrivals, while great-circle
surface-wave tomography must satisfy the path-integral approximation,
and only considers surface waves that present no evidence of multipathing.
-In both these examples, a large proportion of the information contained within the seismograms goes to waste.
+In both these examples, a large proportion of the information contained within the seismograms
+%goes to waste.  CHT
+is unused.
The emerging 3D-3D tomographic methods take advantage of
full wavefield simulations and numeric finite-frequency kernels,
thereby reducing
@@ -27,11 +29,18 @@
of sensitivity.  These methods seem to be the best candidates for studying
regions with complex 3D structure as they permit the use of a larger proportion of the information contained within each seismogram, including complex arrivals not predicted by 1D approximations of Earth structure.  In order to exploit the full power of 3D-3D tomographic methods, we require a new data selection strategy that does not exclude such complex arrivals.

-{\bf As data selection strategies for tomography depend so closely on the tomographic technique, there are nearly as many such strategies as there are tomographic methods.  Furthermore, many of these strategies have been automated in some way, as larger and larger volumes of data have become available for tomographic studies. We shall not try to be exhaustive in the following short presentation of currently available data selection methods, but shall limit our discussion to a few representative examples.  Body wave studies that have moved away from using manual travel-time picks or catalog arrival times generally pick windows around specific arrivals defined by predicted arrival times, and include automated tests on arrival time separation and/or the fit of observed to synthetic pulses to reject inadequate data \cite[e.g.][]{RitsemaVanHeijst2002,LawrenceShearer2008}.  Partial automation of the \cite{vanDecarCrosson1990} multi-channel cross-correlation method has led to efficient methods for obtaining highly accurate travel-time \citep{SiglochNolet2006,HouserEtal2008} and even attenuation \citep{LawrenceEtal2006} measurements.  In the surface wave community, there has been much work done to automate methods for extracting dispersion characteristics of fundamental mode \citep{TrampertWoodhouse1995, LaskeMasters1996, EkstromEtal1997, LevshinRitzwoller2001} and higher mode \citep{vanHeijstWoodhouse1997, Debayle1999, YoshizawaKennett2002b, BeuclerEtal2003, LebedevEtal2005, VisserEtal2007} surface waves.  Recently, \cite{PanningRomanowicz2006} have described an algorithm to semi-automatically pick body and surface wavepackets based on the predicted traveltimes of several phases.}
+{\bf As data selection strategies for tomography depend so closely on the tomographic technique, there are nearly as many such strategies as there are tomographic methods.  Furthermore, many of these strategies have been automated in some way, as larger and larger volumes of data have become available for tomographic studies.}
+% CHT cut following sentence
+%We shall not try to be exhaustive in the following short presentation of currently available data selection methods, but shall limit our discussion to a few representative examples.
+Body wave studies that have moved away from using manual travel-time picks or catalog arrival times generally pick windows around specific arrivals defined by predicted arrival times, and include automated tests on arrival time separation and/or the fit of observed to synthetic pulses to reject inadequate data \cite[e.g.][]{RitsemaVanHeijst2002,LawrenceShearer2008}.  Partial automation of the \cite{vanDecarCrosson1990} multi-channel cross-correlation method has led to efficient methods for obtaining highly accurate travel-time \citep{SiglochNolet2006,HouserEtal2008} and even attenuation \citep{LawrenceEtal2006} measurements.  In the surface wave community, there has been much work done to automate methods for extracting dispersion characteristics of fundamental mode \citep{TrampertWoodhouse1995, LaskeMasters1996, EkstromEtal1997, LevshinRitzwoller2001} and higher mode \citep{vanHeijstWoodhouse1997, Debayle1999, YoshizawaKennett2002b, BeuclerEtal2003, LebedevEtal2005, VisserEtal2007} surface waves.  Recently, \cite{PanningRomanowicz2006} have described an algorithm to semi-automatically pick body and surface wavepackets based on the predicted traveltimes of several phases.

