diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b568b28
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+*.o
+*.mod
+
diff --git a/README.md b/README.md
index ecb016c..a53033a 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,11 @@
-DSurfTomo is a surface wave inversion program which can directly invert surface wave dispersion data to 3D shear wave speed without the intermediate step of constructing the phase or group velocity maps.
+DSurfTomo is a surface wave inversion program which can irectly invert surface wave dispersion data directly to 3D shear wavespeed models, without the intermediate step of constructing the phase or group velocity maps.
The fast marching method (FMM) (Rawlinson et al., 2004) is used to compute, at each period, surface wave travel times and ray paths between sources and receivers. This avoids the assumption of great-circle propagation that is used in most surface wave tomographic studies, but which is not appropriate in complex media.
Please refer to Fang et al. (2015 , GJI) for the detail description of the method.
Fang, H., Yao, H., Zhang, H., Huang, Y. C., & van der Hilst, R. D. (2015). Direct inversion of surface wave dispersion for three-dimensional shallow crustal structure based on ray tracing: methodology and application. Geophysical Journal International, 201(3), 1251-1263.
Rawlinson, N. & Sambridge, M., 2004. Wave front evolution in strongly heterogeneous layered media using the fast marching method, Geophys. J. Int., 156(3), 631–647
+
+For V2.0, I incorporated the random projections based inversion using Poisson Voronoi cells. However, it has not been fully tested, so any feedbacks will be helpful. For more information about the inversion method, please refer to:
+
+Fang, H., van der Hilst, R. D., de Hoop, M. V., Kothari, K., Gupta, S., & Dokmanić, I. (2020). Parsimonious Seismic Tomography with Poisson Voronoi Projections: Methodology and Validation. Seismological Research Letters, 91(1), 343-355.
diff --git a/configure b/configure
index cc38a3b..b78cff0 100755
--- a/configure
+++ b/configure
@@ -1,16 +1,17 @@
#!/usr/bin/env python
# this is a script to install surf_tomo and surf_tomo_syn in your system
# written by Hongjian Fang(fanghj@mail.ustc.edu.cn)
+
import os
if 'bin' in os.listdir('.'):
- print 'installation beginning'
+ print ('Installation beginning')
else:
- print 'installation beginning'
- os.mkdir('bin')
+ print ('Installation beginning')
+ os.mkdir('bin')
os.chdir('src')
os.system('make clean')
os.system('make')
os.system('cp DSurfTomo ../bin')
-print '--------------------------------------'
-print 'Finishing DSurfTomo compiling'
-print '--------------------------------------'
+print ('--------------------------------------')
+print ('DSurfTomo compiling Finished')
+print ('--------------------------------------')
diff --git a/example/DSurfTomo.in b/example_Voronoi_clean/DSurfTomo.in
similarity index 88%
rename from example/DSurfTomo.in
rename to example_Voronoi_clean/DSurfTomo.in
index bce45ef..7f28655 100644
--- a/example/DSurfTomo.in
+++ b/example_Voronoi_clean/DSurfTomo.in
@@ -6,7 +6,7 @@ surfdataTB.dat c: data file
25.2 121.35 c: goxd gozd (upper left point,[lat,lon])
0.015 0.017 c: dvxd dvzd (grid interval in lat and lon direction)
20 c: max(sources, receivers)
-4.0 0.1 c: weight damp
+4.0 1.0 c: weight damp
3 c: sablayers (for computing depth kernel, 2~5)
0.5 2.8 c: minimum velocity, maximum velocity (a priori information)
10 c: maximum iteration
@@ -18,4 +18,5 @@ surfdataTB.dat c: data file
0 c: kmaxLg
0 c: synthetic flag(0:real data,1:synthetic)
0.02 c: noiselevel
-0.5 c: threshold
+0.05 c: threshold
+1 100 50 c: vorotomo,ncells,nrelizations
diff --git a/example/MOD b/example_Voronoi_clean/MOD
similarity index 100%
rename from example/MOD
rename to example_Voronoi_clean/MOD
diff --git a/example/MOD.true b/example_Voronoi_clean/MOD.true
similarity index 100%
rename from example/MOD.true
rename to example_Voronoi_clean/MOD.true
diff --git a/example/surfdataTB.dat b/example_Voronoi_clean/surfdataTB.dat
similarity index 100%
rename from example/surfdataTB.dat
rename to example_Voronoi_clean/surfdataTB.dat
diff --git a/example_smoothing_clean/DSurfTomo.in b/example_smoothing_clean/DSurfTomo.in
new file mode 100644
index 0000000..b5793d9
--- /dev/null
+++ b/example_smoothing_clean/DSurfTomo.in
@@ -0,0 +1,22 @@
+cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
+c INPUT PARAMETERS
+cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
+surfdataTB.dat c: data file
+18 18 9 c: nx ny nz (grid number in lat lon and depth direction)
+25.2 121.35 c: goxd gozd (upper left point,[lat,lon])
+0.015 0.017 c: dvxd dvzd (grid interval in lat and lon direction)
+20 c: max(sources, receivers)
+4.0 1.0 c: weight damp
+3 c: sablayers (for computing depth kernel, 2~5)
+0.5 2.8 c: minimum velocity, maximum velocity (a priori information)
+10 c: maximum iteration
+0.2 c: sparsity fraction
+26 c: kmaxRc (followed by periods)
+0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0
+0 c: kmaxRg
+0 c: kmaxLc
+0 c: kmaxLg
+0 c: synthetic flag(0:real data,1:synthetic)
+0.02 c: noiselevel
+0.05 c: threshold
+0 100 50 c: vorotomo,ncells,nrelizations
diff --git a/example_smoothing_clean/MOD b/example_smoothing_clean/MOD
new file mode 100644
index 0000000..2487402
--- /dev/null
+++ b/example_smoothing_clean/MOD
@@ -0,0 +1,163 @@
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diff --git a/example_smoothing_clean/MOD.true b/example_smoothing_clean/MOD.true
new file mode 100644
index 0000000..019f624
--- /dev/null
+++ b/example_smoothing_clean/MOD.true
