def testInverseCalcReal(na,ka): #################### Build wavelet and CMP gather with const v########## st = Sampling(1500,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first sx = Sampling(60,0.0496,.262); nx,dx,fx = sx.count,sx.delta,sx.first cmp = "C:/Users/Chris/Documents/CWP/Research/research/vikinggrabenCMP/m1cdp=1300.strip" hp = SUDataGrabber.grab2DFile(cmp, nx, nt) hp = tpow(3.0,st,hp) tnmo = [0.010,0.646,0.846,1.150,1.511,1.825,2.490,2.985] vnmo = [1.510,1.575,1.687,1.817,1.938,1.980,2.446,2.735] vnmoInterp = interpolateVel(tnmo,st,vnmo) #################### Enhanced NMO Estimation ######################### wn = WaveletNmo(st,sx,vnmoInterp) aenha = wn.getInverseA(na,ka,hp) # estimate inverse wavelet print "enhance nmo inverse coefficients" dump(aenha) pd = PredDecon(hp,na,ka) apred = pd.getPredErrorCoef() print "predictive decon inverse coefficients" dump(apred) ####Use predictive deconvolution to get prediction error coefficients #to get the inverse of the wavelet #pd = PredDecon(wn.applyNmo(hp),na,ka) #pd = PredDecon(hp,na,ka) #apred = pd.getPredErrorCoef() #print "predictive decon nmo inverse coefficients" dump(apred)
def goEstimateWaveletFromCmpGather():
""" Estimates wavelet from a CMP gather """
st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first
nref,vnmo = 100,2.0 # number of reflectors and NMO velocity
freq,decay = 30.0,0.1 # peak frequency and decay for wavelet
p = makeCmpReflections(vnmo,nref,st,sx) # cmp gather without wavelet
f = addArWavelet(freq,decay,st,sx,p) # cmp gather with wavelet
plotGather(st,sx,f,"CMP gather")
na,ka = 11,-5 # sampling for inverse wavelet a
ak = zerofloat(na) # array for the known inverse wavelet a
r,w = exp(-decay),2.0*PI*freq*st.delta # radius and frequency of poles
a1,a2 = -2.0*r*cos(w),r*r # coefficients for inverse wavelet
ak[0-ka] = 1.0
ak[1-ka] = a1
ak[2-ka] = a2
wn = WaveletNmo(st,sx,vnmo)
ae = wn.getInverseA(na,ka,f) # the estimated inverse wavelet
nh,kh = 100,-20 # sampling for wavelet h
for a in [ak,ae]:
if a is ak:
title = "known wavelet"
else:
title = "estimated wavelet"
print title
h = wn.getWaveletH(na,ka,a,nh,kh);
plotSequence(Sampling(nh,st.delta,kh*st.delta),normalize(h),title=title)
g = wn.applyHNmoA(na,ka,ak,nh,kh,h,f)
plotGather(st,sx,g,"NMO with "+title)
def goEstimateWaveletFromSynthetic_More3NA_PEF(na, ka, nh, kh, nref): #Input data st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first nref,vnmo = nref,2.0 # number of reflectors and NMO velocity freq,decay = 30.0,0.1 # peak frequency and decay for wavelet p = makeCmpReflections(vnmo,1,st,sx) # cmp gather without wavelet hp = addArWavelet(freq,decay,st,sx,p) # cmp gather with wavelet plotGather(st,sx,hp,0,2,100,"CMP gather") na,ka = na,ka # sampling for inverse wavelet a nh,kh = nh,kh # sampling for wavelet h wn = WaveletNmo(st,sx,vnmo) a = wn.getInverseA(na,ka,hp) # estimate inverse wavelet h = wn.getWaveletH(na,ka,a,nh,kh); # estimate wavelet g = wn.applyHNmoA(na,ka,a,nh,kh,h,hp) #convolve inverse, apply NMO, convolve wavelet pd = PredDecon(hp,na,ka) apred = pd.getPredErrorCoef() hpred = wn.getWaveletH(na,ka,apred,nh,kh); # estimate wavelet tmin,tmax,perc = 0.0,2,100.0 #plotGather(st,sx,d,tmin=tmin,tmax=tmax,perc=perc,title="Difference") #plotGather(st,sx,g,tmin=tmin,tmax=tmax,perc=perc,title="improved NMO") #plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,title="conventional NMO") #plotGather(st,sx,hp,tmin=tmin,tmax=tmax,perc=perc,title="input gather") sa = Sampling(na,st.delta,ka*st.delta) sh = Sampling(nh,st.delta,kh*st.delta) plot2Sequences(sa,sa,a,apred,title="inverse") plot2Sequences(sh,sh,normalize(h),normalize(hpred),title="estimated wavelet")
def testInverseCalcSynthetic(na,ka,nref):
#################### Build wavelet and CMP gather with const v##########
st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first
nref,vnmo = nref,2.0 # number of reflectors and NMO velocity
freq,decay = 30.0,0.1 # peak frequency and decay for wavelet
p = makeCmpReflections(vnmo,nref,st,sx) # cmp gather without wavelet
hp = addArWavelet(freq,decay,st,sx,p) # cmp gather with wavelet
ak = zerofloat(na) # array for the known inverse wavelet a
r,w = exp(-decay),2.0*PI*freq*st.delta # radius and frequency of poles
a1,a2 = -2.0*r*cos(w),r*r # coefficients for inverse wavelet
ak[0-ka] = 1.0
ak[1-ka] = a1
ak[2-ka] = a2
print "known inverse coefficients"
dump(ak)
wn = WaveletNmo(st,sx,vnmo)
fa = zerofloat(nt,nx)
plotGather(st,sx,hp,0,2,100)
for x in range(0,nx):
conv(na,0,ak,nt,0,hp[x],nt,0,fa[x])
nmofa = wn.applyNmo(fa)
print PredDecon.semblance(nmofa)
test = zerofloat(nt,nx)
for i in range(0,nx):
test[i][125] = 2.0
plotGatherPNG(st,sx,test,"test zoom",.4,.6)