-{\bf The automated data selection method we present in this paper goes further than that of \cite{PanningRomanowicz2006}, as it not tied to arrival time predictions of known phases. Our algorithm is designed to integrate with} the adjoint approach to 3D-3D tomography \citep{TrompEtal2005,LiuTromp2006,TapeEtal2007}, which builds upon \cite{Tarantola1984}.  In adjoint tomography, the sensitivity kernels that tie variations
+%{\bf The automated data selection method we present in this paper goes further than that of \cite{PanningRomanowicz2006}, as it is not tied to arrival time predictions of known phases.}
+{\bf CHT modified version:}
+Our algorithm is designed for tomographic applications with 3D earth reference models.
+Unlike the techniques discussed above, ours is not tied to arrival time predictions of known phases, and it therefore is able to accommodate complex phases due to 3D complexity in structure.
+One promising approach to 3D-3D tomography is based upon adjoint methods \citep{Tarantola1984,TrompEtal2005,LiuTromp2006,TapeEtal2007}.  In adjoint tomography'' the sensitivity kernels that tie variations
in Earth model parameters to variations in the misfit are obtained by
-interaction bewteen the wavefield used to generate the synthetic seismograms (the
+interaction between the wavefield used to generate the synthetic seismograms (the
direct wavefield) and an adjoint wavefield that obeys the same wave equation
as the direct wavefield, but with a source term which is derived from the
misfit measurements.  The computational cost of such kernel computations for use in seismic tomography depends only on the number of events, and not on the number of receivers nor on the number of measurements made.  It is therefore to our advantage to make the greatest number of measurements on each seismogram.
@@ -60,4 +69,4 @@
measurement method, to the method
used to obtain sensitivity kernels.  One of the major difficulties in defining a general data selection strategy is the great range of possible choices open to the tomographer.  We have  designed a configurable data selection process that can be adapted to different tomographic scenarios by tuning a handful of parameters (see Table~\ref{tb:params}).  Although we have designed our algorithm for use in adjoint tomography, its inherent flexibility should make it useful in many data-selection applications.

-We have successfully applied our windowing algorithm, the details of which are described in Section~\ref{sec:algorithm}, to diverse seismological scenarios: local and near regional tomography in Southern California, regional subduction-zone tomography in Japan, and global tomography.  We present examples from each of these scenarios in Section~\ref{sec:results}, and {\bf we discuss the use of the algorithm in the context of tomography -- and more specifically adjoint tomography -- in Section~\ref{sec:discuss}.}
+We have successfully applied our windowing algorithm, the details of which are described in Section~\ref{sec:algorithm}, to diverse seismological scenarios: local and near regional tomography in Southern California, regional subduction-zone tomography in Japan, and global tomography.  We present examples from each of these scenarios in Section~\ref{sec:results}, and we discuss the use of the algorithm in the context of tomography in Section~\ref{sec:discuss}.

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/method.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/method.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -8,20 +8,20 @@
covering more of the available data.  The input seismograms can be measures of displacement, velocity or acceleration, indifferently.  There is no requirement for horizontal signals to be rotated into radial and transverse directions.  All the synthetic seismograms presented in
this paper have been generated using the SPECFEM3D package \citep{KomatitschEtal2002}.

-The window selection process has five phases, each of which is discussed individually
-below: {\em phase 0:} pre-processing; {\em phase A:} definition of preliminary
-measurement windows; {\em phase B:} rejection of preliminary windows based on
-the content of the synthetic seismogram alone; {\em phase C:} rejection of
+The window selection process has five stages, each of which is discussed individually
+below: {\em \stga:} pre-processing; {\em \stgb:} definition of preliminary
+measurement windows; {\em \stgc:} rejection of preliminary windows based on
+the content of the synthetic seismogram alone; {\em \stgd:} rejection of
preliminary windows based on the differences between observed and synthetic
-seismograms; {\em phase D:} resolution of preliminary window overlaps.  The parameters that permit tuning of the
+seismograms; {\em \stge:} resolution of preliminary window overlaps.  The parameters that permit tuning of the
window selection towards a specific tomographic scenario are all contained in a
simple parameter file (see Table~\ref{tb:params}).  More complexity and finer
tuning can be obtained by rendering some of these parameters time dependent via user defined functions that can depend on the source parameters (e.g. event location or depth).

%----------------------

-\subsection{Phase 0 \label{sec:phase0}}
-The purpose of this phase is to pre-process input seismograms, to reject
+\subsection{\stga\ \label{sec:stageA}}
+The purpose of this stage is to pre-process input seismograms, to reject
noisy records, and to set up a secondary waveform (the short-term / long-term average ratio) derived from the envelope of the synthetic seismogram.  This STA:LTA waveform will be used later to define preliminary
measurement windows.

