def create_constptb_loc(nz, nx, ptb, naz, nax, cz, cx, rectx=20, rectz=20):
"""
Creates a constant perturbation of size nax by nax and at position
(cz,cx)
Parameters
nz - number of depth samples of output velocity model
nx - number of lateral samples of output velocity model
ptb - percent perturbation
naz - number of depth samples of perturbation
nax - number of lateral samples of perturbation
cz - z center position of anomaly
cx - x center position of anomaly
rectx - number of points to smooth in x [20]
rectz - number of points to smooth in z [20]
Returns a model [nz,nx] with the anomaly positioned at (cz,cx) in the model
"""
velc = np.zeros([naz, nax], dtype='float32') + ptb
pz1 = cz - int(naz / 2)
if (naz % 2 != 0): pz1 -= 1
if (pz1 < 0):
cz = int(naz / 2)
pz2 = nz - cz - int(naz / 2)
px1 = cx - int(nax / 2)
if (nax % 2 != 0): px1 -= 1
if (px1 < 0):
cx = int(nax / 2)
px2 = nx - cx - int(nax / 2)
velcp = np.pad(velc, ((pz1, pz2), (px1, px2)),
'constant',
constant_values=1)
return smooth(velcp, rect1=rectx, rect2=rectz)
def focdefocang(img,
mdl,
nzp=64,
nxp=64,
strdz=None,
strdx=None,
rectx=30,
rectz=30,
verb=False):
"""
Classifies an angle gather as focused or defocused
Parameters:
img - the input extended image [na,nz,nx]
mdl - the trained keras model
nzp - z-dimension of the patch provided to the CNN [64]
nxp - x-dimension of the patch provided to the CNN [64]
strdz - z-dimension of the patch stride (50% overlap) [npz/2]
strdx - x-dimension of the patch stride (50% overlap) [npx/2]
rectz - number of points to smooth in z direction [30]
rectx - number of points to smooth in x direction [30]
Returns a smooth probability map of focused/defocused faults
"""
# Get image dimensions
na = img.shape[0]
nz = img.shape[1]
nx = img.shape[2]
# Get strides
if (strdz is None): strdz = int(nzp / 2)
if (strdx is None): strdx = int(nxp / 2)
# Build the Patch Extractors
pea = PatchExtractor((na, nzp, nxp), stride=(na, strdz, strdx))
aptch = np.squeeze(pea.extract(img))
# Flatten patches and make a prediction on each
numpz = aptch.shape[0]
numpx = aptch.shape[1]
aptchf = np.expand_dims(normalize(
aptch.reshape([numpz * numpx, na, nzp, nxp])),
axis=-1)
focprd = mdl.predict(aptchf)
focprdptch = np.zeros([numpz * numpx, nzp, nxp])
for iptch in range(numpz * numpx):
focprdptch[iptch, :, :] = focprd[iptch]
focprdptch = focprdptch.reshape([numpz, numpx, nzp, nxp])
# Output probabilities
per = PatchExtractor((nzp, nxp), stride=(strdz, strdx))
focprdimg = np.zeros([nz, nx])
_ = per.extract(focprdimg)
focprdimg = per.reconstruct(focprdptch.reshape([numpz, numpx, nzp, nxp]))
focprdimgsm = smooth(focprdimg.astype('float32'), rect1=rectx, rect2=rectz)
return focprdimgsm
def salt_mask(img, vel, saltvel, thresh=0.95, rectx=30, rectz=30):
"""
Masks the image based on the salt velocity
Parameters
img - input image (can be extended or not) [nhy,nhx,nz,ny,nx]
vel - input migration velocity model [nz,ny,nx]
saltvel - salt velocity for masking
thresh - mask threshold [0.95]
rectx - amount of smoothing to be applied to mask along x
rectz - amount of smoothing to be applied to mask along z
Returns mask and masked image (msk,maskedimg)
"""
# Create the mask
idx = vel >= saltvel
msk = np.ascontiguousarray(np.copy(vel)).astype('float32')
msk[idx] = 0.0
msk[~idx] = 1.0
if (len(msk.shape) == 3):
mskw = msk[:, 0, :]
else:
mskw = msk
# Smooth and threshold the mask
smmsk = smooth(mskw, rect1=30, rect2=30)
idx2 = smmsk > 0.95
smmsk[idx2] = 1.0
smmsk[~idx2] = 0.0
smmsk2 = smooth(smmsk, rect1=2, rect2=2)
# Apply the mask to the image
imgo = np.zeros(img.shape, dtype='float32')
if (len(img.shape) == 5):
nhx = img.shape[1]
for ihx in range(nhx):
imgo[0, ihx, :, 0, :] = smmsk2 * img[0, ihx, :, 0, :]
elif (len(img.shape) == 3):
imgo = smmsk2 * img[:, 0, :]
elif (len(img.shape) == 2):
imgo = smmsk2 * img
return smmsk2, imgo
def semblance_power(img, transp=False):
"""
A semblance metric for measuring flatness of angle gathers.