@@ -0,0 +1,162 @@
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diff --git a/example_smoothing_clean/surfdataTB.dat b/example_smoothing_clean/surfdataTB.dat
new file mode 100644
index 0000000..e4bf857
--- /dev/null
+++ b/example_smoothing_clean/surfdataTB.dat
@@ -0,0 +1,2510 @@
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diff --git a/scripts/GenerateIniMOD.py b/scripts/GenerateIniMOD.py
index 2fc776a..92a23e0 100644
--- a/scripts/GenerateIniMOD.py
+++ b/scripts/GenerateIniMOD.py
@@ -8,10 +8,11 @@ import numpy as np
#start
nx=18
ny=18
-nz=8
minvel=0.8
velgrad=0.5
dep1=np.array([0,0.2,0.4,0.6,0.8,1.1,1.4,1.8,2.5])
+#dep1=np.array([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.1,1.3,1.5,1.8,2.1,2.5])
+nz=len(dep1)
#end
vs1=np.zeros(nz)
mod=np.zeros((nz*ny,nx))
@@ -29,4 +30,4 @@ with open('MOD','w') as fp:
fp.write('%7.3f' % mod[k*ny+j,i])
fp.write('\n')
for i in range(nz):
- print dep1[i],
+ print (dep1[i]),
diff --git a/scripts/plotcross.gmt b/scripts/plotcross.gmt
new file mode 100644
index 0000000..cf494b9
--- /dev/null
+++ b/scripts/plotcross.gmt
@@ -0,0 +1,69 @@
+#!/bin/csh
+
+#-----------------------------------------------------------
+# 2015-06-08 Hongjian Fang
+# step 1: get velicoty along the track from each depth-layer
+# step 2: combine tracks into 2D profile
+#-----------------------------------------------------------
+
+# change the following parameters according to your case
+# start
+set inp3D = DSurfTomo.inMeasure.dat
+set pstart = 121.4/25.1 #start point for 2D profile
+set pend = 121.55/25.1 #end point for 2D profile
+set pstart2 = 121.5/25.0 #start point for 2D profile
+set pend2 = 121.5/25.14 #end point for 2D profile
+set ddint = 0.017/0.015 #grid interval
+# over
+set diInt = 0.5 #distance interval (km)
+set J = -JX6.0i/-1.5i #size for plot
+set cpt = slice.cpt
+set ps = figcross.ps
+gmt makecpt -Cjet -I -T0.5/2.5/0.1 > $cpt #velocity boundary
+
+# start
+set pro2d = tmp.profile.xzv
+set RMAP = `gmt gmtinfo -C $inp3D | awk '{print "-R"$1"/"$2"/"$3"/"$4}'`
+rm -fr $pro2d
+
+foreach layer(`awk '{print $3}' $inp3D| uniq`)
+ set llayer = `echo $layer | awk '{printf "%04d",$1*1000}'`
+ awk '{if($3==j) print $1,$2,$4}' j=$layer $inp3D |gmt surface -Gtmp.grd $RMAP -I$ddint -T0
+ # be careful -I: grid spacing for x/y
+ gmt project -C$pstart -E$pend -G$diInt -Q > track.dat
+ gmt grdtrack track.dat -Gtmp.grd | awk '{print $3,j,$4}' j=$layer > tmp.D$llayer
+ cat tmp.D$llayer >> $pro2d
+end
+#
+set RMAP = `gmt gmtinfo -C $pro2d | awk '{print "-R"$1"/"$2"/"$3"/"$4}'`
+gmt surface $pro2d -Gtmp.grd $RMAP -I$ddint -T0
+#gmt grdfilter tmp.grd -D0 -Fg4 -Gtmpf.grd
+gmt grdimage tmp.grd $J $RMAP -Bf2.5a5:'Distance (km)':/f0.5a1:'Depth (km)':SenW -C$cpt -K -Y5.2i> $ps
+#gmt psscale -Cslice.cpt -Ba0.5f0.25:'Vs': -D2.5i/-0.7i/6.00/0.2h -O -K -P >> $ps
+
+rm -fr tmp.D* tmp.grd tmp.profile.xzv track.dat
+
+# start
+set pro2d = tmp.profile.xzv
+set RMAP = `gmt gmtinfo -C $inp3D | awk '{print "-R"$1"/"$2"/"$3"/"$4}'`
+rm -fr $pro2d
+
+foreach layer(`awk '{print $3}' $inp3D| uniq`)
+ set llayer = `echo $layer | awk '{printf "%04d",$1*1000}'`
+ awk '{if($3==j) print $1,$2,$4}' j=$layer $inp3D |gmt surface -Gtmp.grd $RMAP -I$ddint -T0
+ # be careful -I: grid spacing for x/y
+ gmt project -C$pstart2 -E$pend2 -G$diInt -Q > track.dat
+ gmt grdtrack track.dat -Gtmp.grd | awk '{print $3,j,$4}' j=$layer > tmp.D$llayer
+ cat tmp.D$llayer >> $pro2d
+end
+#
+set RMAP = `gmt gmtinfo -C $pro2d | awk '{print "-R"$1"/"$2"/"$3"/"$4}'`
+gmt surface $pro2d -Gtmp.grd $RMAP -I$ddint -T0
+#gmt grdfilter tmp.grd -D0 -Fg4 -Gtmpf.grd
+gmt grdimage tmp.grd $J $RMAP -Bf2.5a5:'Distance (km)':/f0.5a1:'Depth (km)':SenW -C$cpt -K -O -Y-2.0i>> $ps
+gmt psscale -Cslice.cpt -Ba0.5f0.25:'Vs': -D2.5i/-0.7i/6.00/0.2h -O -P >> $ps
+
+rm -fr tmp.D* tmp.grd tmp.profile.xzv track.dat
+p
+ps2pdf $ps
+
diff --git a/scripts/plotslice_std.gmt b/scripts/plotslice_std.gmt
new file mode 100644
index 0000000..bcbc164
--- /dev/null
+++ b/scripts/plotslice_std.gmt
@@ -0,0 +1,48 @@
+#!/bin/csh
+
+#-----------------------------------------------------------
+# 2015-06-08 Hongjian Fang
+# how to run:
+# ./plotslice.gmt ModelFile depth1 depth2 depth3 depth4, e.g.
+# csh plotslice.gmt SurfTomo.in.tvMeasure.dat 0.2 0.4 0.8 1.4
+#-----------------------------------------------------------
+
+gmt set FONT_ANNOT_PRIMARY 12
+gmt set FONT_LABEL 12
+#gmt set FRAME_WIDTH=0.1c
+#gmt set LABEL_OFFSET=0.1c
+#gmt set LABEL_FONT_SIZE=5p
+#gmt set TICK_LENGTH=0.1c
+#gmt set TICK_PEN=0.3p
+set inp3D = $1
+set ps = horizVsnew.inv_std.ps
+set J = -JM2i #size for plot
+set cpt = slice.cpt
+
+# start
+gmt makecpt -Cgray -D -I -T0.0/0.5/0.05 > $cpt #velocity boundary
+set RMAP = `gmt gmtinfo -C $inp3D | awk '{print "-R"$1"/"$2"/"$3"/"$4}'`
+gmt psbasemap $RMAP $J -Ba0.1f0.05Wsen -P -K -Y5i >$ps
+awk '{if($3==depth1) print $1,$2,$4}' depth1=$2 $inp3D|gmt xyz2grd -R -I0.017/0.015 -Gtmp.grd
+gmt grdimage tmp.grd $J $RMAP -BSenW -E100 -C$cpt -O -K >> $ps
+rm -rf tmp.grd
+#gmt psscale -Cslice.cpt -Ba0.1f0.05:'S velocity (km/s)': -D1.0i/-0.15i/3.00/0.2h -O -K -P >> $ps
+
+#gmt makecpt -Cseis -D -T0.8/1.8/0.1 > $cpt #velocity boundary
+gmt psbasemap $RMAP $J -Ba0.1f0.05nwse -P -K -O -X2.3i >>$ps
+awk '{if($3==depth2) print $1,$2,$4}' depth2=$3 $inp3D|gmt xyz2grd -R -I0.017/0.015 -Gtmp.grd
+gmt grdimage tmp.grd $J $RMAP -BSenW -E100 -C$cpt -O -K >> $ps
+#gmt psscale -Cslice.cpt -Ba0.2f0.1:'S velocity (km/s)': -D1.0i/-0.15i/3.00/0.2h -O -K -P >> $ps
+
+#gmt makecpt -Cseis -D -T1.1/2.0/0.1 > $cpt #velocity boundary