@@ -117,7 +117,7 @@

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

-\subsection{Phase A \label{sec:phaseA}}
+\subsection{\stgb\ \label{sec:stageB}}

The correspondence between local maxima in the STA:LTA waveform $E(t)$ and the
position of the seismic phases in the synthetic seismogram suggests that we
@@ -164,7 +164,7 @@

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

-\subsection{Phase B \label{sec:phaseB}}
+\subsection{\stgc\ \label{sec:stageC}}
%{\em Parameters used: $T_0$, $w_E(t)$, $c_{0-4}$.}

After having created a complete suite of candidate time windows in the manner
@@ -233,12 +233,12 @@
$c_{4b} T_0$ after their last local maximum (see Figure~\ref{fg:win_composite}d).

Figures~\ref{fg:win_composite} and~\ref{fg:separation} illustrate the shape-based
-rejection procedure (Phase B) on a schematic $E(t)$ time series.  Each
+rejection procedure (\stgc) on a schematic $E(t)$ time series.  Each
successive criterion reduces the number of acceptable candidate windows.  A
similar reduction occurs when this procedure is applied to real $E(t)$ time series, as shown
by the upper portion of Figure~\ref{fg:win_rej_data}.

-\subsection{Phase C \label{sec:phaseC}}
+\subsection{\stgd\ \label{sec:stageD}}
%{\bf User parameters: $\mathrm{CC}_0(t)$, $\Delta\tau_0(t)$, $\Delta\ln{A}_0(t)$}

After having greatly reduced the number of candidate windows by rejection based
@@ -268,9 +268,16 @@
synthetic and observed phase arrival. The amplitude ratio $\Delta \ln A$ is
defined as the amplitude ratio between observed and synthetic
seismograms \citep{DahlenBaig2002}
+%
+%$$+%\Delta\ln{A} = \left[ \frac{\int d(t)^2 \,\rmd t}{\int s(t)^2 \,\rmd t} \right]^{1/2} - 1. \label{eq:dlnA_def} +%$$
$$-\Delta\ln{A} = \left[ \frac{\int d(t)^2 \,\rmd t}{\int s(t)^2 \,\rmd t} \right]^{1/2} - 1. \label{eq:dlnA_def} +\Delta\ln{A} = \ln(A_{\rm obs}/A_{\rm syn}) += 0.5 \ln \left[ \frac{\int d(t)^2 \,\rmd t}{\int s(t)^2 \,\rmd t} \right] \label{eq:dlnA_def}$$
+%
+(Note that \citet[][Eq.~3]{DahlenBaig2002} is the first-order approximation of (\ref{eq:dlnA_def}).)
The limits that trigger rejection of windows based on the values of these four
quantities are the time dependent parameters $r_0(t)$, $\mathrm{CC}_0(t)$, $\Delta \tau_0(t)$ and $\Delta \ln A_0(t)$ in Table~\ref{tb:params}.
@@ -283,13 +290,32 @@
the example seismogram of Figure~\ref{fg:win_rej_data}.

We only accept candidate windows that satisfy all of the following:
+%\begin{align}
+%{\rm SNR}_W & \geq r_0(t_M), \label{eq:snr_win} \\
+%{\rm CC} & \geq {\rm CC}_0(t_M), \label{eq:cc} \\
+%|\Delta\tau| & \leq \Delta\tau_0(t_M), \label{eq:tau} \\
+%|\Delta\ln{A}| & \leq \Delta\ln{A}_0(t_M), \label{eq:dlnA}
+%\end{align}
+{\bf CHT modify equation and following text}
\begin{align}
{\rm SNR}_W & \geq r_0(t_M), \label{eq:snr_win} \\
{\rm CC} & \geq {\rm CC}_0(t_M), \label{eq:cc} \\
-|\Delta\tau| & \leq \Delta\tau_0(t_M), \label{eq:tau} \\
-|\Delta\ln{A}| & \leq \Delta\ln{A}_0(t_M), \label{eq:dlnA}
+\Delta\tau_{\rm min} \leq \Delta\tau & \leq \Delta\tau_{\rm max}, \label{eq:tau} \\
+\Delta\tau_{\rm max} \leq \Delta\ln{A} & \leq \Delta\ln{A}_{\rm max}, \label{eq:dlnA}
\end{align}
-where $t_M$ is the time of the window's seed maximum.  In words, we only accept
+%
+where
+%
+\begin{eqnarray}
+\Delta\tau_{\rm min} &\equiv& \Delta\tau_{\rm ref} - \Delta\tau_0(t_M) \\
+\Delta\tau_{\rm max} &\equiv& \Delta\tau_{\rm ref} + \Delta\tau_0(t_M) \\
+\Delta\ln{A}_{\rm min} &\equiv& \Delta\ln{A}_{\rm ref} - \Delta\ln{A}_0(t_M) \\
+\Delta\ln{A}_{\rm max} &\equiv& \Delta\ln{A}_{\rm ref} + \Delta\ln{A}_0(t_M)
+\end{eqnarray}
+%
+where $t_M$ is the time of the window's seed maximum.  The reference measurement variables, $\Delta\tau_{\rm ref}$ and $\Delta\ln{A}_{\rm ref}$, allow for systematic differences between data and synthetics.  For example, if the 3D velocity model is on average too fast, then $\Delta\tau_{\rm ref}$ should be set to a positive value.  Or if the magnitudes of the synthetic sources lead to systematically non-zero $\Delta\ln{A}$ values, then $\Delta\ln{A}_{\rm ref}$ should be chosen accordingly.  In essence, these reference values should designate the approximate center-value of the distribution of measurements (for example, Figure~\ref{fg:socal_rs_T06}c).
+
+In words, we only accept
windows in which the observed signal is sufficiently above the noise level, the observed and
synthetic signals are reasonably similar in shape, their arrival time
differences are small, and their amplitudes are broadly compatible.  When the synthetic and observed
@@ -299,7 +325,7 @@
essential, however, in eliminating problems due secondary events (natural or
man-made), diffuse noise sources, or instrumental glitches.