Parameters:
img - the input image [na,nx,nx]
"""
if (len(img.shape) != 3):
raise Exception("Input image must be 3D")
stack = np.sum(img, axis=0)
stacksq = stack * stack
num = smooth(stacksq.astype('float32'), rect1=3, rect2=10)
sqstack = np.sum(img * img, axis=0)
denom = smooth(sqstack.astype('float32'), rect1=3, rect2=10)
semb = num / denom
return np.sum(semb)
def fpr_mask(self,lbltm,lbltn,dec=0.9,rectdecay=10,rectspread=3):
"""
Creates a mask for creating the effect of a fault plane reflection
along a fault
Parameters:
lbltm - the normalized fault displacement
lbltn - the fault label (thresholded fault displacement)
dec - the percent decrease of the velocity along the fault
(if None, randomly selected between 90-95%)
rectdecay - smoothing length that controls the decay of
the reflection [10 points]
rectspread - smoothing length that controls the spread of the reflection [3 points]
"""
# Create a mask that smoothly increases from dec to 1.0
lbltmsm = smooth(lbltm,rect1=rectdecay,rect2=rectdecay)
mcomp = 1 - lbltmsm # Mask complement
mcomp += 1 - np.min(mcomp) # Bring up to 1.0
ampmask = mcomp*lbltn
# Set the entire label to be velocity decrease percent
fpmask = 1-lbltn
zidx = fpmask == 0
fpmask[zidx] = dec
# Based on this value, change the max on the amplitude mask (avoids going over one)
newmax = 1/dec
midx = ampmask > newmax
ampmask[midx] = newmax
# Scale the constant velocity decrease by the amplitude mask
fpmask *= ampmask
# Set all zeros to ones
zidx = fpmask == 0
fpmask[zidx] = 1.0
# Smooth to spread out over a few pixels
fpmasksm = smooth(fpmask,rect1=rectspread,rect2=rectspread)
return fpmasksm
def rho_semb(stormang,gagc=True,norm=True,rectz=10,rectro=3,nthreads=1):
"""
Computes semblance from residually migrated angle gathers
Parameters:
stormang - Stolt resiudally migrated angle gathers [nro,nx,na,nz]
gagc - Apply agc [True]
rectz - Smoothing window along z direction [10 points]
rectro - Smoothing window along rho direction [3 points]
Returns a semblance cube [nx,nro,nz]
"""
# Get dimensions
nro,nx,na,nz = stormang.shape
# Compute agc
if(gagc):
angs = np.asarray(Parallel(n_jobs=nthreads)(delayed(agc)(stormang[iro]) for iro in range(nro)))
else:
angs = stormang
# Compute semblance
stackg = np.sum(angs,axis=2)
stacksq = stackg*stackg
num = smooth(stacksq.astype('float32'),rect1=rectz,rect3=rectro)
sqstack = np.sum(angs*angs,axis=2)
den = smooth(sqstack.astype('float32'),rect1=rectz,rect3=rectro)
semb = num/den
sembt = np.transpose(semb,(1,0,2)) # [nro,nx,nz] -> [nx,nro,nz]
if(norm):
sembt /= np.max(sembt)
return sembt
def smooth_model(self,rect1=2,rect2=2,rect3=2,sigma=None):
"""
Applies either a triangular or gaussian smoother to the velocity model.