+gmt psbasemap $RMAP $J -Ba0.1f0.05WneS -P -K -O -Y-2.3i -X-2.3i >>$ps
+awk '{if($3==depth2) print $1,$2,$4}' depth2=$4 $inp3D|gmt xyz2grd -R -I0.017/0.015 -Gtmp.grd
+gmt grdimage tmp.grd $J $RMAP -BSenW -E100 -C$cpt -O -K >> $ps
+#gmt psscale -Cslice.cpt -Ba0.2f0.1:'S velocity (km/s)': -D1.0i/-0.25i/3.00/0.2h -O -K -P >> $ps
+
+#gmt makecpt -Cseis -D -T1.3/2.4/0.1 > $cpt #velocity boundary
+gmt psbasemap $RMAP $J -Ba0.1f0.05Swne -P -K -O -X2.3i >>$ps
+awk '{if($3==depth2) print $1,$2,$4}' depth2=$5 $inp3D|gmt xyz2grd -R -I0.017/0.015 -Gtmp.grd
+gmt grdimage tmp.grd $J $RMAP -BSenW -E100 -C$cpt -O -K >> $ps
+gmt psscale -Cslice.cpt -Ba0.2f0.1:'Vs std (km/s)': -D1.0i/-0.35i/3.00/0.2h -O -P >> $ps
+
diff --git a/src/Makefile b/src/Makefile
index 06cdfb2..077ff65 100644
--- a/src/Makefile
+++ b/src/Makefile
@@ -1,17 +1,19 @@
CMD = DSurfTomo
FC = gfortran
FFLAGS = -O3 -ffixed-line-length-none -ffloat-store\
- -W -fbounds-check -m64 -mcmodel=medium
+ -fbounds-check -m64 -mcmodel=medium
F90SRCS = lsmrDataModule.f90 lsmrblasInterface.f90\
lsmrblas.f90 lsmrModule.f90 delsph.f90\
- aprod.f90 gaussian.f90 main.f90
-FSRCS = surfdisp96.f
-OBJS = $(F90SRCS:%.f90=%.o) $(FSRCS:%.f=%.o) CalSurfG.o
+ aprod.f90 gaussian.f90 voronoiproj.f90
+FSRCS = surfdisp96.f slarnv.f slaruv.f
+OBJS = $(F90SRCS:%.f90=%.o) $(FSRCS:%.f=%.o) CalSurfG.o main.o
all:$(CMD)
$(CMD):$(OBJS)
$(FC) -fopenmp $^ -o $@
CalSurfG.o:CalSurfG.f90
$(FC) -fopenmp $(FFLAGS) -c $< -o $@
+main.o:main.f90
+ $(FC) -fopenmp $(FFLAGS) -c $< -o $@
%.o: %.f90
$(FC) $(FFLAGS) -c $(@F:.o=.f90) -o $@
%.o: %.f
diff --git a/src/main.f90 b/src/main.f90
index 240b461..d716830 100644
--- a/src/main.f90
+++ b/src/main.f90
@@ -20,6 +20,7 @@
program SurfTomo
use lsmrModule, only:lsmr
use lsmrblasInterface, only : dnrm2
+ use omp_lib
implicit none
! VARIABLE DEFINE
@@ -63,7 +64,8 @@ program SurfTomo
integer,dimension(:,:),allocatable::periods
real,dimension(:),allocatable::rw
integer,dimension(:),allocatable::iw,col
- real,dimension(:),allocatable::dv,norm
+ real,dimension(:),allocatable::dv,norm,dvsub,dvstd,dvall
+! real,dimension(:),allocatable::dvall
real,dimension(:,:,:),allocatable::vsf
real,dimension(:,:,:),allocatable::vsftrue
character strf
@@ -100,6 +102,9 @@ program SurfTomo
real maxnorm
real threshold0
+ !For Poisson Voronoi inverison
+ integer iproj,vorotomo,ncells,nrealizations,idx
+ real hvratio
! OPEN FILES FIRST TO OUTPUT THE PROCESS
nout=36
@@ -107,9 +112,11 @@ program SurfTomo
! OUTPUT PROGRAM INFOMATION
write(*,*)
- write(*,*),' DSurfTomo (v1.3)'
- write(*,*),'PLEASE contact Hongjain Fang &
- (fanghj@mail.ustc.edu.cn) if you find any bug'
+ write(*,*) ' DSurfTomo (v2.0)'
+ !write(*,*) 'PLEASE contact Hongjain Fang &
+ ! (fanghj@mail.ustc.edu.cn) if you find any bug'
+ write(*,*) 'For bug report, PLEASE contact Hongjain Fang &
+ (fanghj1990@gmail.com)'
write(*,*)
! READ INPUT FILE
@@ -151,8 +158,8 @@ program SurfTomo
write(logfile,'(a,a)')trim(inputfile),'.log'
open(66,file=logfile)
write(66,*)
- write(66,*),' S U R F T O M O'
- write(66,*),'PLEASE contact Hongjain Fang &
+ write(66,*)' S U R F T O M O'
+ write(66,*)'PLEASE contact Hongjain Fang &
(fanghj@mail.ustc.edu.cn) if you find any bug'
write(66,*)
write(66,*) 'model origin:latitude,longitue'
@@ -204,6 +211,8 @@ program SurfTomo
read(10,*)ifsyn
read(10,*)noiselevel
read(10,*) threshold0
+
+ read(10,*) vorotomo,ncells,nrealizations!,hvratio
close(10)
nrc=nsrc
kmax=kmaxRc+kmaxRg+kmaxLc+kmaxLg
@@ -281,7 +290,8 @@ program SurfTomo
allocate(depz(nz), stat=checkstat)
maxnar = spfra*dall*nx*ny*nz!sparsity fraction
maxvp = (nx-2)*(ny-2)*(nz-1)
- allocate(dv(maxvp), stat=checkstat)
+ allocate(dv(maxvp),dvsub(maxvp),dvstd(maxvp),dvall(maxvp*nrealizations), stat=checkstat)
+! allocate(dvall(maxvp*nrealizations),stats=checkstat)
allocate(norm(maxvp), stat=checkstat)
allocate(vsf(nx,ny,nz), stat=checkstat)
allocate(vsftrue(nx,ny,nz), stat=checkstat)
@@ -302,7 +312,7 @@ program SurfTomo
stat=checkstat)
! MEASUREMENTS STATISTICS AND READ INITIAL MODEL
- write(*,'(a,i7)') 'Number of all measurements',dall
+ write(*,'(a,i7)') ' Number of all measurements',dall
open(10,file='MOD',status='old')
read(10,*) (depz(i),i=1,nz)
@@ -317,7 +327,7 @@ program SurfTomo
! CHECKERBOARD TEST
if (ifsyn == 1) then
- write(*,*) 'Checkerboard Resolution Test Begin'
+ write(*,*) 'Synthetic Test Begin'
vsftrue = vsf
open(11,file='MOD.true',status='old')
@@ -397,6 +407,36 @@ program SurfTomo
! ADDING REGULARIZATION TERM
+ if (vorotomo /= 0) then
+
+ hvratio = dvxd*(nx-3)*111.19/depz(nz-1)
+ dv = 0
+ dvstd = 0
+ leniw = 2*nar+1
+ lenrw = nar
+ iw(1)=nar
+ iw(nar+2:2*nar+1) = col(1:nar)
+ !$omp parallel &
+ !$omp default(private) &
+ !$omp shared(leniw,lenrw,iw,rw,cbst,goxd,gozd,dvxd,dvzd,depz,maxvp) &
+ !$omp shared(nx,ny,nz,dall,ncells,hvratio,damp,nrealizations,dvall)
+ !$omp do
+ do iproj = 1,nrealizations
+ call voronoiproj(leniw,lenrw,iw,rw,cbst,goxd,dvxd,gozd,dvzd,depz,&
+ nx,ny,nz,dall,ncells,hvratio,damp,iproj,dvsub)
+ dvall((iproj-1)*maxvp+1:iproj*maxvp) = dvsub(1:maxvp)
+ enddo
+ !$omp end do
+ !$omp end parallel
+ do iproj = 1,nrealizations
+ dvsub = dvall((iproj-1)*maxvp+1:iproj*maxvp)!:,iproj)
+ dv = dv+dvsub
+ dvstd = dvstd+dvsub**2
+ enddo
+ dv = dv/nrealizations
+ dvstd = sqrt(dvstd/nrealizations-dv**2)
+ else
+
weight=weight0
nar_tmp=nar
nars=0
@@ -472,11 +512,13 @@ program SurfTomo
atol, btol, conlim, itnlim, localSize, nout,&
dv, istop, itn, anorm, acond, rnorm, arnorm, xnorm)
+ endif ! end vorotomo
+
mean = sum(cbst(1:dall))/dall
std_devs = sqrt(sum(cbst(1:dall)**2)/dall - mean**2)
- write(*,'(i2,a)'),iter,'th iteration...'