plotGatherPNG(st,sx,nmofa,"nmofa zoom",.4,.6)
print PredDecon.semblance(test)
plotGather(st,sx,fa,0,2,100)
plotGather(st,sx,nmofa,0,2,100)
#plotGather(st,sx,fa,0,2,100)
#################### Enhanced NMO Estimation #########################
aenha = wn.getInverseA(na,ka,hp) # estimate inverse wavelet
print "enhance nmo inverse coefficients"
dump(aenha)
pd = PredDecon(hp,na,ka)
apredC = pd.getPredErrorCoef()
apredD = pd.getInverseAPef(na, 0, hp)
print "Chris predictive decon inverse coefficients"
dump(apredC)
print "predictive decon inverse coefficients"
dump(apredD)
fa = zerofloat(nt,nx)
for x in range(0,nx):
conv(na,0,apred,nt,0,hp[x],nt,0,fa[x])
def makeObjectFunctPlotReal():
st = Sampling(1500,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(60,0.0496,.262); nx,dx,fx = sx.count,sx.delta,sx.first
cmp = "C:/Users/Chris/Documents/CWP/Research/research/vikinggrabenCMP/cdp=1301.strip"
hp = SUDataGrabber.grab2DFile(cmp, nx, nt)
hp = tpow(3.0,st,hp)
#tnmo = [0.0380286,0.475358,0.950715,1.25,1.75882,2.33876,3.54617,4.35428,5.68528]
#vnmo = [1.50597 ,1.52462 ,1.79203 ,1.87,2.0,2.28953,2.45122,2.65644,2.69997]
#ts=0.010,0.646,0.846,1.150,1.511,1.825,2.490,2.985
#vs=1.510,1.575,1.687,1.817,1.938,1.980,2.446,2.735
ts=0.010,0.646,0.846,1.150,1.511,1.825,2.490,2.985
vs=1.510,1.575,1.687,1.817,1.938,2.105,2.446,2.735
vnmoInterp = interpolateVel(ts,st,vs)
sp = SimplePlot(SimplePlot.Origin.UPPER_LEFT)
sp.addPoints(st, vnmoInterp)
wn = WaveletNmo(st,sx,vnmoInterp)
na = 3
ka = 0
pd = PredDecon(hp,na,ka)
apred = pd.getPredErrorCoef()
fa1 = apred[1]
fa2 = apred[2]
sa1 = Sampling(1,.1,fa1)
sa2 = Sampling(1,.1,fa2)
#nmo corrected gather
wn = WaveletNmo(st,sx,vnmoInterp)
ojgnmo = ObjectiveFunction.PEFGather(hp,sa1,sa2,sx,st,wn,vnmo)
#weird "semblance" term
ojgnmosem = ObjectiveFunction.SemblanceGather(hp,sa1,sa2,sx,st,wn,vnmo)
plotObjectiveFunctWa(sa1,sa2,ojgnmo,"VG Obj. Function PEF Term",apred)
plotObjectiveFunctWa(sa1,sa2,ojgnmosem,"VG Obj. Function Semblance Term",apred)
fa = zerofloat(nt,nx)
for x in range(0,nx):
conv(na,0,apred,nt,0,hp[x],nt,0,fa[x])
nmofa = wn.applyNmo(fa)
plotGatherPNG(st,sx,hp,0,6,100,"Viking Graben CDP 1300")
plotGather(st,sx,fa,0,6,100)
plotGather(st,sx,nmofa,0,6,100)
def makeObjectFunctPlotSynthetic(nref):
st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first
nref,vnmo = nref,2.0 # number of reflectors and NMO velocity
freq,decay = 30.0,0.1 # peak frequency and decay for wavelet
p = makeCmpReflections(vnmo,nref,st,sx) # cmp gather without wavelet
hp = addArWavelet(freq,decay,st,sx,p) # cmp gather with wavelet
na,ka = 3,0 # sampling for inverse wavelet a
ak = zerofloat(na) # array for the known inverse wavelet a
r,w = exp(-decay),2.0*PI*freq*st.delta # radius and frequency of poles
a1,a2 = -2.0*r*cos(w),r*r # coefficients for inverse wavelet
ak[0-ka] = 1.0
ak[1-ka] = a1
ak[2-ka] = a2
print "known inverse coefficients"
dump(ak)
fa1 = a1-2.5
fa2 = a2-2.5
sa1 = Sampling(50,.1,fa1)
sa2 = Sampling(50,.1,fa2)
#ojg = PredDecon.constructPEFGatherObjFunction(hp,sa1,sa2,sx,st)
#nmo corrected gather
wn = WaveletNmo(st,sx,vnmo)
ojgnmo = ObjectiveFunction.PEFGather(hp,sa1,sa2,sx,st,wn,vnmo)
#weird "semblance" term
ojgnmosem = ObjectiveFunction.SemblanceGather(hp,sa1,sa2,sx,st,wn,vnmo)
fa = zerofloat(nt,nx)
for x in range(0,nx):
conv(na,0,ak,nt,0,hp[x],nt,0,fa[x])
nmofa = wn.applyNmo(fa)
plotGatherPNG(st,sx,hp,0,2,100,"Synthetic Gather")
plotGather(st,sx,fa,0,2,100)
plotGather(st,sx,nmofa,0,2,100)
print ak
#plotObjectiveFunct(sa1,sa2,ojg,"Synth Gather objective function",ak)
plotObjectiveFunctWa(sa1,sa2,ojgnmo,"Synth. Obj. Function PEF Term",ak)
plotObjectiveFunctWa(sa1,sa2,ojgnmosem,"Synth. Obj. Function Semblance Term",ak)
def goEstimateWaveletFromSyntheticPEF(na, ka, nh, kh, nref): #Input data st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first nref,vnmo = nref,2.0 # number of reflectors and NMO velocity freq,decay = 30.0,0.1 # peak frequency and decay for wavelet p = makeCmpReflections(vnmo,1,st,sx) # cmp gather without wavelet hp = addArWavelet(freq,decay,st,sx,p) # cmp gather with wavelet plotGather(st,sx,hp,0,2,100,"CMP gather") na,ka = na,ka # sampling for inverse wavelet a nh,kh = nh,kh # sampling for wavelet h ak = zerofloat(na) # array for the known inverse wavelet a r,w = exp(-decay),2.0*PI*freq*st.delta # radius and frequency of poles a1,a2 = -2.0*r*cos(w),r*r # coefficients for inverse wavelet ak = zerofloat(na) # array for the known inverse wavelet a ak[0-ka] = 1.0 ak[1-ka] = a1 ak[2-ka] = a2 wn = WaveletNmo(st,sx,vnmo) a = wn.getInverseA(na,ka,hp) # estimate inverse wavelet h = wn.getWaveletH(na,ka,a,nh,kh); # estimate wavelet g = wn.applyHNmoA(na,ka,a,nh,kh,h,hp) #convolve