Default is a triangular smoother
Parameters
rect1 - Length of triangular filter along z-axis [2 gridpoints]
rect2 - Length of triangular filter along x-axis [2 gridpoints]
rect3 - Length of triangular filter along y-axis [2 gridpoints]
sigma - size of gaussian filter [None]
"""
if(sigma is not None):
self.vel = gaussian_filter(self.vel,sigma=sigma).astype('float32')
else:
self.vel = smooth(self.vel,rect1=rect1,rect2=rect2,rect3=rect3)
def detectfaultpatch(img,
mdl,
nzp=64,
nxp=64,
strdz=None,
strdx=None,
rectx=30,
rectz=30,
verb=False):
"""
Detects if a fault is present within an image or not
Parameters:
img - the input image [nz,nx]
mdl - the trained keras model
nzp - z-dimension of the patch provided to the CNN [128]
nxp - x-dimension of the patch provided to the CNN [128]
strdz - z-dimension of the patch stride (50% overlap) [npz/2]
strdx - x-dimension of the patch stride (50% overlap) [npx/2]
rectz - number of points to smooth in z direction [30]
rectx - number of points to smooth in x direction [30]
Returns a smooth probability map of detected faults
"""
# Resample to nearest power of 2
rimg = resizepow2(img, kind='linear')
# Perform the patch extraction
if (strdz is None): strdz = int(nzp / 2)
if (strdx is None): strdx = int(nxp / 2)
pe = PatchExtractor((nzp, nxp), stride=(strdx, strdz))
iptch = pe.extract(rimg)
numpz = iptch.shape[0]
numpx = iptch.shape[1]
iptch = iptch.reshape([numpx * numpz, nzp, nxp, 1])
# Normalize and predict for each patch
iprd = np.zeros(iptch.shape)
for ip in range(numpz * numpx):
iprd[ip, :, :] = mdl.predict(
np.expand_dims(normalize(iptch[ip, :, :]), axis=0))
# Reconstruct the predictions
ipra = iprd.reshape([numpz, numpx, nzp, nxp])
iprb = pe.reconstruct(ipra)
# Smooth the predictions
smprb = smooth(iprb.astype('float32'), rect1=rectx, rect2=rectz)
return smprb
def agc(dat, rect1=125, transp=False):
"""
Applies an automatic gain control (AGC) to the data/image
Applies it trace by trace (assumes t or z is the fast axis)
Parameters:
dat - the input data/image [nx,nt/nz]
rect1 - size of AGC window along the fast axis
transp - transpose a 2D image so that t/z is the fast axis
Returns the gained data
"""
if (transp):
dat = np.ascontiguousarray(dat.T)
# First compute the absolute value of the data
databs = np.abs(dat)
# Smooth the absolute value
databssm = smooth(databs, rect1=rect1)
# Divide by the smoothed amplitude
idx = databssm <= 0
databssm[idx] = 1
return dat / databssm
def anglemask(nz,na,zpos=None,apos=None,rectz=10,recta=10,mode='slant',rand=True,verb=False):
"""
Creates an angle mask that can be applied to an
angle gather to limit the number of angles (illumination)
Parameters:
nz - number of depth samples
na - number of angle samples
zpos - percentage in z where to begin the mask [0.3]
apos - percentage in a where to end the mask [0.6]
rectz - number of points to smooth the mask in z [10]
recta - number of points to smooth the mask in a [10]
mode - mode of creating mask. Either a vertical mask ('vert') or slanted ['slant']
rand - add smooth random variation to the mask [True]
verb - verbosity flag [False]
Returns a single angle gather mask [nz,na]
"""
# Create z(a) linear function
if(zpos is None):
z0 = int(0.3*nz)
else:
z0 = int(zpos*nz)
if(apos is None):
a0 = int(0.6*na)
else:
a0 = int(apos*na)
if(mode == 'vert'):
if(apos > 0.5):
raise Exception("Please a choose an a0 < 0.5 for vertical mask mode")