- write(*,'(a,f7.3)'),'weight is:',weight
- write(*,'(a,f8.1,a,f8.2,a,f8.3)'),'mean,std_devs and rms of &
+ write(*,'(i2,a)')iter,'th iteration...'
+! write(*,'(a,f7.3)')'weight is:',weight
+ write(*,'(a,f8.1,a,f8.2,a,f8.3)')' mean,std_devs and rms of &
residual after weighting: ',mean*1000,'ms ',1000*std_devs,'ms ',&
dnrm2(dall,cbst,1)/sqrt(real(dall))
@@ -486,17 +528,17 @@ program SurfTomo
mean = sum(cbst(1:dall))/dall
std_devs = sqrt(sum(cbst(1:dall)**2)/dall - mean**2)
- write(*,'(a,f8.1,a,f8.2,a,f8.3)'),'residual before weighting: ',mean*1000,'ms ',1000*std_devs,'ms ',&
+ write(*,'(a,f8.1,a,f8.2,a,f8.3)')' residual before weighting: ',mean*1000,'ms ',1000*std_devs,'ms ',&
dnrm2(dall,cbst,1)/sqrt(real(dall))
- write(66,'(i2,a)'),iter,'th iteration...'
- write(66,'(a,f7.3)'),'weight is:',weight
- write(66,'(a,f8.1,a,f8.2,a,f8.3)'),'mean,std_devs and rms of &
+ write(66,'(i2,a)')iter,'th iteration...'
+! write(66,'(a,f7.3)')'weight is:',weight
+ write(66,'(a,f8.1,a,f8.2,a,f8.3)')'mean,std_devs and rms of &
residual: ',mean*1000,'ms ',1000*std_devs,'ms ',&
dnrm2(dall,cbst,1)/sqrt(real(dall))
- write(*,'(a,2f7.4)'),'min and max velocity variation ',&
+ write(*,'(a,2f7.4)')' min and max velocity variation ',&
minval(dv),maxval(dv)
- write(66,'(a,2f7.4)'),'min and max velocity variation ',&
+ write(66,'(a,2f7.4)')'min and max velocity variation ',&
minval(dv),maxval(dv)
do k=1,nz-1
@@ -531,8 +573,8 @@ program SurfTomo
! OUTPUT THE VELOCITY MODEL
- write(*,*),'Program finishes successfully'
- write(66,*),'Program finishes successfully'
+ write(*,*)'Program finishes successfully'
+ write(66,*)'Program finishes successfully'
if(ifsyn == 1) then
open(65,file='Vs_model.real')
@@ -548,13 +590,13 @@ program SurfTomo
enddo
close(65)
close(63)
- write(*,*),'Output True velocity model &
+ write(*,*)'Output True velocity model &
to Vs_model.real'
- write(*,*),'Output inverted shear velocity model &
+ write(*,*)'Output inverted shear velocity model &
to ',outsyn
- write(66,*),'Output True velocity model &
+ write(66,*)'Output True velocity model &
to Vs_model.real'
- write(66,*),'Output inverted shear velocity model &
+ write(66,*)'Output inverted shear velocity model &
to ',outsyn
else
write(outmodel,'(a,a)') trim(inputfile),'Measure.dat'
@@ -567,10 +609,23 @@ program SurfTomo
enddo
enddo
close(64)
- write(*,*),'Output inverted shear velocity model &
+ write(*,*)'Output inverted shear velocity model &
to ',outmodel
- write(66,*),'Output inverted shear velocity model &
+ write(66,*)'Output inverted shear velocity model &
to ',outmodel
+
+ write(outmodel,'(a,a)') trim(inputfile),'Measure_std.dat'
+ open(64,file=outmodel)
+ do k=1,nz-1
+ do j=1,ny-2
+ do i=1,nx-2
+ idx = (k-1)*(nx-2)*(ny-2)+(j-1)*(nx-2)+i
+ write(64,'(5f9.3)') gozd+(j-1)*dvzd,goxd-(i-1)*dvxd,depz(k),dvstd(idx)
+ enddo
+ enddo
+ enddo
+ close(64)
+
endif
close(40)
@@ -588,7 +643,7 @@ program SurfTomo
deallocate(rw)
deallocate(iw,col)
deallocate(cbst,wt,dtres,datweight)
- deallocate(dv)
+ deallocate(dv,dvsub,dvstd,dvall)
deallocate(norm)
deallocate(vsf)
deallocate(vsftrue)
diff --git a/src/slarnv.f b/src/slarnv.f
new file mode 100644
index 0000000..44fdeb9
--- /dev/null
+++ b/src/slarnv.f
@@ -0,0 +1,178 @@
+*> \brief \b SLARNV returns a vector of random numbers from a uniform or normal distribution.
+*
+* =========== DOCUMENTATION ===========
+*
+* Online html documentation available at
+* http://www.netlib.org/lapack/explore-html/
+*
+*> \htmlonly
+*> Download SLARNV + dependencies
+*>
+*> [TGZ]
+*>
+*> [ZIP]
+*>
+*> [TXT]
+*> \endhtmlonly
+*
+* Definition:
+* ===========
+*
+* SUBROUTINE SLARNV( IDIST, ISEED, N, X )
+*
+* .. Scalar Arguments ..
+* INTEGER IDIST, N
+* ..
+* .. Array Arguments ..
+* INTEGER ISEED( 4 )
+* REAL X( * )
+* ..
+*
+*
+*> \par Purpose:
+* =============
+*>
+*> \verbatim
+*>
+*> SLARNV returns a vector of n random real numbers from a uniform or
+*> normal distribution.
+*> \endverbatim
+*
+* Arguments:
+* ==========
+*
+*> \param[in] IDIST
+*> \verbatim
+*> IDIST is INTEGER
+*> Specifies the distribution of the random numbers:
+*> = 1: uniform (0,1)
+*> = 2: uniform (-1,1)
+*> = 3: normal (0,1)
+*> \endverbatim
+*>
+*> \param[in,out] ISEED
+*> \verbatim
+*> ISEED is INTEGER array, dimension (4)
+*> On entry, the seed of the random number generator; the array
+*> elements must be between 0 and 4095, and ISEED(4) must be
+*> odd.
+*> On exit, the seed is updated.
+*> \endverbatim
+*>
+*> \param[in] N
+*> \verbatim
+*> N is INTEGER
+*> The number of random numbers to be generated.
+*> \endverbatim
+*>
+*> \param[out] X
+*> \verbatim
+*> X is REAL array, dimension (N)
+*> The generated random numbers.
+*> \endverbatim
+*
+* Authors:
+* ========
+*
+*> \author Univ. of Tennessee
+*> \author Univ. of California Berkeley
+*> \author Univ. of Colorado Denver
+*> \author NAG Ltd.
+*
+*> \date December 2016
+*
+*> \ingroup OTHERauxiliary
+*
+*> \par Further Details:
+* =====================
+*>
+*> \verbatim
+*>
+*> This routine calls the auxiliary routine SLARUV to generate random
+*> real numbers from a uniform (0,1) distribution, in batches of up to
+*> 128 using vectorisable code. The Box-Muller method is used to
+*> transform numbers from a uniform to a normal distribution.
+*> \endverbatim
+*>
+* =====================================================================
+ SUBROUTINE SLARNV( IDIST, ISEED, N, X )
+*
+* -- LAPACK auxiliary routine (version 3.7.0) --
+* -- LAPACK is a software package provided by Univ. of Tennessee, --
+* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
+* December 2016
+*
+* .. Scalar Arguments ..
+ INTEGER IDIST, N
+* ..
+* .. Array Arguments ..
+ INTEGER ISEED( 4 )
+ REAL X( * )
+* ..
+*
+* =====================================================================
+*
+* .. Parameters ..
+ REAL ONE, TWO
+ PARAMETER ( ONE = 1.0E+0, TWO = 2.0E+0 )
+ INTEGER LV
+ PARAMETER ( LV = 128 )
+ REAL TWOPI
+ PARAMETER ( TWOPI = 6.2831853071795864769252867663E+0 )
+* ..