inverse, apply NMO, convolve wavelet #apply NMO to original data e = wn.applyNmo(hp) hk = wn.getWaveletH(na,ka,ak,nh,kh); # estimate wavelet pd = PredDecon(hp,na,ka) apred = pd.getPredErrorCoef() hpred = wn.getWaveletH(na,ka,apred,nh,kh); # estimate wavelet tmin,tmax,perc = 0.0,2,100.0 #plotGather(st,sx,d,tmin=tmin,tmax=tmax,perc=perc,title="Difference") #plotGather(st,sx,g,tmin=tmin,tmax=tmax,perc=perc,title="improved NMO") #plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,title="conventional NMO") #plotGather(st,sx,hp,tmin=tmin,tmax=tmax,perc=perc,title="input gather") sa = Sampling(na,st.delta,ka*st.delta) sh = Sampling(nh,st.delta,kh*st.delta) plot3Sequences(sa,sa,sa,ak,a,apred,title="inverse") plot3Sequences(sh,sh,sh,normalize(hk),normalize(h),normalize(hpred),title="estimated wavelet")
def goEstimateWaveletFromOzGather():
""" Estimates wavelet from one of Oz Yilmaz's gathers """
name = "oz30"
if name is "oz01": # Vibroseis
st = Sampling(1275,0.004,0.004); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(53,0.100584,-2.615184); nx,dx,fx = sx.count,sx.delta,sx.first
vnmo = 3.00 # NMO velocity
texp,tbal = 1.00,500
tmin,tmax,perc = 1.5,2.5,99
na,ka = 21,-10 # sampling for inverse wavelet a
nh,kh = 51,-25 # sampling for wavelet h
elif name is "oz04": # Vibroseis
st = Sampling(1275,0.004,0.004); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(52,0.1,-2.55); nx,dx,fx = sx.count,sx.delta,sx.first
vnmo = 3.00 # NMO velocity
texp,tbal = 1.00,500
tmin,tmax,perc = 0.0,5.0,99
na,ka = 21,-10 # sampling for inverse wavelet a
nh,kh = 51,-25 # sampling for wavelet h
elif name is "oz16": # Airgun
st = Sampling(1325,0.004,0.004); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling( 48,0.025,0.233); nx,dx,fx = sx.count,sx.delta,sx.first
vnmo = 1.95 # NMO velocity
texp,tbal = 1.00,100
tmin,tmax,perc = 0.8,2.3,98
na,ka = 11,0 # sampling for inverse wavelet a
nh,kh = 201,-50 # sampling for wavelet h
elif name is "oz30": # Airgun
st = Sampling(2175,0.004,0.00400); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling( 96,0.025,0.23075); nx,dx,fx = sx.count,sx.delta,sx.first
vnmo = 1.60 # NMO velocity
texp,tbal = 0.00,0
tmin,tmax,perc = 2.5,3.0,98
na,ka = 11,0 # sampling for inverse wavelet a
nh,kh = 201,-50 # sampling for wavelet h
f = zerofloat(nt,nx)
ais = ArrayInputStream("/data/seis/oz/"+name+".F")
ais.readFloats(f)
ais.close()
f = tpow(texp,st,f)
if tbal>0:
f = balance(tbal,f)
wn = WaveletNmo(st,sx,vnmo)
a = wn.getInverseA(na,ka,f) # estimate inverse wavelet
h = wn.getWaveletH(na,ka,a,nh,kh); # estimate wavelet
nah = na+nh
kah = ka+kh
ah = zerofloat(nah)
conv(na,ka,a,nh,kh,h,nah,kah,ah)
g = wn.applyHNmoA(na,ka,a,nh,kh,h,f)
e = wn.applyNmo(f)
print "a ="; dump(a);
plotGather(st,sx,f,tmin=tmin,tmax=tmax,perc=perc,title="input gather")
plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,title="conventional NMO")
plotGather(st,sx,g,tmin=tmin,tmax=tmax,perc=perc,title="improved NMO")
plotSequence(Sampling(na,st.delta,ka*st.delta),normalize(a),
title="inverse")
plotSequence(Sampling(nh,st.delta,kh*st.delta),normalize(h),
title="estimated wavelet")
plotSequence(Sampling(nah,st.delta,kah*st.delta),normalize(ah),
title="unit impulse")
def goEstimateWaveletFromVigGather():
""" Estimates wavelet from Viking Graben gather """
st,sx,f = readVigGather(800)
nt,nx = st.count,sx.count
dt,dx = st.delta,sx.delta
ft,fx = st.first,sx.first
ts=0.010,0.646,0.846,1.150,1.511,1.825,2.490,2.985
vs=1.510,1.575,1.687,1.817,1.938,1.980,2.446,2.735
vnmoP = CubicInterpolator(ts,vs).interpolate(rampfloat(ft,dt,nt))
vnmoM = fillfloat(1.500,nt) # water velocity, for multiples
vnmo = vnmoM
na,ka = 16,0 # sampling for inverse wavelet a
nh,kh = 151,-25 # sampling for wavelet h
fmin,fmax,sfac = 5.0,75.0,1.00
texp,tbal,smax = 2.0,100,9.00
tmin,tmax,perc = 0.4,1.9,98.0
#f = tpow(texp,st,f)
if tbal>0:
f = balance(tbal,f)
nmo = NormalMoveout()
nmo.setStretchMax(smax)
wn = WaveletNmo(nmo)
wn.setTimeRange(int(tmin/dt),int(tmax/dt))
wn.setFrequencyRange(fmin*dt,fmax*dt)
wn.setStabilityFactor(sfac)
e = nmo.apply(st,sx,vnmo,f)
apef = wn.getInverseAPef(na,ka,f)
anmo = wn.getInverseANmo(na,ka,st,sx,vnmo,f)
hpef = wn.getWaveletH(na,ka,apef,nh,kh);
hnmo = wn.getWaveletH(na,ka,anmo,nh,kh);
plotWavelets(Sampling(nh,st.delta,kh*st.delta),[hnmo,hpef],
title="estimated wavelets")
plotGather(st,sx,f,tmin=tmin,tmax=tmax,perc=perc,title="CMP gather")
plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,title="conventional NMO")
alist = [apef,anmo]
hlist = [hpef,hnmo]
tlist = ["PEF","NMO"]
for ia in range(0,len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na+nh
kah = ka+kh
ah = zerofloat(nah)
conv(na,ka,a,nh,kh,h,nah,kah,ah)