abeg = a0; aend = na-a0
mask = np.ones([nz,na])
mask[:,0:abeg] = 0.0
mask[:,aend:] = 0.0
if(verb):
print("z0=%d a0=%d af=%d"%(z0,a0,aend))
elif(mode == 'slant'):
zf = nz-1; af = na-1
if(verb):
print("z0=%d a0=%d zf=%d af=%d"%(z0,a0,zf,af))
# Slope and intercept
m = (zf - z0)/(a0 - af)
b = (z0*a0 - zf*af)/(a0 - af)
a = np.array(range(a0,na))
zofa = (a*m + b).astype(int)
if(rand):
noise = 250*perlin(x=np.linspace(0,2,len(zofa)), octaves=2, persist=0.3, ncpu=1)
noise = noise - np.mean(noise)
zofa += noise.astype(int)
# Create mask
liner = np.ones([nz,na])
j = 0
for ia in a:
liner[zofa[j]:,ia] = 0
j += 1
# Flip for symmetry
linel = np.fliplr(liner)
mask = linel*liner
masksm = smooth(mask.astype('float32'),rect1=recta,rect2=rectz)
return masksm
def estro_varimax(rimgs,dro,oro,nzp=64,nxp=64,strdz=None,strdx=None,rectz=30,rectx=30,qcimgs=True):
"""
Estimates rho by choosing the residually migrated patch that has
the highest image entropy computed via the varimax norm
Parameters
rimgs - residually migrated images [nro,nz,nx]
dro - residual migration sampling
oro - residual migration origin
nzp - size of patch in z dimension [64]
nxp - size of patch in x dimension [64]
strdz - size of stride in z dimension [nzp/2]
strdx - size of stride in x dimension [nxp/2]
rectz - length of smoother in z dimension [30]
rectx - length of smoother in x dimension [30]
qcimgs - flag for returning the smoothed varimax norm [nro,nz,nx]
Returns an estimate of rho(x,z)
"""
# Get image dimensions
nro = rimgs.shape[0]; nz = rimgs.shape[1]; nx = rimgs.shape[2]
# Get strides
if(strdz is None): strdz = int(nzp/2)
if(strdx is None): strdx = int(nxp/2)
# Extract patches from residual migration image
per = PatchExtractor((nro,nzp,nxp),stride=(nro,strdz,strdx))
rptch = np.squeeze(per.extract(rimgs))
# Flatten patches and make a prediction on each
numpz = rptch.shape[0]; numpx = rptch.shape[1]
nptch = nro*numpz*numpx
rptchf = rptch.reshape([nptch,nzp,nxp])
norm = np.zeros(rptchf.shape)
for iptch in range(nptch):
norm[iptch,:,:] = varimax(rptchf[iptch])
# Assign prediction to entire patch for QC
normptch = norm.reshape([numpz,numpx,nro,nzp,nxp])
# Output rho image
rho = np.zeros([nz,nx])
pe = PatchExtractor((nzp,nxp),stride=(strdz,strdx))
rhop = pe.extract(rho)
# Using hasfault array, estimate rho from fault focus probabilities
hlfz = int(nzp/2); hlfx = int(nxp/2)
for izp in range(numpz):
for ixp in range(numpx):
# Find maximum entropy and compute rho
ient = normptch[izp,ixp,:,hlfz,hlfx]
rhop[izp,ixp,:,:] = np.argmax(ient)*dro + oro
# Reconstruct the rho, fault patches and fault probabiliites
rho = pe.reconstruct(rhop)
normimg = per.reconstruct(normptch.reshape([1,numpz,numpx,nro,nzp,nxp]))
# Smooth and return rho, fault patches and fault probabilities
rhosm = smooth(rho.astype('float32'),rect1=rectx,rect2=rectz)
if(qcimgs):
normimgsm = np.zeros(normimg.shape)
# Smooth the fault focusing for each rho
for iro in range(nro):
normimgsm[iro] = smooth(normimg[iro].astype('float32'),rect1=rectx,rect2=rectz)
# Return images
return rhosm,normimgsm
else:
return rhosm
def estro_fltangfocdefoc(rimgs,foccnn,dro,oro,nzp=64,nxp=64,strdz=None,strdx=None, # Patching parameters
rectz=30,rectx=30,fltthresh=75,fltlbls=None,qcimgs=True,verb=False,
fmwrk='torch',device=None):
"""
Estimates rho by choosing the residually migrated patch that has