+* .. Local Scalars ..
+ INTEGER I, IL, IL2, IV
+* ..
+* .. Local Arrays ..
+ REAL U( LV )
+* ..
+* .. Intrinsic Functions ..
+ INTRINSIC COS, LOG, MIN, SQRT
+* ..
+* .. External Subroutines ..
+ EXTERNAL SLARUV
+* ..
+* .. Executable Statements ..
+*
+ DO 40 IV = 1, N, LV / 2
+ IL = MIN( LV / 2, N-IV+1 )
+ IF( IDIST.EQ.3 ) THEN
+ IL2 = 2*IL
+ ELSE
+ IL2 = IL
+ END IF
+*
+* Call SLARUV to generate IL2 numbers from a uniform (0,1)
+* distribution (IL2 <= LV)
+*
+ CALL SLARUV( ISEED, IL2, U )
+*
+ IF( IDIST.EQ.1 ) THEN
+*
+* Copy generated numbers
+*
+ DO 10 I = 1, IL
+ X( IV+I-1 ) = U( I )
+ 10 CONTINUE
+ ELSE IF( IDIST.EQ.2 ) THEN
+*
+* Convert generated numbers to uniform (-1,1) distribution
+*
+ DO 20 I = 1, IL
+ X( IV+I-1 ) = TWO*U( I ) - ONE
+ 20 CONTINUE
+ ELSE IF( IDIST.EQ.3 ) THEN
+*
+* Convert generated numbers to normal (0,1) distribution
+*
+ DO 30 I = 1, IL
+ X( IV+I-1 ) = SQRT( -TWO*LOG( U( 2*I-1 ) ) )*
+ $ COS( TWOPI*U( 2*I ) )
+ 30 CONTINUE
+ END IF
+ 40 CONTINUE
+ RETURN
+*
+* End of SLARNV
+*
+ END
diff --git a/src/slaruv.f b/src/slaruv.f
new file mode 100644
index 0000000..ed84200
--- /dev/null
+++ b/src/slaruv.f
@@ -0,0 +1,447 @@
+*> \brief \b SLARUV returns a vector of n random real numbers from a uniform distribution.
+*
+* =========== DOCUMENTATION ===========
+*
+* Online html documentation available at
+* http://www.netlib.org/lapack/explore-html/
+*
+*> \htmlonly
+*> Download SLARUV + dependencies
+*>
+*> [TGZ]
+*>
+*> [ZIP]
+*>
+*> [TXT]
+*> \endhtmlonly
+*
+* Definition:
+* ===========
+*
+* SUBROUTINE SLARUV( ISEED, N, X )
+*
+* .. Scalar Arguments ..
+* INTEGER N
+* ..
+* .. Array Arguments ..
+* INTEGER ISEED( 4 )
+* REAL X( N )
+* ..
+*
+*
+*> \par Purpose:
+* =============
+*>
+*> \verbatim
+*>
+*> SLARUV returns a vector of n random real numbers from a uniform (0,1)
+*> distribution (n <= 128).
+*>
+*> This is an auxiliary routine called by SLARNV and CLARNV.
+*> \endverbatim
+*
+* Arguments:
+* ==========
+*
+*> \param[in,out] ISEED
+*> \verbatim
+*> ISEED is INTEGER array, dimension (4)
+*> On entry, the seed of the random number generator; the array
+*> elements must be between 0 and 4095, and ISEED(4) must be
+*> odd.
+*> On exit, the seed is updated.
+*> \endverbatim
+*>
+*> \param[in] N
+*> \verbatim
+*> N is INTEGER
+*> The number of random numbers to be generated. N <= 128.
+*> \endverbatim
+*>
+*> \param[out] X
+*> \verbatim
+*> X is REAL array, dimension (N)
+*> The generated random numbers.
+*> \endverbatim
+*
+* Authors:
+* ========
+*
+*> \author Univ. of Tennessee
+*> \author Univ. of California Berkeley
+*> \author Univ. of Colorado Denver
+*> \author NAG Ltd.
+*
+*> \date December 2016
+*
+*> \ingroup OTHERauxiliary
+*
+*> \par Further Details:
+* =====================
+*>
+*> \verbatim
+*>
+*> This routine uses a multiplicative congruential method with modulus
+*> 2**48 and multiplier 33952834046453 (see G.S.Fishman,
+*> 'Multiplicative congruential random number generators with modulus
+*> 2**b: an exhaustive analysis for b = 32 and a partial analysis for
+*> b = 48', Math. Comp. 189, pp 331-344, 1990).
+*>
+*> 48-bit integers are stored in 4 integer array elements with 12 bits
+*> per element. Hence the routine is portable across machines with
+*> integers of 32 bits or more.
+*> \endverbatim
+*>
+* =====================================================================
+ SUBROUTINE SLARUV( ISEED, N, X )
+*
+* -- LAPACK auxiliary routine (version 3.7.0) --
+* -- LAPACK is a software package provided by Univ. of Tennessee, --
+* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
+* December 2016
+*
+* .. Scalar Arguments ..
+ INTEGER N
+* ..
+* .. Array Arguments ..
+ INTEGER ISEED( 4 )
+ REAL X( N )
+* ..
+*
+* =====================================================================
+*
+* .. Parameters ..
+ REAL ONE
+ PARAMETER ( ONE = 1.0E0 )
+ INTEGER LV, IPW2
+ REAL R
+ PARAMETER ( LV = 128, IPW2 = 4096, R = ONE / IPW2 )
+* ..
+* .. Local Scalars ..
+ INTEGER I, I1, I2, I3, I4, IT1, IT2, IT3, IT4, J
+* ..
+* .. Local Arrays ..
+ INTEGER MM( LV, 4 )
+* ..
+* .. Intrinsic Functions ..
+ INTRINSIC MIN, MOD, REAL
+* ..
+* .. Data statements ..
