def goShifts(): s1,s2,g = getImage() g = slog(g) plot2(s1,s2,g,title="log input") fse = FaultSemblance() g = fse.taper(10,g) p = fse.slopes(g) sn,sd = fse.semblanceNumDen(p,g) fsc = FaultScanner2(sigmaTheta,[sn,sd],smoother) f,t = fsc.scan(-15,15) plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood") #ff,tt = fsc.thin([f,t]) #plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood") shiftMin,shiftMax = -20,20 faults = fsc.findFaults([f,t],shiftMax-shiftMin); ff = faults.getLikelihoods() plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood") g = fsc.smooth(4,p,ff,g) plot2(s1,s2,g,ff,gmin=0,gmax=1,title="input smoothed") p = fse.slopes(g) faults.findShifts(g,p,shiftMin,shiftMax) faults.clean() s = faults.getShifts() print "s min =",min(s)," max =",max(s) plot2(s1,s2,g,s,gmin=-8,gmax=8,title="fault throws")
def goShifts():
s1,s2,g = getImage()
g = slog(g)
#plot2(s1,s2,g,title="log input")
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn,sd = fse.semblanceNumDen(p,g)
fsc = FaultScanner2(sigmaTheta,[sn,sd],smoother)
f,t = fsc.scan(-15,15)
shiftMin,shiftMax = -20,20
faults = fsc.findFaults([f,t],shiftMax-shiftMin);
ff = faults.getLikelihoods()
plot2(s1,s2,g,ff,gmin=0,gmax=1,gmap=jetr,label="Fault likelihood",png="flg")
g = fsc.smooth(4,p,ff,g)
plot2(s1,s2,g,ff,gmin=0,gmax=1,gmap=jetr,label="Fault likelihood",png="flgs")
plot2(s1,s2,g,label="Log amplitude",png="gs")
p = fse.slopes(g)
faults.findShifts(g,p,shiftMin,shiftMax)
faults.clean()
s = faults.getShifts()
s = mul(s1.delta*1000.0,s)
s = neg(s)
print "s min =",min(s)," max =",max(s)
plot2(s1,s2,g,s,gmin=0,gmax=28,gmap=jetr,
label="Vertical component of throw (ms)",png="fs")
plot2(s1,s2,g,s,gmin=0,gmax=15,gmap=jetr,
label="Vertical component of throw (ms)",png="fs15")
def goShifts():
s1, s2, g = getImage()
g = slog(g)
plot2(s1, s2, g, title="log input")
fse = FaultSemblance()
g = fse.taper(10, g)
p = fse.slopes(g)
sn, sd = fse.semblanceNumDen(p, g)
fsc = FaultScanner2(sigmaTheta, [sn, sd], smoother)
f, t = fsc.scan(-15, 15)
#plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood")
#ff,tt = fsc.thin([f,t])
#plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood")
shiftMin, shiftMax = -20, 20
faults = fsc.findFaults([f, t], shiftMax - shiftMin)
ff = faults.getLikelihoods()
#plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood")
#plot2(s1,s2,g,ff,gmin=0,gmax=1,label="Fault likelihood",png="flg")
g = fsc.smooth(4, p, ff, g)
#plot2(s1,s2,g,ff,gmin=0,gmax=1,title="input smoothed")
#plot2(s1,s2,g,ff,gmin=0,gmax=1,label="Fault likelihood",png="flgs")
plot2(s1, s2, g, label="Log amplitude", png="gs")
p = fse.slopes(g)
faults.findShifts(g, p, shiftMin, shiftMax)
faults.clean()
s = faults.getShifts()
s = mul(s1.delta * 1000.0, s)
print "s min =", min(s), " max =", max(s)
def goShifts():
s1, s2, g = getImage()
g = slog(g)
plot2(s1, s2, g, title="log input")
fse = FaultSemblance()
g = fse.taper(10, g)
p = fse.slopes(g)
sn, sd = fse.semblanceNumDen(p, g)