highest angle gather and fault focus probability given by the neural network
Parameters
rimgs - residually migrated angle gathers images [nro,na,nz,nx]
foccnn - CNN for determining if angle gather/fault is focused or not
dro - residual migration sampling
oro - residual migration origin
nzp - size of patch in z dimension [64]
nxp - size of patch in x dimension [64]
strdz - size of stride in z dimension [nzp/2]
strdx - size of stride in x dimension [nxp/2]
rectz - length of smoother in z dimension [30]
rectx - length of smoother in x dimension [30]
fltlbls - input fault segmentation labels [None]
qcimgs - flag for returning the fault focusing probabilities [nro,nz,nx]
and fault patches [nz,nx]
verb - verbosity flag [False]
fmwrk - deep learning framework to be used for the prediction [torch]
device - device for pytorch networks
Returns an estimate of rho(x,z)
"""
# Get image dimensions
nro = rimgs.shape[0]; na = rimgs.shape[1]; nz = rimgs.shape[2]; nx = rimgs.shape[3]
# Get strides
if(strdz is None): strdz = int(nzp/2)
if(strdx is None): strdx = int(nxp/2)
# Build the Patch Extractors
pea = PatchExtractor((nro,na,nzp,nxp),stride=(nro,na,strdz,strdx))
aptch = np.squeeze(pea.extract(rimgs))
# Flatten patches and make a prediction on each
numpz = aptch.shape[0]; numpx = aptch.shape[1]
if(fmwrk == 'torch'):
aptchf = normalize(aptch.reshape([nro*numpz*numpx,1,na,nzp,nxp]))
with(torch.no_grad()):
aptchft = torch.tensor(aptchf)
focprdt = torch.zeros([nro*numpz*numpx,1])
for iptch in progressbar(range(aptchf.shape[0]),verb=verb):
gptch = aptchft[iptch].to(device)
focprdt[iptch] = torch.sigmoid(foccnn(gptch.unsqueeze(0)))
focprd = focprdt.cpu().numpy()
elif(fmwrk == 'tf'):
aptchf = np.expand_dims(normalize(aptch.reshape([nro*numpz*numpx,na,nzp,nxp])),axis=-1)
focprd = foccnn.predict(aptchf,verbose=verb)
elif(fmwrk is None):
aptchf = normalize(aptch.reshape([nro*numpz*numpx,na,nzp,nxp]))
focprd = np.zeros([nro*numpz*numpx,1])
for iptch in progressbar(range(aptchf.shape[0]),verb=verb):
focprd[iptch] = semblance_power(aptchf[iptch])
# Assign prediction to entire patch for QC
focprdptch = np.zeros([numpz*numpx*nro,nzp,nxp])
for iptch in range(nro*numpz*numpx): focprdptch[iptch,:,:] = focprd[iptch]
focprdptch = focprdptch.reshape([numpz,numpx,nro,nzp,nxp])
# Save predictions as a function of rho only
focprdr = focprd.reshape([numpz,numpx,nro])
# Output rho image
pe = PatchExtractor((nzp,nxp),stride=(strdz,strdx))
rho = np.zeros([nz,nx])
rhop = pe.extract(rho)
# Output probabilities
focprdnrm = np.zeros(focprdptch.shape)
per = PatchExtractor((nro,nzp,nxp),stride=(nro,strdz,strdx))
focprdimg = np.zeros([nro,nz,nx])
_ = per.extract(focprdimg)
if(fltlbls is None):
fltptch = np.ones([numpz,numpx,nzp,nxp],dtype='int')
else:
pef = PatchExtractor((nzp,nxp),stride=(strdz,strdx))
fltptch = pef.extract(fltlbls)
# Estimate rho from angle-fault focus probabilities
hlfz = int(nzp/2); hlfx = int(nxp/2)
for izp in range(numpz):
for ixp in range(numpx):
if(np.sum(fltptch[izp,ixp]) > fltthresh):
# Find maximum probability and compute rho
iprb = focprdptch[izp,ixp,:,hlfz,hlfx]
rhop[izp,ixp,:,:] = np.argmax(iprb)*dro + oro
# Normalize across rho within a patch for QC
if(np.max(focprdptch[izp,ixp,:,hlfz,hlfx]) == 0.0):
focprdnrm[izp,ixp,:,:,:] = 0.0
else:
focprdnrm[izp,ixp,:,:,:] = focprdptch[izp,ixp,:,:,:]/np.max(focprdptch[izp,ixp,:,hlfz,hlfx])