+ DATA ( MM( 1, J ), J = 1, 4 ) / 494, 322, 2508,
+ $ 2549 /
+ DATA ( MM( 2, J ), J = 1, 4 ) / 2637, 789, 3754,
+ $ 1145 /
+ DATA ( MM( 3, J ), J = 1, 4 ) / 255, 1440, 1766,
+ $ 2253 /
+ DATA ( MM( 4, J ), J = 1, 4 ) / 2008, 752, 3572,
+ $ 305 /
+ DATA ( MM( 5, J ), J = 1, 4 ) / 1253, 2859, 2893,
+ $ 3301 /
+ DATA ( MM( 6, J ), J = 1, 4 ) / 3344, 123, 307,
+ $ 1065 /
+ DATA ( MM( 7, J ), J = 1, 4 ) / 4084, 1848, 1297,
+ $ 3133 /
+ DATA ( MM( 8, J ), J = 1, 4 ) / 1739, 643, 3966,
+ $ 2913 /
+ DATA ( MM( 9, J ), J = 1, 4 ) / 3143, 2405, 758,
+ $ 3285 /
+ DATA ( MM( 10, J ), J = 1, 4 ) / 3468, 2638, 2598,
+ $ 1241 /
+ DATA ( MM( 11, J ), J = 1, 4 ) / 688, 2344, 3406,
+ $ 1197 /
+ DATA ( MM( 12, J ), J = 1, 4 ) / 1657, 46, 2922,
+ $ 3729 /
+ DATA ( MM( 13, J ), J = 1, 4 ) / 1238, 3814, 1038,
+ $ 2501 /
+ DATA ( MM( 14, J ), J = 1, 4 ) / 3166, 913, 2934,
+ $ 1673 /
+ DATA ( MM( 15, J ), J = 1, 4 ) / 1292, 3649, 2091,
+ $ 541 /
+ DATA ( MM( 16, J ), J = 1, 4 ) / 3422, 339, 2451,
+ $ 2753 /
+ DATA ( MM( 17, J ), J = 1, 4 ) / 1270, 3808, 1580,
+ $ 949 /
+ DATA ( MM( 18, J ), J = 1, 4 ) / 2016, 822, 1958,
+ $ 2361 /
+ DATA ( MM( 19, J ), J = 1, 4 ) / 154, 2832, 2055,
+ $ 1165 /
+ DATA ( MM( 20, J ), J = 1, 4 ) / 2862, 3078, 1507,
+ $ 4081 /
+ DATA ( MM( 21, J ), J = 1, 4 ) / 697, 3633, 1078,
+ $ 2725 /
+ DATA ( MM( 22, J ), J = 1, 4 ) / 1706, 2970, 3273,
+ $ 3305 /
+ DATA ( MM( 23, J ), J = 1, 4 ) / 491, 637, 17,
+ $ 3069 /
+ DATA ( MM( 24, J ), J = 1, 4 ) / 931, 2249, 854,
+ $ 3617 /
+ DATA ( MM( 25, J ), J = 1, 4 ) / 1444, 2081, 2916,
+ $ 3733 /
+ DATA ( MM( 26, J ), J = 1, 4 ) / 444, 4019, 3971,
+ $ 409 /
+ DATA ( MM( 27, J ), J = 1, 4 ) / 3577, 1478, 2889,
+ $ 2157 /
+ DATA ( MM( 28, J ), J = 1, 4 ) / 3944, 242, 3831,
+ $ 1361 /
+ DATA ( MM( 29, J ), J = 1, 4 ) / 2184, 481, 2621,
+ $ 3973 /
+ DATA ( MM( 30, J ), J = 1, 4 ) / 1661, 2075, 1541,
+ $ 1865 /
+ DATA ( MM( 31, J ), J = 1, 4 ) / 3482, 4058, 893,
+ $ 2525 /
+ DATA ( MM( 32, J ), J = 1, 4 ) / 657, 622, 736,
+ $ 1409 /
+ DATA ( MM( 33, J ), J = 1, 4 ) / 3023, 3376, 3992,
+ $ 3445 /
+ DATA ( MM( 34, J ), J = 1, 4 ) / 3618, 812, 787,
+ $ 3577 /
+ DATA ( MM( 35, J ), J = 1, 4 ) / 1267, 234, 2125,
+ $ 77 /
+ DATA ( MM( 36, J ), J = 1, 4 ) / 1828, 641, 2364,
+ $ 3761 /
+ DATA ( MM( 37, J ), J = 1, 4 ) / 164, 4005, 2460,
+ $ 2149 /
+ DATA ( MM( 38, J ), J = 1, 4 ) / 3798, 1122, 257,
+ $ 1449 /
+ DATA ( MM( 39, J ), J = 1, 4 ) / 3087, 3135, 1574,
+ $ 3005 /
+ DATA ( MM( 40, J ), J = 1, 4 ) / 2400, 2640, 3912,
+ $ 225 /
+ DATA ( MM( 41, J ), J = 1, 4 ) / 2870, 2302, 1216,
+ $ 85 /
+ DATA ( MM( 42, J ), J = 1, 4 ) / 3876, 40, 3248,
+ $ 3673 /
+ DATA ( MM( 43, J ), J = 1, 4 ) / 1905, 1832, 3401,
+ $ 3117 /
+ DATA ( MM( 44, J ), J = 1, 4 ) / 1593, 2247, 2124,
+ $ 3089 /
+ DATA ( MM( 45, J ), J = 1, 4 ) / 1797, 2034, 2762,
+ $ 1349 /
+ DATA ( MM( 46, J ), J = 1, 4 ) / 1234, 2637, 149,
+ $ 2057 /
+ DATA ( MM( 47, J ), J = 1, 4 ) / 3460, 1287, 2245,
+ $ 413 /
+ DATA ( MM( 48, J ), J = 1, 4 ) / 328, 1691, 166,
+ $ 65 /
+ DATA ( MM( 49, J ), J = 1, 4 ) / 2861, 496, 466,
+ $ 1845 /
+ DATA ( MM( 50, J ), J = 1, 4 ) / 1950, 1597, 4018,
+ $ 697 /
+ DATA ( MM( 51, J ), J = 1, 4 ) / 617, 2394, 1399,
+ $ 3085 /
+ DATA ( MM( 52, J ), J = 1, 4 ) / 2070, 2584, 190,
+ $ 3441 /
+ DATA ( MM( 53, J ), J = 1, 4 ) / 3331, 1843, 2879,
+ $ 1573 /
+ DATA ( MM( 54, J ), J = 1, 4 ) / 769, 336, 153,
+ $ 3689 /
+ DATA ( MM( 55, J ), J = 1, 4 ) / 1558, 1472, 2320,
+ $ 2941 /
+ DATA ( MM( 56, J ), J = 1, 4 ) / 2412, 2407, 18,
+ $ 929 /
+ DATA ( MM( 57, J ), J = 1, 4 ) / 2800, 433, 712,
+ $ 533 /
+ DATA ( MM( 58, J ), J = 1, 4 ) / 189, 2096, 2159,
+ $ 2841 /
+ DATA ( MM( 59, J ), J = 1, 4 ) / 287, 1761, 2318,
+ $ 4077 /
+ DATA ( MM( 60, J ), J = 1, 4 ) / 2045, 2810, 2091,
+ $ 721 /
+ DATA ( MM( 61, J ), J = 1, 4 ) / 1227, 566, 3443,
+ $ 2821 /
+ DATA ( MM( 62, J ), J = 1, 4 ) / 2838, 442, 1510,
+ $ 2249 /
+ DATA ( MM( 63, J ), J = 1, 4 ) / 209, 41, 449,
+ $ 2397 /
+ DATA ( MM( 64, J ), J = 1, 4 ) / 2770, 1238, 1956,
+ $ 2817 /
+ DATA ( MM( 65, J ), J = 1, 4 ) / 3654, 1086, 2201,
+ $ 245 /
+ DATA ( MM( 66, J ), J = 1, 4 ) / 3993, 603, 3137,
+ $ 1913 /
+ DATA ( MM( 67, J ), J = 1, 4 ) / 192, 840, 3399,
+ $ 1997 /
+ DATA ( MM( 68, J ), J = 1, 4 ) / 2253, 3168, 1321,
+ $ 3121 /
+ DATA ( MM( 69, J ), J = 1, 4 ) / 3491, 1499, 2271,
+ $ 997 /
+ DATA ( MM( 70, J ), J = 1, 4 ) / 2889, 1084, 3667,
+ $ 1833 /
+ DATA ( MM( 71, J ), J = 1, 4 ) / 2857, 3438, 2703,
+ $ 2877 /
+ DATA ( MM( 72, J ), J = 1, 4 ) / 2094, 2408, 629,
+ $ 1633 /
+ DATA ( MM( 73, J ), J = 1, 4 ) / 1818, 1589, 2365,
+ $ 981 /
+ DATA ( MM( 74, J ), J = 1, 4 ) / 688, 2391, 2431,
+ $ 2009 /
+ DATA ( MM( 75, J ), J = 1, 4 ) / 1407, 288, 1113,
