displays multiple stacks ''' import toolbox import pylab import numpy as np filelist = ["1st_vels_stack.su", #"1st_vels_stack_elev.su", "fk_stack.su", #"model.su", "model_filtered.su", #"trim_stack.su", "trim_stack2.su", ] dataset, params = toolbox.initialise(filelist[0]) dataset['fldr'] = 0 for index, file in enumerate(filelist[1:]): data, junk = toolbox.initialise(file) data['fldr'] = index + 1 dataset = np.column_stack([dataset, data]) params['window'] = 1000 toolbox.agc(dataset, None, **params) params['primary'] = 'fldr' params['secondary'] = 'cdp' params['clip'] = 0.02 toolbox.display(dataset, **params) pylab.show()
@io
def add_noise(dataset, **kwargs):
noise = np.random.normal(0.0, 1e-8, size=(dataset.shape))
dataset += noise
return dataset
@io
def convolve_wavelet(dataset, **kwargs):
wavelet = toolbox.ricker(60)
dataset = toolbox.conv(dataset, wavelet)
return dataset
if __name__ == '__main__':
#initialise
workspace, params = initialise()
#build record
build_combined(workspace, None, **params)
#add wavelet
workspace = convolve_wavelet(workspace, None, **params)
#add noise
workspace = add_noise(workspace, None, **params)
#display
toolbox.agc(workspace, None, **params)
toolbox.display(workspace, None, **params)
parameters['offset'] = parameters['gx'] - parameters['sx']
parameters['aoffsets'] = np.abs(parameters['offset'])
#return workspace and parameters
return workspace, parameters
if __name__ == '__main__':
#initialise
workspace, params = initialise()
#check dictionary contents
print params['model'].keys()
#have a look at it - it has a build in display routine
params['model'].display()
#add spikes
spike(workspace, None, **params)
#display
toolbox.display(workspace, None, **params)
print dataset['cdp']
params['primary'] = 'cdp'
params['secondary'] = 'offset'
params['step'] = 20
#~ toolbox.display(dataset, None, **params)
#we then want to extract that single cdp
#for testing with later. we can do that
#the following way
cdp500 = dataset[dataset['cdp'] == 500]
toolbox.scan(cdp500)
#view it
#~ toolbox.display(cdp500, None, **params)
#we have the right cdp = but the traces are in the wrong
#order. lets sort by offset
cdp500 = np.sort(cdp500, order=['cdp', 'offset'])
#output it for later
toolbox.cp(cdp500, 'cdp500.su', None)
params['clip'] = 6e-4
toolbox.display(cdp500, None, **params)
pylab.show()
tar(workspace, None, **params)
#apply LMO
#~ print "applying lmo"
#~ params['lmo'] =1000.0
#~ lmo(workspace, None, **params)
#~ workspace['trace'][:,:30] *= 0
#~ workspace['trace'][:,1850:] *= 0
#~ params['lmo'] = -1000.0
#~ lmo(workspace, None, **params)
#apply our NMO
print "applying nmo"
params['smute'] = 30
v = [3000]
t = [0.5]
params['vels'] = toolbox.build_vels(t, v, ns=params['ns'])
nmo(workspace, None, **params)
#~ #apply AGC
toolbox.agc(workspace, None, **params)
#~ #stack
print "stacking"
stack(workspace, 'stack1.su', **params)
#view
params['primary'] = None
toolbox.display('stack1.su', None, **params)
pylab.show()
#~ lmo(workspace, None, **params)
#~ workspace['trace'][:,:30] *= 0
#~ workspace['trace'][:,1850:] *= 0
#~ params['lmo'] = -1000.0
#~ lmo(workspace, None, **params)
#apply our NMO
print "applying nmo"
params['smute'] = 30
v = [3000]
t = [0.5]
params['vels'] = toolbox.build_vels(t, v, ns=params['ns'])
nmo(workspace, None, **params)
#~ #apply AGC
toolbox.agc(workspace, None, **params)
#~ #stack
print "stacking"
stack(workspace, 'stack1.su', **params)
#view
params['primary'] = None