g = wn.applyHNmoA(na,ka,a,nh,kh,h,st,sx,vnmo,f)
epef = wn.getVariancePef(na,ka,a,f)
enmo = wn.getVarianceNmo(na,ka,a,st,sx,vnmo,f)
enor = wn.getNormalizedVarianceNmo(na,ka,a,st,sx,vnmo,f)
print tlist[ia]+": epef =",epef," enmo =",enmo," enor =",enor
print " a ="; dump(a)
plotGather(st,sx,g,tmin=tmin,tmax=tmax,perc=perc,
title=t+": improved NMO")
def inverseAndWaveletCalc(sx,st,f,vnmo,smax,fmin,fmax,sfac,
na,ka,nh,kh,tmin,tmax,perc,w=None):
tmino = 0
tmaxo = 3
hmax = 5
nt,nx = st.count,sx.count
dt,dx = st.delta,sx.delta
ft,fx = st.first,sx.first
nmo = NormalMoveout()
nmo.setStretchMax(smax)
if w:
wn = WaveletNmo(nmo,w)
else:
wn = WaveletNmo(nmo)
wn.setTimeRange(int(tmin/dt),int(tmax/dt))
wn.setFrequencyRange(fmin*dt,fmax*dt)
wn.setStabilityFactor(sfac)
e = nmo.apply(st,sx,vnmo,f)
apef = wn.getInverseAPef(na,ka,f)
anmo = wn.getInverseANmo(na,ka,st,sx,vnmo,f)
hpef = wn.getWaveletH(na,ka,apef,nh,kh);
hnmo = wn.getWaveletH(na,ka,anmo,nh,kh);
title = "Estimated Wavelets "+str(tmin)+" s to "+str(tmax)+" s"
plotWavelets(Sampling(nh,st.delta,kh*st.delta),[hnmo,hpef],hmax,
title=title,pngDir=pngDir)
title = "Input Gather "+str(tmin)+" s to "+str(tmax)+" s"
plotGather(st,sx,f,tmin=tmin,tmax=tmax,perc=perc,title=title,pngDir=pngDir)
title = "Conventional NMO "+str(tmin)+" s to "+str(tmax)+" s"
plotGather(st,sx,e,tmin=tmino,tmax=tmaxo,perc=perc,title=title,pngDir=pngDir)
alist = [apef,anmo]
hlist = [hpef,hnmo]
tlist = ["PEF","NMO"]
for ia in range(0,len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na+nh
kah = ka+kh
ah = zerofloat(nah)
conv(na,ka,a,nh,kh,h,nah,kah,ah)
g = wn.applyHNmoA(na,ka,a,nh,kh,h,st,sx,vnmo,f)
epef = wn.getVariancePef(na,ka,a,f)
enmo = wn.getVarianceNmo(na,ka,a,st,sx,vnmo,f)
enor = wn.getNormalizedVarianceNmo(na,ka,a,st,sx,vnmo,f)
print tlist[ia]+": epef =",epef," enmo =",enmo," enor =",enor
print " a ="; dump(a)
title = t+" improved NMO "+str(tmin)+" s to "+str(tmax)+" s"
plotGather(st,sx,g,tmin=tmino,tmax=tmaxo,perc=perc,title=title,pngDir=pngDir)
#plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,
# title=t+": stack error")
#plotSequence(Sampling(na,st.delta,ka*st.delta),normalize(a),
# title="inverse wavelet")
#plotSequence(Sampling(nah,st.delta,kah*st.delta),normalize(ah),
# title="unit impulse")
#d = wn.getDifferenceGathers(na,ka,st,sx,vnmo,f)
#for ia in range(na):
# plotGather(st,sx,d[ia],
# tmin=tmin,tmax=tmax,perc=perc,title="lag="+str(ka+ia))
return hpef,hnmo
def inverseAndWaveletCalc(sx,st,f,vnmo,fmin,fmax,sfac,
na,ka,nh,kh,hsyn,tmin,tmax,uncert,w=None):
nt,nx = st.count,sx.count
dt,dx = st.delta,sx.delta
ft,fx = st.first,sx.first
vnmo = fillfloat(vnmo,nt)
nmo = NormalMoveout()
#nmo.setStretchMax(9.0)
if w:
wn = WaveletNmo(nmo,w)
else:
wn = WaveletNmo(nmo)
wn.setFrequencyRange(fmin*dt,fmax*dt)
wn.setTimeRange(int(tmin/dt),int(tmax/dt))
wn.setStabilityFactor(sfac)
e = nmo.apply(st,sx,vnmo,f)
apef = wn.getInverseAPef(na,ka,f)
anmo = wn.getInverseANmo(na,ka,st,sx,vnmo,f)
hpef = wn.getWaveletH(na,ka,apef,nh,kh)
hnmo = wn.getWaveletH(na,ka,anmo,nh,kh)
title = "Estimated Wavelets "+str(uncert)+" "+str(tmin)+" s to "+str(tmax)+" s"
plotWavelets(Sampling(nh,st.delta,kh*st.delta),[hnmo,hpef,hsyn],
title=title,pngDir=pngDir)
title = "Input Gather "+str(uncert)+" "+str(tmin)+" s to "+str(tmax)+" s"
plotGather(st,sx,f,tmin=tmin,tmax=tmax,title=title,pngDir=pngDir)
title = "Conventional NMO "+str(uncert)+" "+str(tmin)+" s to "+str(tmax)+" s"
plotGather(st,sx,e,title=title,pngDir=pngDir)
alist = [apef,anmo]
hlist = [hpef,hnmo]
tlist = ["PEF","NMO"]
for ia in range(0,len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na+nh
kah = ka+kh
ah = zerofloat(nah)
conv(na,ka,a,nh,kh,h,nah,kah,ah)
g = wn.applyHNmoA(na,ka,a,nh,kh,h,st,sx,vnmo,f)
epef = wn.getVariancePef(na,ka,a,f)
enmo = wn.getVarianceNmo(na,ka,a,st,sx,vnmo,f)
enor = wn.getNormalizedVarianceNmo(na,ka,a,st,sx,vnmo,f)
print t+" epef =",epef," enmo =",enmo," enor =",enor
print " a ="; dump(a)
title = t+" improved NMO "+str(uncert)+" "+str(tmin)+" s to "+str(tmax)+" s"
plotGather(st,sx,g,tmin=tmin,tmax=tmax,title=title,pngDir=pngDir)
return hpef,hnmo
def goEstimateWaveletFromGather(name):
""" Estimates wavelet from a gather sampled in time and offset """
print name
if name == "syn1": # Synthetic with one reflector
st = Sampling(501, 0.004, 0.0)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(201, 0.010, 0.0)
nx, dx, fx = sx.count, sx.delta, sx.first
nref, vnmo = 1, 2.0 # number of reflectors and NMO velocity
tran, tbed = False, False
freq, decay = 20.0, 0.05 # peak frequency and decay for wavelet
fmin, fmax, sfac = 0.0, 50.0, 1.00
texp, tbal = 0.00, 0
tmin, tmax, perc = 0.75, 1.75, 100.0
zp = True # zero-phase?