+ $ 941 /
+ DATA ( MM( 76, J ), J = 1, 4 ) / 634, 26, 3922,
+ $ 2449 /
+ DATA ( MM( 77, J ), J = 1, 4 ) / 3231, 512, 2554,
+ $ 197 /
+ DATA ( MM( 78, J ), J = 1, 4 ) / 815, 1456, 184,
+ $ 2441 /
+ DATA ( MM( 79, J ), J = 1, 4 ) / 3524, 171, 2099,
+ $ 285 /
+ DATA ( MM( 80, J ), J = 1, 4 ) / 1914, 1677, 3228,
+ $ 1473 /
+ DATA ( MM( 81, J ), J = 1, 4 ) / 516, 2657, 4012,
+ $ 2741 /
+ DATA ( MM( 82, J ), J = 1, 4 ) / 164, 2270, 1921,
+ $ 3129 /
+ DATA ( MM( 83, J ), J = 1, 4 ) / 303, 2587, 3452,
+ $ 909 /
+ DATA ( MM( 84, J ), J = 1, 4 ) / 2144, 2961, 3901,
+ $ 2801 /
+ DATA ( MM( 85, J ), J = 1, 4 ) / 3480, 1970, 572,
+ $ 421 /
+ DATA ( MM( 86, J ), J = 1, 4 ) / 119, 1817, 3309,
+ $ 4073 /
+ DATA ( MM( 87, J ), J = 1, 4 ) / 3357, 676, 3171,
+ $ 2813 /
+ DATA ( MM( 88, J ), J = 1, 4 ) / 837, 1410, 817,
+ $ 2337 /
+ DATA ( MM( 89, J ), J = 1, 4 ) / 2826, 3723, 3039,
+ $ 1429 /
+ DATA ( MM( 90, J ), J = 1, 4 ) / 2332, 2803, 1696,
+ $ 1177 /
+ DATA ( MM( 91, J ), J = 1, 4 ) / 2089, 3185, 1256,
+ $ 1901 /
+ DATA ( MM( 92, J ), J = 1, 4 ) / 3780, 184, 3715,
+ $ 81 /
+ DATA ( MM( 93, J ), J = 1, 4 ) / 1700, 663, 2077,
+ $ 1669 /
+ DATA ( MM( 94, J ), J = 1, 4 ) / 3712, 499, 3019,
+ $ 2633 /
+ DATA ( MM( 95, J ), J = 1, 4 ) / 150, 3784, 1497,
+ $ 2269 /
+ DATA ( MM( 96, J ), J = 1, 4 ) / 2000, 1631, 1101,
+ $ 129 /
+ DATA ( MM( 97, J ), J = 1, 4 ) / 3375, 1925, 717,
+ $ 1141 /
+ DATA ( MM( 98, J ), J = 1, 4 ) / 1621, 3912, 51,
+ $ 249 /
+ DATA ( MM( 99, J ), J = 1, 4 ) / 3090, 1398, 981,
+ $ 3917 /
+ DATA ( MM( 100, J ), J = 1, 4 ) / 3765, 1349, 1978,
+ $ 2481 /
+ DATA ( MM( 101, J ), J = 1, 4 ) / 1149, 1441, 1813,
+ $ 3941 /
+ DATA ( MM( 102, J ), J = 1, 4 ) / 3146, 2224, 3881,
+ $ 2217 /
+ DATA ( MM( 103, J ), J = 1, 4 ) / 33, 2411, 76,
+ $ 2749 /
+ DATA ( MM( 104, J ), J = 1, 4 ) / 3082, 1907, 3846,
+ $ 3041 /
+ DATA ( MM( 105, J ), J = 1, 4 ) / 2741, 3192, 3694,
+ $ 1877 /
+ DATA ( MM( 106, J ), J = 1, 4 ) / 359, 2786, 1682,
+ $ 345 /
+ DATA ( MM( 107, J ), J = 1, 4 ) / 3316, 382, 124,
+ $ 2861 /
+ DATA ( MM( 108, J ), J = 1, 4 ) / 1749, 37, 1660,
+ $ 1809 /
+ DATA ( MM( 109, J ), J = 1, 4 ) / 185, 759, 3997,
+ $ 3141 /
+ DATA ( MM( 110, J ), J = 1, 4 ) / 2784, 2948, 479,
+ $ 2825 /
+ DATA ( MM( 111, J ), J = 1, 4 ) / 2202, 1862, 1141,
+ $ 157 /
+ DATA ( MM( 112, J ), J = 1, 4 ) / 2199, 3802, 886,
+ $ 2881 /
+ DATA ( MM( 113, J ), J = 1, 4 ) / 1364, 2423, 3514,
+ $ 3637 /
+ DATA ( MM( 114, J ), J = 1, 4 ) / 1244, 2051, 1301,
+ $ 1465 /
+ DATA ( MM( 115, J ), J = 1, 4 ) / 2020, 2295, 3604,
+ $ 2829 /
+ DATA ( MM( 116, J ), J = 1, 4 ) / 3160, 1332, 1888,
+ $ 2161 /
+ DATA ( MM( 117, J ), J = 1, 4 ) / 2785, 1832, 1836,
+ $ 3365 /
+ DATA ( MM( 118, J ), J = 1, 4 ) / 2772, 2405, 1990,
+ $ 361 /
+ DATA ( MM( 119, J ), J = 1, 4 ) / 1217, 3638, 2058,
+ $ 2685 /
+ DATA ( MM( 120, J ), J = 1, 4 ) / 1822, 3661, 692,
+ $ 3745 /
+ DATA ( MM( 121, J ), J = 1, 4 ) / 1245, 327, 1194,
+ $ 2325 /
+ DATA ( MM( 122, J ), J = 1, 4 ) / 2252, 3660, 20,
+ $ 3609 /
+ DATA ( MM( 123, J ), J = 1, 4 ) / 3904, 716, 3285,
+ $ 3821 /
+ DATA ( MM( 124, J ), J = 1, 4 ) / 2774, 1842, 2046,
+ $ 3537 /
+ DATA ( MM( 125, J ), J = 1, 4 ) / 997, 3987, 2107,
+ $ 517 /
+ DATA ( MM( 126, J ), J = 1, 4 ) / 2573, 1368, 3508,
+ $ 3017 /
+ DATA ( MM( 127, J ), J = 1, 4 ) / 1148, 1848, 3525,
+ $ 2141 /
+ DATA ( MM( 128, J ), J = 1, 4 ) / 545, 2366, 3801,
+ $ 1537 /
+* ..
+* .. Executable Statements ..
+*
+ I1 = ISEED( 1 )
+ I2 = ISEED( 2 )
+ I3 = ISEED( 3 )
+ I4 = ISEED( 4 )
+*
+ DO 10 I = 1, MIN( N, LV )
+*
+ 20 CONTINUE
+*
+* Multiply the seed by i-th power of the multiplier modulo 2**48
+*
+ IT4 = I4*MM( I, 4 )
+ IT3 = IT4 / IPW2
+ IT4 = IT4 - IPW2*IT3
+ IT3 = IT3 + I3*MM( I, 4 ) + I4*MM( I, 3 )
+ IT2 = IT3 / IPW2
+ IT3 = IT3 - IPW2*IT2
+ IT2 = IT2 + I2*MM( I, 4 ) + I3*MM( I, 3 ) + I4*MM( I, 2 )
+ IT1 = IT2 / IPW2
+ IT2 = IT2 - IPW2*IT1
+ IT1 = IT1 + I1*MM( I, 4 ) + I2*MM( I, 3 ) + I3*MM( I, 2 ) +
+ $ I4*MM( I, 1 )
+ IT1 = MOD( IT1, IPW2 )
+*
+* Convert 48-bit integer to a real number in the interval (0,1)
+*
+ X( I ) = R*( REAL( IT1 )+R*( REAL( IT2 )+R*( REAL( IT3 )+R*
+ $ REAL( IT4 ) ) ) )
+*
+ IF (X( I ).EQ.1.0) THEN
+* If a real number has n bits of precision, and the first
+* n bits of the 48-bit integer above happen to be all 1 (which
+* will occur about once every 2**n calls), then X( I ) will
+* be rounded to exactly 1.0. In IEEE single precision arithmetic,
+* this will happen relatively often since n = 24.
+* Since X( I ) is not supposed to return exactly 0.0 or 1.0,
+* the statistically correct thing to do in this situation is
+* simply to iterate again.
+* N.B. the case X( I ) = 0.0 should not be possible.