-\subsection{Phase D \label{sec:phaseD}}
+\subsection{\stge\ \label{sec:stageE}}
%{\em User parameters: $w_{\mathrm{CC}}$, $w_{\rm len}$.}

After having rejected candidate data windows that fail any of the shape or
@@ -328,9 +354,9 @@
removes the straightforward solution of merging overlapping windows.  Indeed, given any two
overlapping windows, we know that the window defined by their merger
existed in the complete list of candidate windows obtained at the end of
-Phase~A, and that its absence from the current list means it was rejected
-either because of the shape of its $E(t)$ time-series (Phase~B), or because of
-an inadequate similarity between observed and synthetic waveforms (Phase~C).
+\stgb, and that its absence from the current list means it was rejected
+either because of the shape of its $E(t)$ time-series (\stgc), or because of
+an inadequate similarity between observed and synthetic waveforms (\stgd).
It would therefore be meaningless to re-instate such a window at this stage.
Any modification of current candidate windows would be disallowed by similar
considerations.  We must therefore choose between overlapping
@@ -345,7 +371,7 @@
three scores is necessary, and is controlled by the three parameters
$w_{\mathrm{CC}}$, $w_{\rm len}$ and $w_{\rm nwin}$ in Table~\ref{tb:params}. }

-As can be seen in Figure~\ref{fg:phaseD}, the generation of subsets is
+As can be seen in Figure~\ref{fg:stageE}, the generation of subsets is
facilitated by first grouping candidate windows such that no group overlaps
with any other group.  The selection of the optimal subsets can then be
performed independently within each group.  We score each non-overlapping

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/latex/results.tex	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/latex/results.tex	2008-11-16 08:15:03 UTC (rev 13318)
@@ -262,15 +262,16 @@
\subsection{Local tomography in Southern California}
\label{sec:socal}

-Our last scenario is a local tomographic study of southern California.  We apply the windowing algorithm to a set of 150 events within southern California, for which we have computed synthetic seismograms using the spectral-element method and a regional 3D crustal and upper mantle model \citep{KomatitschEtal2004}.  This model contains three discontinuities: the surface topography (included in the mesh), the basement layer that separates the sedimentary basins from the bedrock, and the Moho, separating the lower crust from the upper mantle. The basement surface is essential for simulating the resonance of seismic waves within sedimentary basins, such as the Ventura basin, the Los Angeles basin, and the Salton trough \citep{KomatitschEtal2004,LovelyEtal2006}. The smooth 3D background velocity model used in \citet{KomatitschEtal2004} was determined by \citet{Hauksson2000}; we use an updated version provided by \citet{LinEtal2007}. The physical domain of the model is approximately 600~km by 500~km at the surface, and extends to a depth of 60~km. Our simulations of seismic waves are numerically accurate down to a period of 2~s.
+Our last scenario is a local tomographic study of southern California.  We apply the windowing algorithm to a set of 140 events within southern California, for which we have computed synthetic seismograms using the spectral-element method and a regional 3D crustal and upper mantle model \citep{KomatitschEtal2004}.  This model contains three discontinuities: the surface topography (included in the mesh), the basement layer that separates the sedimentary basins from the bedrock, and the Moho, separating the lower crust from the upper mantle. The model includes several sedimentary basins, such as the Ventura basin, the Los Angeles basin, and the Salton trough \citep{KomatitschEtal2004,LovelyEtal2006}. The smooth 3D background velocity model used in \citet{KomatitschEtal2004} was determined by \citet{Hauksson2000}; we use an updated version provided by \citet{LinEtal2007}. The physical domain of the model is approximately 600~km by 500~km at the surface, and extends to a depth of 60~km. Our simulations of seismic waves are numerically accurate down to a period of 2~s.