if zp:
na, ka = 5, -2
nh, kh = 251, -125
decay *= 4.0
else:
na, ka = 11, 0
nh, kh = 151, -25
hsyn = getArWavelet(freq, decay, st, nh, kh, zp)
elif name == "synr": # Synthetic with random reflectors
st = Sampling(501, 0.004, 0.0)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(201, 0.010, 0.0)
nx, dx, fx = sx.count, sx.delta, sx.first
tran, tbed = True, False
nref, vnmo = 40, 2.0 # number of reflectors and NMO velocity
freq, decay = 20.0, 0.05 # peak frequency and decay for wavelet
fmin, fmax, sfac = 0.0, 50.0, 1.00
texp, tbal = 0.00, 0
tmin, tmax, perc = 0.0, 1.75, 100.0
zp = False # zero-phase?
na, ka = 11, 0 # sampling for inverse wavelet a
nh, kh = 151, -25 # sampling for wavelet h
hsyn = getArWavelet(freq, decay, st, nh, kh)
elif name == "synt": # Synthetic with random thin beds
st = Sampling(501, 0.004, 0.0)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(201, 0.010, 0.0)
nx, dx, fx = sx.count, sx.delta, sx.first
tran, tbed = True, True
nref, vnmo = 40, 2.0 # number of reflectors and NMO velocity
freq, decay = 20.0, 0.05 # peak frequency and decay for wavelet
fmin, fmax, sfac = 0.0, 50.0, 1.0001
texp, tbal = 0.00, 0
tmin, tmax, perc = 0.15, 1.75, 100.0
zp = True # zero-phase?
if zp:
na, ka = 5, -2
nh, kh = 251, -125
decay *= 4.0
else:
na, ka = 11, 0
nh, kh = 151, -25
hsyn = getArWavelet(freq, decay, st, nh, kh, zp)
elif name == "oz01": # Vibroseis
st = Sampling(1275, 0.004, 0.004)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(53, 0.100584, -2.615184)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 3.00 # NMO velocity
fmin, fmax, sfac = 5.0, 80.0, 1.01
texp, tbal = 0.00, 100
tmin, tmax, perc = 1.5, 2.5, 99
na, ka = 21, 0 # sampling for inverse wavelet a
nh, kh = 51, -25 # sampling for wavelet h
hsyn = None
elif name == "oz04": # Vibroseis
st = Sampling(1275, 0.004, 0.004)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(52, 0.1, -2.55)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 3.00 # NMO velocity
fmin, fmax, sfac = 5.0, 80.0, 1.01
texp, tbal = 0.00, 100
tmin, tmax, perc = 0.0, 5.0, 99
na, ka = 21, 0 # sampling for inverse wavelet a
nh, kh = 51, -25 # sampling for wavelet h
hsyn = None
elif name == "oz16": # Airgun
st = Sampling(1325, 0.004, 0.004)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(48, 0.025, 0.233)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 1.95 # NMO velocity
fmin, fmax, sfac = 5.0, 50.0, 1.001
texp, tbal = 0.00, 100
tmin, tmax, perc = 0.8, 2.3, 98
na, ka = 11, 0 # sampling for inverse wavelet a
nh, kh = 201, -50 # sampling for wavelet h
hsyn = None
elif name == "oz30": # Airgun
st = Sampling(2175, 0.004, 0.00400)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(96, 0.025, 0.23075)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 1.55 # NMO velocity
fmin, fmax, sfac = 5.0, 100.0, 1.00
texp, tbal = 0.00, 100
# tmin,tmax,perc = 1.2,2.2,98
# tmin,tmax,perc = 2.5,3.5,98
tmin, tmax, perc = 1.2, 3.2, 98
na, ka = 11, 0 # sampling for inverse wavelet a
nh, kh = 151, -25 # sampling for wavelet h
hsyn = None
f = zerofloat(nt, nx)
if name[0:3] == "syn":
p = makeCmpReflections(vnmo, nref, st, sx, random=tran, thinBeds=tbed)
f = addArWavelet(freq, decay, st, sx, p, zp)
else:
ais = ArrayInputStream("/data/seis/oz/" + name + ".F")
ais.readFloats(f)
ais.close()
f = tpow(texp, st, f)
if tbal > 0:
f = balance(tbal, f)
nt, nx = st.count, sx.count
dt, dx = st.delta, sx.delta
ft, fx = st.first, sx.first
vnmo = fillfloat(vnmo, nt)
nmo = NormalMoveout()
# nmo.setStretchMax(9.0)
wn = WaveletNmo(nmo)
wn.setFrequencyRange(fmin * dt, fmax * dt)
wn.setTimeRange(int(tmin / dt), int(tmax / dt))
wn.setStabilityFactor(sfac)
e = nmo.apply(st, sx, vnmo, f)
apef = wn.getInverseAPef(na, ka, f)
anmo = wn.getInverseANmo(na, ka, st, sx, vnmo, f)
hpef = wn.getWaveletH(na, ka, apef, nh, kh)
hnmo = wn.getWaveletH(na, ka, anmo, nh, kh)
plotWavelets(Sampling(nh, st.delta, kh * st.delta), [hnmo, hpef, hsyn], title="estimated wavelets")
plotGather(st, sx, f, tmin=tmin, tmax=tmax, perc=perc, title="input gather")
plotGather(st, sx, e, tmin=tmin, tmax=tmax, perc=perc, title="conventional NMO")
alist = [apef, anmo]
hlist = [hpef, hnmo]
tlist = ["PEF", "NMO"]