+ I1 = I1 + 2
+ I2 = I2 + 2
+ I3 = I3 + 2
+ I4 = I4 + 2
+ GOTO 20
+ END IF
+*
+ 10 CONTINUE
+*
+* Return final value of seed
+*
+ ISEED( 1 ) = IT1
+ ISEED( 2 ) = IT2
+ ISEED( 3 ) = IT3
+ ISEED( 4 ) = IT4
+ RETURN
+*
+* End of SLARUV
+*
+ END
diff --git a/src/voronoiproj.f90 b/src/voronoiproj.f90
new file mode 100644
index 0000000..6ef4a65
--- /dev/null
+++ b/src/voronoiproj.f90
@@ -0,0 +1,175 @@
+subroutine voronoiproj(leniw,lenrw,iw,rw,dres,goxd,dvxd,gozd,dvzd,depz,&
+ nx,ny,nz,nd,ncells,hvratio,damp,iproj,dv)
+ use lsmrModule, only:lsmr
+
+ implicit none
+ integer leniw,lenrw
+ integer nx,ny,nz
+ integer iw(leniw)
+ real depz(nz)
+ real rw(lenrw)
+ integer ncells
+ real dv(*),dres(*)
+ real goxd,gozd,dvxd,dvzd
+ real damp
+ real hvratio,cmb
+ integer ndim,nd
+ integer iproj
+
+ real,parameter:: radius = 6371.0,ftol = 0.1,pi = 3.141592654
+ integer ii,ix,iy,iz
+ real,dimension(:),allocatable:: grow,gcol,subrow,dis,dws,xunknown
+ real,dimension(:),allocatable:: lat,lon,rad,theta,phi,rrad,xpts,ypts,zpts
+ real,dimension(:),allocatable :: rw_p,rwgp,norm
+ integer,dimension(:),allocatable:: iw_p,row,col,iwgp,colgp
+ integer idx
+ integer maxnar,nzid
+ integer iseed(4)
+ real xs,ys,zs
+
+ real atol,btol
+ real conlim
+ integer istop
+ integer itnlim
+ real acond
+ real anorm
+ real arnorm
+ real rnorm
+ real xnorm
+ integer localSize,nout,itn
+ integer leniw_p,lenrw_p,leniwgp,lenrwgp
+
+ allocate(lat(nx-2),lon(ny-2),rad(nz-1))
+ ndim = (nx-2)*(ny-2)*(nz-1)
+
+ do ii = 1,nx-2
+ lat(ii) = (goxd-(ii-1)*dvxd)*pi/180
+ enddo
+
+ do ii = 1,ny-2
+ lon(ii) = (gozd+(ii-1)*dvzd)*pi/180
+ enddo
+
+ !cmb = radius - depz(nz-1)*hvratio
+ do ii = 1,nz-1
+ rad(ii) = radius-depz(ii)*hvratio
+ !rad(ii) = cmb+depz(ii)*hvratio
+ enddo
+
+ allocate(theta(ncells),phi(ncells),rrad(ncells),dws(ncells),norm(ncells))
+ allocate(xpts(ncells),ypts(ncells),zpts(ncells),dis(ncells),xunknown(ncells))
+ allocate(rw_p(ndim))
+ allocate(iw_p(2*ndim+1),row(ndim),col(ndim))
+
+ iseed(1:3) = (/38,62,346/)
+ iseed(4) = 2*iproj+1
+ call slarnv(1,iseed,ncells,theta)
+ theta = (gozd+theta*(ny-3)*dvzd)*pi/180
+ call slarnv(1,iseed,ncells,phi)
+ phi = pi/2-(goxd-phi*(nx-3)*dvxd)*pi/180
+ call slarnv(1,iseed,ncells,rrad)
+ rrad = radius-rrad*depz(nz-1)*hvratio
+
+ xpts = rrad*sin(phi)*cos(theta)
+ ypts = rrad*sin(phi)*sin(theta)
+ zpts = rrad*cos(phi)
+
+ idx = 0
+ do iz = 1,nz-1
+ do iy = 1,ny-2
+ do ix = 1,nx-2
+ xs = rrad(iz)*sin(pi/2-lat(ix))*cos(lon(iy))
+ ys = rrad(iz)*sin(pi/2-lat(ix))*sin(lon(iy))
+ zs = rrad(iz)*cos(pi/2-lat(ix))
+ dis = (xpts-xs)**2+(ypts-ys)**2+(zpts-zs)**2
+ idx = idx+1
+ col(idx) = (iz-1)*(nx-2)*(ny-2)+(iy-1)*(nx-2)+ix
+ row(idx) = minloc(dis,1)
+ enddo
+ enddo
+ enddo
+ rw_p = 1.0
+ leniw_p = 2*ndim+1
+ lenrw_p = ndim
+ iw_p(1) = ndim
+ iw_p(2:ndim+1) = row
+ iw_p(ndim+2:2*ndim+1) = col
+
+ allocate(grow(ndim),gcol(nd),subrow(ncells))
+ maxnar = int(0.6*nd*ncells)
+ allocate(iwgp(maxnar*2+1),colgp(maxnar),rwgp(maxnar))
+
+ nzid = 0
+ do ii = 1,nd
+ grow = 0
+ gcol = 0
+ gcol(ii) = 1.0
+ call aprod(2,nd,ndim,grow,gcol,leniw,lenrw,iw,rw)
+ subrow = 0
+ call aprod(1,ncells,ndim,grow,subrow,leniw_p,lenrw_p,iw_p,rw_p)
+ do ix = 1,ncells
+ if(abs(subrow(ix))>ftol) then
+ nzid = nzid+1
+ rwgp(nzid) = subrow(ix)
+ iwgp(1+nzid) = ii
+ colgp(nzid) = ix
+ endif
+ enddo
+ enddo
+ leniwgp = nzid*2+1
+ lenrwgp = nzid
+ iwgp(1) = lenrwgp
+ iwgp(nzid+2:nzid*2+1) = colgp(1:nzid)
+
+ dws = 0
+ !do ii=1,nzid
+ !dws(iwgp(1+ii+nzid)) = dws(iwgp(1+ii+nzid))+abs(rwgp(ii))
+ !enddo
+ norm = 0
+ do ii=1,nzid
+ norm(iwgp(1+ii+nzid)) = norm(iwgp(1+ii+nzid))+rwgp(ii)**2
+ enddo
+
+ do ii =1,ncells
+ norm(ii) = sqrt(norm(ii)/nd+0.01)
+ enddo
+
+ do ii =1,nzid
+ rwgp(ii) = rwgp(ii)/norm(iwgp(1+ii+nzid))
+ enddo
+
+ conlim = 50
+ itnlim = 100
+ atol = 1e-2
+ btol = 1e-3
+ istop = 0
+ anorm = 0.0
+ acond = 0.0
+ arnorm = 0.0
+ xnorm = 0.0
+ localSize = int(ncells/4)
+ !damp = dampvel
+ ! using lsmr to solve for the projection coefficients
+ !print*, 'LSMR beginning ...'
+
+ nout = -1
+ !nout = 36
+ !open(nout,file='lsmrout_sub.txt')
+
+ call LSMR(nd, ncells, leniwgp, lenrwgp,iwgp,rwgp,dres,damp,&
+ atol, btol, conlim, itnlim, localSize,nout,&
+ xunknown, istop, itn, anorm, acond,rnorm, arnorm, xnorm)
+ !close(nout)
+ do ii = 1,ncells
+ xunknown(ii) = xunknown(ii)/norm(ii)
+ enddo
+ dv(1:ndim) = 0
+ call aprod(2,ncells,ndim,dv,xunknown,leniw_p,lenrw_p,iw_p,rw_p)
+ deallocate(grow,gcol,subrow)
+ deallocate(theta,phi,rrad,dws,norm)
+ deallocate(xpts,ypts,zpts,dis,xunknown)
+ deallocate(iw_p,rw_p,row,col)
+ deallocate(lat,lon,rad)
+ deallocate(iwgp,colgp,rwgp)
+
+ end subroutine