-The 150 events, with $M_w$ magnitudes between 3.5 and 5.5, were recorded between 1999 and 2007 and constitute a subset of the focal mechanisms presented by \citet{ClintonEtal2006}.  The locations and origin times are primarily from \citet{LinEtal2008}, supplemented by the catalog of \citet{ThurberEtal2006} for events near Parkfield, and the catalog of \citet{McLarenEtal2008} for events near San Simeon.  The Parkfield and San Simeon regions had $M_w > 6$ earthquakes within our time period of interest: 2004.09.28~$M_w$~6.0 (Parkfield) and 2003.12.22~$M_w$~6.5 (San Simeon). Aftershocks of both events are included in the dataset.
+%The 140 events, with $M_w$ magnitudes between 3.5 and 5.5, were recorded between 1999 and 2007 and constitute a subset of the focal mechanisms presented by \citet{ClintonEtal2006}.  The locations and origin times are primarily from \citet{LinEtal2008}, supplemented by the catalog of \citet{ThurberEtal2006} for events near Parkfield, and the catalog of \citet{McLarenEtal2008} for events near San Simeon.  The Parkfield and San Simeon regions had $M_w > 6$ earthquakes within our time period of interest: 2004.09.28~$M_w$~6.0 (Parkfield) and 2003.12.22~$M_w$~6.5 (San Simeon). Aftershocks of both events are included in the dataset.
+The 140 events have $M_w$ magnitudes between 3.5 and 5.5 and were recorded between 1999 and 2007. The locations and origin times are primarily from \citet{LinEtal2008}, and the focal mechanisms are from \citet{ClintonEtal2006}, \citet{HardebeckShearer2003}, or \citet{YTan06}.

-We test the windowing code using two period ranges: \trange{6}{40} and \trange{2}{40}.  The parameters we use for the windowing code are listed in Table~\ref{tb:example_params}.  Figures~\ref{fg:socal_CLC} and~\ref{fg:socal_FMP}  show examples of the output from the windowing algorithm for event 9818433 listed in Table~\ref{tb:events} recorded at two different stations, while Figure~\ref{fg:socal_rs_T06} shows a summary plot for event 9983429 in the  \trange{6}{40} period range.
+We test the windowing code using three period ranges: \trange{6}{30}, \trange{3}{30}, and \trange{2}{30}.  The parameters we use for the windowing code are listed in Table~\ref{tb:example_params}.  Figures~\ref{fg:socal_CLC} and~\ref{fg:socal_FMP}  show examples of the output from the windowing algorithm for event 9818433 listed in Table~\ref{tb:events} recorded at two different stations, while Figure~\ref{fg:socal_rs_T06} shows a summary plot for event 9983429 in the \trange{6}{30} period range.

The windowing algorithm tends to identify five windows on each set of three-component longer-period seismograms (Figures~\ref{fg:socal_CLC} and~\ref{fg:socal_rs_T06}): on the vertical and radial components the first window corresponds to the body-wave arrival and the second to the Rayleigh wave, while windows on the transverse component capture the Love wave.
-The shorter-period synthetic seismograms do not agree well with the observed seismograms, especially in the later part of the signal, leading to fewer picked windows. In Figure~\ref{fg:socal_CLC}e, only two windows are selected by the algorithm: a P arrival recorded on the radial component, and the combined S and Love-wave arrival on the transverse component. The P-wave arrival on the vertical component is rejected because the cross-correlation value within the time window did not exceed the specified minimum value of 0.85 (Table~\ref{tb:example_params}).
+The shorter-period synthetic seismograms do not agree well with the observed seismograms, especially in the later part of the signal, leading to fewer picked windows. In Figure~\ref{fg:socal_CLC}c, only three windows are selected by the algorithm: the P arrival recorded on the radial component, the S arrival on the transverse component, and the Love-wave arrival on the transverse component. The P-wave arrival on the vertical component is rejected because the cross-correlation value within the time window did not exceed the specified minimum value of 0.85 (Table~\ref{tb:example_params}). The small peak at 38~s in the STA:LTA curve for the transverse component identifies P-wave energy that does not have a large enough signal-to-noise ratio to be picked.  However, it highlights the possibility of measuring subtle phases that may be present in 3D synthetics.