for ia in range(0, len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na + nh
kah = ka + kh
ah = zerofloat(nah)
conv(na, ka, a, nh, kh, h, nah, kah, ah)
g = wn.applyHNmoA(na, ka, a, nh, kh, h, st, sx, vnmo, f)
epef = wn.getVariancePef(na, ka, a, f)
enmo = wn.getVarianceNmo(na, ka, a, st, sx, vnmo, f)
enor = wn.getNormalizedVarianceNmo(na, ka, a, st, sx, vnmo, f)
print t + ": epef =", epef, " enmo =", enmo, " enor =", enor
print " a ="
dump(a)
plotGather(st, sx, g, tmin=tmin, tmax=tmax, perc=perc, title=t + ": improved NMO")
def goEstimateWaveletFromGather(name):
""" Estimates wavelet from a gather sampled in time and offset """
print name
if name == "syn1": # Synthetic with one reflector
st = Sampling(501, 0.004, 0.0)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(201, 0.010, 0.0)
nx, dx, fx = sx.count, sx.delta, sx.first
nref, vnmo = 1, 2.0 # number of reflectors and NMO velocity
tran, tbed = False, False
freq, decay = 20.0, 0.05 # peak frequency and decay for wavelet
fmin, fmax, sfac = 0.0, 50.0, 1.00
texp, tbal = 0.00, 0
tmin, tmax, perc = 0.75, 1.75, 100.0
zp = True # zero-phase?
if zp:
na, ka = 5, -2
nh, kh = 251, -125
decay *= 4.0
else:
na, ka = 11, 0
nh, kh = 151, -25
hsyn = getArWavelet(freq, decay, st, nh, kh, zp)
elif name == "synr": # Synthetic with random reflectors
st = Sampling(501, 0.004, 0.0)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(201, 0.010, 0.0)
nx, dx, fx = sx.count, sx.delta, sx.first
tran, tbed = True, False
nref, vnmo = 40, 2.0 # number of reflectors and NMO velocity
freq, decay = 20.0, 0.05 # peak frequency and decay for wavelet
fmin, fmax, sfac = 0.0, 50.0, 1.00
texp, tbal = 0.00, 0
tmin, tmax, perc = 0.0, 1.75, 100.0
zp = False # zero-phase?
na, ka = 11, 0 # sampling for inverse wavelet a
nh, kh = 151, -25 # sampling for wavelet h
hsyn = getArWavelet(freq, decay, st, nh, kh)
elif name == "synt": # Synthetic with random thin beds
st = Sampling(501, 0.004, 0.0)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(201, 0.010, 0.0)
nx, dx, fx = sx.count, sx.delta, sx.first
tran, tbed = True, True
nref, vnmo = 40, 2.0 # number of reflectors and NMO velocity
freq, decay = 20.0, 0.05 # peak frequency and decay for wavelet
fmin, fmax, sfac = 0.0, 50.0, 1.0001
texp, tbal = 0.00, 0
tmin, tmax, perc = 0.15, 1.75, 100.0
zp = True # zero-phase?
if zp:
na, ka = 5, -2
nh, kh = 251, -125
decay *= 4.0
else:
na, ka = 11, 0
nh, kh = 151, -25
hsyn = getArWavelet(freq, decay, st, nh, kh, zp)
elif name == "oz01": # Vibroseis
st = Sampling(1275, 0.004, 0.004)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(53, 0.100584, -2.615184)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 3.00 # NMO velocity
fmin, fmax, sfac = 5.0, 80.0, 1.01
texp, tbal = 0.00, 100
tmin, tmax, perc = 1.5, 2.5, 99
na, ka = 21, 0 # sampling for inverse wavelet a
nh, kh = 51, -25 # sampling for wavelet h
hsyn = None
elif name == "oz04": # Vibroseis
st = Sampling(1275, 0.004, 0.004)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(52, 0.1, -2.55)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 3.00 # NMO velocity
fmin, fmax, sfac = 5.0, 80.0, 1.01
texp, tbal = 0.00, 100
tmin, tmax, perc = 0.0, 5.0, 99
na, ka = 21, 0 # sampling for inverse wavelet a
nh, kh = 51, -25 # sampling for wavelet h
hsyn = None
elif name == "oz16": # Airgun
st = Sampling(1325, 0.004, 0.004)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(48, 0.025, 0.233)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 1.95 # NMO velocity
fmin, fmax, sfac = 5.0, 50.0, 1.001
texp, tbal = 0.00, 100
tmin, tmax, perc = 0.8, 2.3, 98
na, ka = 11, 0 # sampling for inverse wavelet a
nh, kh = 201, -50 # sampling for wavelet h
hsyn = None
elif name == "oz30": # Airgun
st = Sampling(2175, 0.004, 0.00400)
nt, dt, ft = st.count, st.delta, st.first
sx = Sampling(96, 0.025, 0.23075)
nx, dx, fx = sx.count, sx.delta, sx.first
vnmo = 1.55 # NMO velocity
fmin, fmax, sfac = 5.0, 100.0, 1.00
texp, tbal = 0.00, 100
#tmin,tmax,perc = 1.2,2.2,98
#tmin,tmax,perc = 2.5,3.5,98
tmin, tmax, perc = 1.2, 3.2, 98
na, ka = 11, 0 # sampling for inverse wavelet a
nh, kh = 151, -25 # sampling for wavelet h
hsyn = None
f = zerofloat(nt, nx)
if name[0:3] == "syn":