else:
rhop[izp,ixp,:,:] = 1.0
# Reconstruct rho and probabiliites
rho = pe.reconstruct(rhop)
focprdimg = per.reconstruct(focprdnrm.reshape([1,numpz,numpx,nro,nzp,nxp]))
# Smooth and return rho, fault patches and fault probabilities
rhosm = smooth(rho.astype('float32'),rect1=rectx,rect2=rectz)
if(qcimgs):
focprdimgsm = np.zeros(focprdimg.shape)
# Smooth the fault focusing for each rho
for iro in range(nro):
focprdimgsm[iro] = smooth(focprdimg[iro].astype('float32'),rect1=rectx,rect2=rectz)
# Return images
return rhosm,focprdimgsm,focprdr
else:
return rhosm
def estro_fltfocdefoc(rimgs,foccnn,dro,oro,nzp=64,nxp=64,strdz=None,strdx=None, # Patching parameters
hasfault=None,rectz=30,rectx=30,qcimgs=True):
"""
Estimates rho by choosing the residually migrated patch that has
highest fault focus probability given by the neural network
Parameters
rimgs - residually migrated images [nro,nz,nx]
foccnn - CNN for determining if fault is focused or not
dro - residual migration sampling
oro - residual migration origin
nzp - size of patch in z dimension [64]
nxp - size of patch in x dimension [64]
strdz - size of stride in z dimension [nzp/2]
strdx - size of stride in x dimension [nxp/2]
hasfault - array indicating if a patch has faults or not [None]
If None, all patches are considered to have faults
rectz - length of smoother in z dimension [30]
rectx - length of smoother in x dimension [30]
qcimgs - flag for returning the fault focusing probabilities [nro,nz,nx]
and fault patches [nz,nx]
Returns an estimate of rho(x,z)
"""
# Get image dimensions
nro = rimgs.shape[0]; nz = rimgs.shape[1]; nx = rimgs.shape[2]
# Get strides
if(strdz is None): strdz = int(nzp/2)
if(strdx is None): strdx = int(nxp/2)
# Extract patches from residual migration image
per = PatchExtractor((nro,nzp,nxp),stride=(nro,strdz,strdx))
rptch = np.squeeze(per.extract(rimgs))
# Flatten patches and make a prediction on each
numpz = rptch.shape[0]; numpx = rptch.shape[1]
rptchf = np.expand_dims(normalize(rptch.reshape([nro*numpz*numpx,nzp,nxp])),axis=-1)
focprd = foccnn.predict(rptchf)
# Assign prediction to entire patch for QC
focprdptch = np.zeros(rptchf.shape)
for iptch in range(nro*numpz*numpx): focprdptch[iptch,:,:] = focprd[iptch]
focprdptch = focprdptch.reshape([numpz,numpx,nro,nzp,nxp])
if(hasfault is None):
hasfault = np.ones([numpz,numpx,nzp,nxp],dtype='int')
# Output rho image
rho = np.zeros([nz,nx])
pe = PatchExtractor((nzp,nxp),stride=(strdz,strdx))
rhop = pe.extract(rho)
# Using hasfault array, estimate rho from fault focus probabilities
hlfz = int(nzp/2); hlfx = int(nxp/2)
for izp in range(numpz):
for ixp in range(numpx):
if(hasfault[izp,ixp,hlfz,hlfx]):
# Find maximum probability and compute rho
iprb = focprdptch[izp,ixp,:,hlfz,hlfx]
rhop[izp,ixp,:,:] = np.argmax(iprb)*dro + oro
else:
rhop[izp,ixp,:,:] = 1.0
# Reconstruct the rho, fault patches and fault probabiliites
rho = pe.reconstruct(rhop)
focprdimg = per.reconstruct(focprdptch.reshape([1,numpz,numpx,nro,nzp,nxp]))
# Smooth and return rho, fault patches and fault probabilities
rhosm = smooth(rho.astype('float32'),rect1=rectx,rect2=rectz)
if(qcimgs):
focprdimgsm = np.zeros(focprdimg.shape)
# Smooth the fault focusing for each rho
for iro in range(nro):
focprdimgsm[iro] = smooth(focprdimg[iro].astype('float32'),rect1=rectx,rect2=rectz)
# Return images
return rhosm,focprdimgsm
else:
return rhosm