-Figure~\ref{fg:socal_FMP} shows results for the same event as Figure~\ref{fg:socal_CLC}, but for a different station, FMP, situated 52~km from the event and within the Los Angeles basin. Comparison of the two figures highlights the characteristic resonance caused by the thick sediments within the basin.  This resonance is beautifully captured by the transverse component synthetics (Figure~\ref{fg:socal_FMP}d, record T), thanks to the inclusion of the basement layer in the crustal model \citep{KomatitschEtal2004}. In order to pick such long time windows with substantial frequency-dependent measurement differences, we are forced to lower the minimum cross-correlation value $\mathrm{CC}_0$ for the entire dataset (0.74 in Table~\ref{tb:example_params}) and increase $c_{4b}$ to capture the slow decay in the STA:LTA curves (Figure~\ref{fg:socal_FMP}d, record T). It is striking that although these arrivals look nothing like the energy packets typical for the global case, the windowing algorithm is still able to determine the proper start and end times for the windows.  In Figure~\ref{fg:socal_FMP}e the windowing algorithm selects three short-period body-wave time windows with superb agreement between data and synthetics.
+Figure~\ref{fg:socal_FMP} shows results for the same event as Figure~\ref{fg:socal_CLC}, but for a different station, FMP, situated 52~km from the event and within the Los Angeles basin. Comparison of the two figures highlights the characteristic resonance caused by the thick sediments within the basin.  This resonance is beautifully captured by the transverse component synthetics (Figure~\ref{fg:socal_FMP}b, record T), thanks to the inclusion of the basin in the model \citep{KomatitschEtal2004}. In order to pick such long time windows with substantial frequency-dependent measurement differences, we are forced to lower the minimum cross-correlation value $\mathrm{CC}_0$ for the entire dataset (0.71 in Table~\ref{tb:example_params}) and increase $c_{4b}$ to capture the slow decay in the STA:LTA curves (Figure~\ref{fg:socal_FMP}b, record T). It is striking that although these arrivals look nothing like the energy packets typical for the global case, the windowing algorithm is still able to determine the proper start and end times for the windows.  In Figure~\ref{fg:socal_FMP}c the windowing algorithm selects three short-period body-wave time windows with superb agreement between data and synthetics.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/scripts/pick_all_windows.pl	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/scripts/pick_all_windows.pl	2008-11-16 08:15:03 UTC (rev 13318)
@@ -39,7 +39,7 @@
#
#  EXAMPLES:
#    pick_all_windows.pl m13 0 6/30   1/204 1/1/1/0 1 0     # make plots and WINDOWS file, T = 6-30s
-#    pick_all_windows.pl m13 0 3/30   1/204 1/1/1/1 1 0     # make plots and WINDOWS file, T = 3-30s
+#    pick_all_windows.pl m13 0 3/30   1/204 1/1/1/0 1 0     # make plots and WINDOWS file, T = 3-30s
#    pick_all_windows.pl m13 0 2/30   1/204 1/1/1/1 1 0     # make plots and WINDOWS file, T = 2-30s
#
#    pick_all_windows.pl m00 0 6/30 179/179 1/1/0/0 1 0     # make plots only, T = 6-30s
@@ -144,6 +144,7 @@