p = makeCmpReflections(vnmo, nref, st, sx, random=tran, thinBeds=tbed)
f = addArWavelet(freq, decay, st, sx, p, zp)
else:
ais = ArrayInputStream("/data/seis/oz/" + name + ".F")
ais.readFloats(f)
ais.close()
f = tpow(texp, st, f)
if tbal > 0:
f = balance(tbal, f)
nt, nx = st.count, sx.count
dt, dx = st.delta, sx.delta
ft, fx = st.first, sx.first
vnmo = fillfloat(vnmo, nt)
nmo = NormalMoveout()
#nmo.setStretchMax(9.0)
wn = WaveletNmo(nmo)
wn.setFrequencyRange(fmin * dt, fmax * dt)
wn.setTimeRange(int(tmin / dt), int(tmax / dt))
wn.setStabilityFactor(sfac)
e = nmo.apply(st, sx, vnmo, f)
apef = wn.getInverseAPef(na, ka, f)
anmo = wn.getInverseANmo(na, ka, st, sx, vnmo, f)
hpef = wn.getWaveletH(na, ka, apef, nh, kh)
hnmo = wn.getWaveletH(na, ka, anmo, nh, kh)
plotWavelets(Sampling(nh, st.delta, kh * st.delta), [hnmo, hpef, hsyn],
title="estimated wavelets")
plotGather(st,
sx,
f,
tmin=tmin,
tmax=tmax,
perc=perc,
title="input gather")
plotGather(st,
sx,
e,
tmin=tmin,
tmax=tmax,
perc=perc,
title="conventional NMO")
alist = [apef, anmo]
hlist = [hpef, hnmo]
tlist = ["PEF", "NMO"]
for ia in range(0, len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na + nh
kah = ka + kh
ah = zerofloat(nah)
conv(na, ka, a, nh, kh, h, nah, kah, ah)
g = wn.applyHNmoA(na, ka, a, nh, kh, h, st, sx, vnmo, f)
epef = wn.getVariancePef(na, ka, a, f)
enmo = wn.getVarianceNmo(na, ka, a, st, sx, vnmo, f)
enor = wn.getNormalizedVarianceNmo(na, ka, a, st, sx, vnmo, f)
print t + ": epef =", epef, " enmo =", enmo, " enor =", enor
print " a ="
dump(a)
plotGather(st,
sx,
g,
tmin=tmin,
tmax=tmax,
perc=perc,
title=t + ": improved NMO")
def goEstimateWaveletFromGather(name):
""" Estimates wavelet from a gather sampled in time and offset """
print name
if name == "syn1": # Synthetic with one reflector
st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first
nref,vnmo = 1,2.0 # number of reflectors and NMO velocity
tran,tbed = False,False
freq,decay = 20.0,0.05 # peak frequency and decay for wavelet
fmin,fmax,sfac = 0.0,50.0,1.00
texp,tbal = 0.00,0
tmin,tmax,perc = 0.75,1.75,100.0
zp = False # zero-phase?
if zp:
na,ka = 5,-2
nh,kh = 251,-125
decay *= 4.0
else:
na,ka = 11,0
nh,kh = 151,-25
hsyn = getArWavelet(freq,decay,st,nh,kh,zp)
elif name == "synr": # Synthetic with random reflectors
st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first
tran,tbed = True,False
nref,vnmo = 40,2.0 # number of reflectors and NMO velocity
freq,decay = 20.0,0.05 # peak frequency and decay for wavelet
fmin,fmax,sfac = 0.0,50.0,1.00
texp,tbal = 0.00,0
tmin,tmax,perc = 0.0,1.75,100.0
zp = False # zero-phase?
na,ka = 11,0 # sampling for inverse wavelet a
nh,kh = 151,-25 # sampling for wavelet h
hsyn = getArWavelet(freq,decay,st,nh,kh)
elif name == "synt": # Synthetic with random thin beds
st = Sampling(501,0.004,0.0); nt,dt,ft = st.count,st.delta,st.first
sx = Sampling(201,0.010,0.0); nx,dx,fx = sx.count,sx.delta,sx.first
tran,tbed = True,True
nref,vnmo = 40,2.0 # number of reflectors and NMO velocity
freq,decay = 20.0,0.05 # peak frequency and decay for wavelet
fmin,fmax,sfac = 0.0,50.0,1.0001
texp,tbal = 0.00,0
tmin,tmax,perc = 0.15,1.75,100.0
zp = True # zero-phase?
if zp:
na,ka = 5,-2
nh,kh = 251,-125
decay *= 4.0
else:
na,ka = 11,0
nh,kh = 151,-25
hsyn = getArWavelet(freq,decay,st,nh,kh,zp)
f = zerofloat(nt,nx)
p = makeCmpReflections(vnmo,nref,st,sx,random=tran,thinBeds=tbed)
f = addArWavelet(freq,decay,st,sx,p,zp)
f = tpow(texp,st,f)
if tbal>0:
f = balance(tbal,f)
nt,nx = st.count,sx.count
dt,dx = st.delta,sx.delta
ft,fx = st.first,sx.first
vnmo = fillfloat(vnmo,nt)
nmo = NormalMoveout()
#nmo.setStretchMax(9.0)
wn = WaveletNmo(nmo)
wn.setFrequencyRange(fmin*dt,fmax*dt)
wn.setTimeRange(int(tmin/dt),int(tmax/dt))
wn.setStabilityFactor(sfac)
e = nmo.apply(st,sx,vnmo,f)
apef = wn.getInverseAPef(na,ka,f)
anmo = wn.getInverseANmo(na,ka,st,sx,vnmo,f)
hpef = wn.getWaveletH(na,ka,apef,nh,kh);
hnmo = wn.getWaveletH(na,ka,anmo,nh,kh);
plotWavelets(Sampling(nh,st.delta,kh*st.delta),[hnmo,hpef,hsyn],
title="estimated wavelets")
plotGather(st,sx,f,tmin=tmin,tmax=tmax,perc=perc,title="input gather")
plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,title="conventional NMO")
alist = [apef,anmo]
hlist = [hpef,hnmo]