fsc = FaultScanner2(sigmaTheta, [sn, sd], smoother)
f, t = fsc.scan(-15, 15)
# plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood")
# ff,tt = fsc.thin([f,t])
# plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood")
shiftMin, shiftMax = -20, 20
faults = fsc.findFaults([f, t], shiftMax - shiftMin)
ff = faults.getLikelihoods()
# plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood")
# plot2(s1,s2,g,ff,gmin=0,gmax=1,label="Fault likelihood",png="flg")
g = fsc.smooth(4, p, ff, g)
# plot2(s1,s2,g,ff,gmin=0,gmax=1,title="input smoothed")
# plot2(s1,s2,g,ff,gmin=0,gmax=1,label="Fault likelihood",png="flgs")
plot2(s1, s2, g, label="Log amplitude", png="gs")
p = fse.slopes(g)
faults.findShifts(g, p, shiftMin, shiftMax)
faults.clean()
s = faults.getShifts()
s = mul(s1.delta * 1000.0, s)
print "s min =", min(s), " max =", max(s)
def goShifts(): s1,s2,g = getImage() g = slog(g) plot2(s1,s2,g,title="log input") fse = FaultSemblance() g = fse.taper(10,g) p = fse.slopes(g) sn,sd = fse.semblanceNumDen(p,g) fsc = FaultScanner2(sigmaTheta,[sn,sd],smoother) f,t = fsc.scan(-15,15) plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood") #ff,tt = fsc.thin([f,t]) #plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood") shiftMin,shiftMax = -20,20 faults = fsc.findFaults([f,t],shiftMax-shiftMin); ff = faults.getLikelihoods() plot2(s1,s2,g,ff,gmin=0,gmax=1,title="fault likelihood") g = fsc.smooth(4,p,ff,g) plot2(s1,s2,g,ff,gmin=0,gmax=1,title="input smoothed") p = fse.slopes(g) faults.findShifts(g,p,shiftMin,shiftMax) faults.clean() s = faults.getShifts() print "s min =",min(s)," max =",max(s) plot2(s1,s2,g,s,gmin=-8,gmax=8,title="fault throws")
def goThin():
s1, s2, g = getImage()
g = slog(g)
# plot2(s1,s2,g,title="log input")
fse = FaultSemblance()
g = fse.taper(10, g)
for iter in range(1):
p = fse.slopes(g)
# p = zerofloat(len(p[0]),len(p))
sn, sd = fse.semblanceNumDen(p, g)
fsc = FaultScanner2(sigmaTheta, [sn, sd], smoother)
f, t = fsc.scan(-15, 15)
# plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood")
# plot2(s1,s2,g,t,title="fault dip (degrees)")
fs = copy(f)
RecursiveGaussianFilter(1.0).apply00(fs, fs)
# plot2(s1,s2,g,fs,gmin=0,gmax=1,title="fault likelihood smoothed")
plot2(s1, s2, g, fs, gmin=0.5, gmax=1, gmap=jetr, label="Fault likelihood")
ft, tt = fsc.thin([f, t])
# plot2(s1,s2,g,ft,gmin=0,gmax=1,title="fault likelihood thinned")
# plot2(s1,s2,g,tt,title="fault dip (degrees) thinned")
plot2(s1, s2, g, ft, gmin=0.5, gmax=1, gmap=jetr, label="Fault likelihood", png="flt")
plot2(s1, s2, g, tt, gmap=bwrn, label="Fault dip (degrees)", png="ftt")
g = fsc.smooth(8, p, ft, g)
# plot2(s1,s2,g,title="input smoothed")
plot2(s1, s2, g, label="Log amplitude", png="gs")
def goThin():
s1,s2,g = getImage()
g = slog(g)
#plot2(s1,s2,g,title="log input")
fse = FaultSemblance()
g = fse.taper(10,g)
for iter in range(1):
p = fse.slopes(g)
#p = zerofloat(len(p[0]),len(p))
sn,sd = fse.semblanceNumDen(p,g)
fsc = FaultScanner2(sigmaTheta,[sn,sd],smoother)