# NEW: EVENT LIST
$eid_list = "/net/sierra/raid1/carltape/results/EID_LISTS/syn_run_${smodel}";
+$eid_list = "/net/sierra/raid1/carltape/results/EID_LISTS/syn_run_m12"; if (not -f$eid_list) {die("check if eid_list ${eid_list} exist or not\n")} open(IN,$eid_list); @eids = <IN>; close(IN);
$nevent0 = @eids; Added: seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/PAR_FILE_T003_T030_m00 =================================================================== --- seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/PAR_FILE_T003_T030_m00 (rev 0) +++ seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/PAR_FILE_T003_T030_m00 2008-11-16 08:15:03 UTC (rev 13318) @@ -0,0 +1,71 @@ +# ------------------------------------------------------------- +# +# This is the parameter file for FLEXWIN. It is based on the +# same syntax as the Par_file for SPECFEM. Variable names are +# put first, values are placed after the 34th column. +# +# Comment lines and blank lines are significant. If you +# change the layout of this file or add/remove parameters +# you must also modify the user_variables module and the +# read_parameter_file subroutine at the start of seismo_subs.f90. +# +# ------------------------------------------------------------- + +# ------------------------------------------------------------- +# boolean parameters +DEBUG = .true. +MAKE_SEISMO_PLOTS = .true. +MAKE_WINDOW_FILES = .true. +BODY_WAVE_ONLY = .true. + +# ------------------------------------------------------------- +# period min/max for filtering +RUN_BANDPASS = .false. +WIN_MIN_PERIOD = 3.00 +WIN_MAX_PERIOD = 30.00 + +# ------------------------------------------------------------- +# E(t) water level +STALTA_BASE = 0.11 + +# ------------------------------------------------------------- +# maximum allowable time shift from reference TSHIFT +TSHIFT_BASE = 4.0 +TSHIFT_REFERENCE = 2.0 + +# ------------------------------------------------------------- +# maximum allowable amplitude measurement relative to reference DLNA +DLNA_BASE = 1.0 +DLNA_REFERENCE = 0.0 + +# ------------------------------------------------------------- +# limit on CC for window acceptance +CC_BASE = 0.80 + +# ------------------------------------------------------------- +# boolean switch for check_data_quality +DATA_QUALITY = .true. + +# if DATA_QUALITY = .true. and if two different measurements of +# signal-to-noise ratios exceeds these two base levels, +# then the data time series (and syn) is kept +SNR_INTEGRATE_BASE = 2.5 +SNR_MAX_BASE = 3.5 + +# ------------------------------------------------------------- +# limit on signal to noise ratio in a particular window. +WINDOW_SNR_BASE = 4.0 + +# ------------------------------------------------------------- +# Fine tuning constants +C_0 (internal minima) = 1.3 +C_1 (small windows) = 4.0 +C_2 (prominence) = 0.0 +C_3a (separation height) = 4.0 +C_3b (separation time) = 2.5 +C_4a (curtail on left) = 2.0 +C_4b (curtail on right) = 6.0 + +WEIGHT_SPACE_COVERAGE = 0.70 +WEIGHT_AVERAGE_CC = 0.25 +WEIGHT_N_WINDOWS = 0.05 Modified: seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/user_functions_m00.f90 =================================================================== --- seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/user_functions_m00.f90 2008-11-15 21:47:02 UTC (rev 13317) +++ seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/user_functions_m00.f90 2008-11-16 08:15:03 UTC (rev 13318) @@ -115,15 +115,15 @@ !!$        endif

! double the STA/LTA water level after the surface waves
-        !if(time.gt.Sw_end) then
-        !   STALTA_W_LEVEL(i) = 2.0*STALTA_BASE
-        !endif
-
-        ! allow 100 seconds to possibly capture additional phases
-        if(time.gt. (Sw_end+100.0) ) then
-           STALTA_W_LEVEL(i) = 10.*STALTA_BASE
+        if(time.gt.Sw_end) then
+           STALTA_W_LEVEL(i) = 10.0*STALTA_BASE
endif

+!!$! allow 100 seconds to possibly capture additional phases +!!$        if(time.gt. (Sw_end+100.0) ) then
+!!$STALTA_W_LEVEL(i) = 10.*STALTA_BASE +!!$        endif
+
endif

enddo

===================================================================
--- seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/user_functions_m13.f90	2008-11-15 21:47:02 UTC (rev 13317)
+++ seismo/3D/ADJOINT_TOMO/flexwin/user_files/socal_3D/user_functions_m13.f90	2008-11-16 08:15:03 UTC (rev 13318)
@@ -96,6 +96,8 @@
endif

! raises STA/LTA water level after surface wave arrives
+     ! NOTE: CHT is effectively no longer using BODY_WAVE mode at all,
+     !       but for the 2s data, we do not look AFTER the surface waves for exotic phases.
if (BODY_WAVE_ONLY) then
!if(time.gt.S_end) then
if(time.gt.Sw_end) then