tlist = ["PEF","NMO"]
directory = "./thesisFiguresSlides/nmo/"
fracWidth,fracHeight,aspectRatio = 0.9,0.8,16.0/9.0
pngDir = directory
#pngDir = None
title= "Nmo correction"
print title
vmin,vmax = tmin,tmax
vlabel,vminmax,vint = "Time (s)",[vmin,vmax],0.5
hlabel = ["Offset (km)","Offset (km)"]
hminmax = None
hint = 0.5
tilespacing = None
clip = 3.0
plotting.plotImagesSideBySideNMO(st,sx,[e],\
vlabel=vlabel,vminmax=vminmax,vint=vint,\
hlabel=hlabel,hminmax=hminmax,hint=hint,\
clip = clip,\
tilespacing = tilespacing,\
title=title,pngDir=pngDir,\
slide=True,fracWidth=fracWidth,fracHeight=fracHeight,aspectRatio=aspectRatio)
directory = "./thesisFiguresSlides/nmo/"
fracWidth,fracHeight,aspectRatio = 0.9,0.8,16.0/9.0
pngDir = directory
#pngDir = None
title= "Nmo cmp"
print title
vmin,vmax = tmin,tmax
vlabel,vminmax,vint = "Time (s)",[vmin,vmax],0.5
hlabel = ["Offset (km)","Offset (km)"]
hminmax = None
hint = 0.5
tilespacing = None
clip = 3.0
plotting.plotImagesSideBySideNMO(st,sx,[f],\
vlabel=vlabel,vminmax=vminmax,vint=vint,\
hlabel=hlabel,hminmax=hminmax,hint=hint,\
clip = clip,\
tilespacing = tilespacing,\
title=title,pngDir=pngDir,\
slide=True,fracWidth=fracWidth,fracHeight=fracHeight,aspectRatio=aspectRatio)
for ia in range(0,len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na+nh
kah = ka+kh
ah = zerofloat(nah)
conv(na,ka,a,nh,kh,h,nah,kah,ah)
g = wn.applyHNmoA(na,ka,a,nh,kh,h,st,sx,vnmo,f)
epef = wn.getVariancePef(na,ka,a,f)
enmo = wn.getVarianceNmo(na,ka,a,st,sx,vnmo,f)
enor = wn.getNormalizedVarianceNmo(na,ka,a,st,sx,vnmo,f)
print t+": epef =",epef," enmo =",enmo," enor =",enor
print " a ="; dump(a)
plotGather(st,sx,g,tmin=tmin,tmax=tmax,perc=perc,
title=t+": improved NMO")
def inverseAndWaveletCalc(icdp,smax,fmin,fmax,texp,tbal,sfac,
na,ka,nh,kh,tmin,tmax,perc):
""" Estimates wavelet from a gather sampled in time and offset """
tmino = 0
tmaxo = 3
hmax = 5
st,sx,f = getVikingGrabenCDP(icdp)
nt,nx = st.count,sx.count
dt,dx = st.delta,sx.delta
ft,fx = st.first,sx.first
#ts = [0.0380,0.475,0.950,1.758,2.338,3.546,4.354,5.685]
#vs = [1.505,1.524,1.792,2.040,2.289,2.451,2.656,2.699]
ts=0.010,0.646,0.846,1.150,1.511,1.825,2.490,2.985
vs=1.510,1.575,1.687,1.817,1.938,1.980,2.446,2.735
vnmoP = CubicInterpolator(ts,vs).interpolate(rampfloat(ft,dt,nt))
vnmoM = fillfloat(1.500,nt) # water velocity, for multiples
vnmo = vnmoP
#f = tpow(texp,st,f)
if tbal>0:
f = balance(tbal,f)
nmo = NormalMoveout()
nmo.setStretchMax(smax)
wn = WaveletNmo(nmo)
wn.setTimeRange(int(tmin/dt),int(tmax/dt))
wn.setFrequencyRange(fmin*dt,fmax*dt)
wn.setStabilityFactor(sfac)
e = nmo.apply(st,sx,vnmo,f)
apef = wn.getInverseAPef(na,ka,f)
anmo = wn.getInverseANmo(na,ka,st,sx,vnmo,f)
hpef = wn.getWaveletH(na,ka,apef,nh,kh);
hnmo = wn.getWaveletH(na,ka,anmo,nh,kh);
title = "Estimated Wavelets CDP "+str(icdp)
plotWavelets(Sampling(nh,st.delta,kh*st.delta),[hnmo,hpef],hmax,
title=title,pngDir=pngDir)
title = "Input Gather CDP "+str(icdp)
plotGather(st,sx,f,tmin=tmin,tmax=tmax,perc=perc,title=title,pngDir=pngDir)
title = "Conventional NMO CDP "+str(icdp)
plotGather(st,sx,e,tmin=tmino,tmax=tmaxo,perc=perc,title=title,pngDir=pngDir)
alist = [apef,anmo]
hlist = [hpef,hnmo]
tlist = ["PEF","NMO"]
for ia in range(0,len(alist)):
a = alist[ia]
h = hlist[ia]
t = tlist[ia]
nah = na+nh
kah = ka+kh
ah = zerofloat(nah)
conv(na,ka,a,nh,kh,h,nah,kah,ah)
g = wn.applyHNmoA(na,ka,a,nh,kh,h,st,sx,vnmo,f)
epef = wn.getVariancePef(na,ka,a,f)
enmo = wn.getVarianceNmo(na,ka,a,st,sx,vnmo,f)
enor = wn.getNormalizedVarianceNmo(na,ka,a,st,sx,vnmo,f)
print tlist[ia]+": epef =",epef," enmo =",enmo," enor =",enor
print " a ="; dump(a)
title = t+" improved NMO CDP "+str(icdp)
plotGather(st,sx,g,tmin=tmino,tmax=tmaxo,perc=perc,
title=title,pngDir=pngDir)
#plotGather(st,sx,e,tmin=tmin,tmax=tmax,perc=perc,
# title=t+": stack error")
#plotSequence(Sampling(na,st.delta,ka*st.delta),normalize(a),
# title="inverse wavelet")
#plotSequence(Sampling(nah,st.delta,kah*st.delta),normalize(ah),
# title="unit impulse")
#d = wn.getDifferenceGathers(na,ka,st,sx,vnmo,f)
#for ia in range(na):
# plotGather(st,sx,d[ia],
# tmin=tmin,tmax=tmax,perc=perc,title="lag="+str(ka+ia))
return hpef,hnmo,st