f,t = fsc.scan(-15,15)
#plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood")
#plot2(s1,s2,g,t,title="fault dip (degrees)")
fs = copy(f); RecursiveGaussianFilter(1.0).apply00(fs,fs)
#plot2(s1,s2,g,fs,gmin=0,gmax=1,title="fault likelihood smoothed")
plot2(s1,s2,g,fs,gmin=0.5,gmax=1,gmap=jetr,label="Fault likelihood")
ft,tt = fsc.thin([f,t])
#plot2(s1,s2,g,ft,gmin=0,gmax=1,title="fault likelihood thinned")
#plot2(s1,s2,g,tt,title="fault dip (degrees) thinned")
plot2(s1,s2,g,ft,gmin=0.5,gmax=1,gmap=jetr,
label="Fault likelihood",png="flt")
plot2(s1,s2,g,tt,gmap=bwrn,label="Fault dip (degrees)",png="ftt")
g = fsc.smooth(8,p,ft,g)
#plot2(s1,s2,g,title="input smoothed")
plot2(s1,s2,g,label="Log amplitude",png="gs")
def goSlopes(): s1,s2,g = getImage() plot2(s1,s2,g,title="input") g = slog(g) plot2(s1,s2,g,title="log input") fse = FaultSemblance() p = fse.slopes(g) plot2(s1,s2,g,p,gmin=-0.9,gmax=0.9,title="slopes")
def goSlopes(): s1,s2,g = getImage() plot2(s1,s2,g,title="input") g = slog(g) plot2(s1,s2,g,title="log input") fse = FaultSemblance() p = fse.slopes(g) plot2(s1,s2,g,p,gmin=-0.9,gmax=0.9,title="slopes")
def goSlopes(): s1,s2,g = getImage() #plot2(s1,s2,g,title="input") plot2(s1,s2,g,label="Amplitude",png="ga") g = slog(g) #plot2(s1,s2,g,title="log input") plot2(s1,s2,g,label="Log amplitude",png="g") fse = FaultSemblance() p = fse.slopes(g) p = mul(s1.delta/s2.delta,p) #plot2(s1,s2,g,p,gmin=-0.9,gmax=0.9,title="slopes") plot2(s1,s2,g,p,gmin=-0.07,gmax=0.07,gmap=bwrf,label="Slope (s/km)",png="p")
def goSlopes():
s1, s2, g = getImage()
# plot2(s1,s2,g,title="input")
plot2(s1, s2, g, label="Amplitude", png="ga")
g = slog(g)
# plot2(s1,s2,g,title="log input")
plot2(s1, s2, g, label="Log amplitude", png="g")
fse = FaultSemblance()
p = fse.slopes(g)
p = mul(s1.delta / s2.delta, p)
# plot2(s1,s2,g,p,gmin=-0.9,gmax=0.9,title="slopes")
plot2(s1, s2, g, p, gmin=-0.15, gmax=0.15, gmap=bwrf, label="Slope (s/km)", png="p")
def goScan():
s1,s2,g = getImage()
g = slog(g)
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn,sd = fse.semblanceNumDen(p,g)
fsc = FaultScanner2(sigmaTheta,[sn,sd])
st = Sampling(31,1.0,-15.0)
for theta in st.values:
f = fsc.likelihood(theta)
plot2(s1,s2,g,f,gmin=0,gmax=1,title="theta = "+str(int(theta)))
tmin,tmax = st.first,st.last
f,t = fsc.scan(tmin,tmax)
plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood")
plot2(s1,s2,g,t,gmin=tmin,gmax=tmax,title="fault dip (degrees)")
def goScan():
s1,s2,g = getImage()
g = slog(g)
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn,sd = fse.semblanceNumDen(p,g)
fsc = FaultScanner2(sigmaTheta,[sn,sd])
st = Sampling(31,1.0,-15.0)
for theta in st.values:
f = fsc.likelihood(theta)
png = "fl"+str(int(theta))
plot2(s1,s2,g,f,gmin=0.5,gmax=1,gmap=jetr,label="Fault likelihood",png=png)
tmin,tmax = st.first,st.last
f,t = fsc.scan(tmin,tmax)
plot2(s1,s2,g,f,gmin=0.5,gmax=1,gmap=jetr,label="Fault likelihood",png="fl")
def goScan():
s1,s2,g = getImage()
g = slog(g)
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn,sd = fse.semblanceNumDen(p,g)
fsc = FaultScanner2(sigmaTheta,[sn,sd])
st = Sampling(31,1.0,-15.0)
for theta in st.values:
f = fsc.likelihood(theta)
png = "fl"+str(int(theta))
plot2(s1,s2,g,f,gmin=0,gmax=1,gmap=jetr,label="Fault likelihood",png=png)
tmin,tmax = st.first,st.last
f,t = fsc.scan(tmin,tmax)
plot2(s1,s2,g,f,gmin=0,gmax=1,gmap=jetr,label="Fault likelihood",png="fl")
def goScan():
s1,s2,g = getImage()
g = slog(g)
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn,sd = fse.semblanceNumDen(p,g)
fsc = FaultScanner2(sigmaTheta,[sn,sd])
st = Sampling(31,1.0,-15.0)
for theta in st.values:
f = fsc.likelihood(theta)
plot2(s1,s2,g,f,gmin=0,gmax=1,title="theta = "+str(int(theta)))
tmin,tmax = st.first,st.last
f,t = fsc.scan(tmin,tmax)
plot2(s1,s2,g,f,gmin=0,gmax=1,title="fault likelihood")
plot2(s1,s2,g,t,gmin=tmin,gmax=tmax,title="fault dip (degrees)")
def goAlign(): s1,s2,g = getImage() g = slog(g) n1,n2 = len(g[0]),len(g) fse = FaultSemblance() p = fse.slopes(g) ref = RecursiveExponentialFilter(4) sn,sd = fse.semblanceNumDen(p,g) ref.apply1(sn,sn) ref.apply1(sd,sd) s = fse.semblanceFromNumDen(sn,sd) plot2(s1,s2,g,s,gmin=0,gmax=1,title="semblance with alignment") p = zerofloat(n1,n2) # semblance with zero slopes sn,sd = fse.semblanceNumDen(p,g) ref.apply1(sn,sn) ref.apply1(sd,sd) s = fse.semblanceFromNumDen(sn,sd) plot2(s1,s2,g,s,gmin=0,gmax=1,title="semblance without alignment")
def goSemblance():
s1,s2,g = getImage()
g = slog(g)
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn0,sd0 = fse.semblanceNumDen(p,g)
print "semblances for different vertical smoothings:"
for sigma in [0,2,4,8]:
ref = RecursiveExponentialFilter(sigma)
sn = copy(sn0)
sd = copy(sd0)
ref.apply1(sn,sn)
ref.apply1(sd,sd)
s = fse.semblanceFromNumDen(sn,sd)
print "sigma =",sigma," s min =",min(s)," max =",max(s)
title = "semblance: sigma = "+str(sigma)
plot2(s1,s2,g,s,gmin=0,gmax=1,title=title)
def goAlign(): s1,s2,g = getImage() g = slog(g) n1,n2 = len(g[0]),len(g) fse = FaultSemblance() p = fse.slopes(g) ref = RecursiveExponentialFilter(4) sn,sd = fse.semblanceNumDen(p,g) ref.apply1(sn,sn) ref.apply1(sd,sd) s = fse.semblanceFromNumDen(sn,sd) plot2(s1,s2,g,s,gmin=0,gmax=1,title="semblance with alignment") p = zerofloat(n1,n2) # semblance with zero slopes sn,sd = fse.semblanceNumDen(p,g) ref.apply1(sn,sn) ref.apply1(sd,sd) s = fse.semblanceFromNumDen(sn,sd) plot2(s1,s2,g,s,gmin=0,gmax=1,title="semblance without alignment")
def goSemblance():
s1,s2,g = getImage()
g = slog(g)
fse = FaultSemblance()
g = fse.taper(10,g)
p = fse.slopes(g)
sn0,sd0 = fse.semblanceNumDen(p,g)
print "semblances for different vertical smoothings:"
for sigma in [0,2,4,8]:
ref = RecursiveExponentialFilter(sigma)
sn = copy(sn0)
sd = copy(sd0)
ref.apply1(sn,sn)
ref.apply1(sd,sd)
s = fse.semblanceFromNumDen(sn,sd)
print "sigma =",sigma," s min =",min(s)," max =",max(s)
title = "semblance: sigma = "+str(sigma)
plot2(s1,s2,g,s,gmin=0,gmax=1,title=title)