toolbox.display('stack1.su', None, **params)
pylab.show()
#apply tar
params['gamma'] = 5
toolbox.tar(workspace, None, **params)
#copy vels from previous exercise
vels = {}
vels[225] = (0.06, 1537.38) , (0.28, 2876.21) , (0.87, 4608.10)
vels[270] = (0.05, 1525.09) , (0.18, 2483.16) , (0.36, 3171.00) , (0.66, 4079.93) , (0.98, 4816.90)
vels[315] = (0.04, 1365.42) , (0.14, 2728.82) , (0.22, 3134.15) , (0.57, 4116.78) , (0.74, 4571.25) , (0.97, 5013.43)
vels[360] = (0.04, 1697.05) , (0.10, 2520.01) , (0.21, 2937.62) , (0.43, 3244.70) , (0.64, 3981.67) , (0.98, 4239.61)
vels[405] = (0.06, 1439.11) , (0.27, 2753.38) , (0.49, 3957.10) , (0.97, 5381.92)
vels[450] = (0.06, 1340.85) , (0.41, 2741.10) , (0.52, 3625.47) , (0.02, 1144.32) , (0.29, 3060.45) , (0.54, 3711.45) , (0.97, 4313.31)
vels[495] = (0.04, 1611.07) , (0.11, 3072.74) , (0.23, 3318.39) , (0.35, 3772.86) , (0.48, 3981.67) , (0.94, 5099.41)
vels[539] = (0.04, 2028.69) , (0.11, 3072.74) , (0.32, 3883.41) , (0.51, 4485.27) , (0.96, 5222.24)
vels[584] = (0.06, 1623.36) , (0.20, 2495.44) , (0.32, 3121.87) , (0.95, 4411.57)
#build vels
vels = toolbox.build_vels(vels, **params)
params['primary'] = None
params['smute'] = 30
params['vels'] = vels
v100 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v100, None, **params)
section100 = toolbox.stack(v100, None, **params)
toolbox.cp(section100, 'stack100.su', None)
toolbox.display(section100, None, **params)
pylab.show()
import toolbox
import numpy as np
import pylab
#extract shot record
data, params = toolbox.initialise("prepro.su")
mask = data['fldr'] == 221
shot = data[mask].copy()
#agc
toolbox.agc(shot, None, **params)
params['primary'] = 'fldr'
params['secondary'] = 'tracf'
params['wiggle'] = True
toolbox.display(shot, **params)
#fk plot
params['dx'] = 33.5 #m
#~ toolbox.fk_view(shot, **params)
#~ #fk filter design
params['fkVelocity'] = 2000
params['fkSmooth'] = 20
params['fkFilter'] = toolbox.fk_design(shot, **params)
shot = toolbox.fk_filter(shot, None, **params)
toolbox.display(shot, **params)
##############end of testing
#~ data, nparams = toolbox.initialise("prepro.su")
refract_amps[~np.isfinite(refract_amps)] = 0.01
#it probably wont exceed 1s, but to make it look right we
#need to limit it so that it doesnt cross over the direct
directv = 330.0 #m/s
direct_times = kwargs['aoffsets'] / directv
limits = [refraction_times < direct_times]
x = kwargs['gx'][limits]
t = refraction_times[limits]
refract_amps = refract_amps[limits]
#convert coordinates to integers
x = np.floor(x).astype(np.int)
t *= 1000 # milliseconds
t = np.floor(t).astype(np.int)
#write values to array
dataset[x, t] += refract_amps
return dataset
if __name__ == '__main__':
#initialise
workspace, params = initialise()
#build refractor
build_refractor(workspace, None, **params)
#display
tmp = toolbox.agc(workspace, None, **params)
toolbox.display(tmp, None, **params)
cdps = np.unique(data['cdp'])
#recreate original velocity field
#~ vels = {}
#~ vels[753]= (2456.0, 0.153), (2772.1, 0.413), (3003.2, 0.612), (3076.1, 0.704), (3270.7, 1.056), (3367.9, 1.668), (3538.2, 2.204), (3671.9, 3.566), (3915.1, 5.908),
#~ vels[3056]=(2456.0, 0.153), (2772.1, 0.413), (3003.2, 0.612), (3076.1, 0.704), (3270.7, 1.056), (3367.9, 1.668), (3538.2, 2.204), (3671.9, 3.566), (3915.1, 5.908),
#~ params['cdp'] = cdps
#~ params['vels'] = toolbox.build_vels(vels, **params)
#~ np.array(params['vels']).tofile('vels_initial.bin')
params['vels'] = np.fromfile('vels_initial.bin').reshape(-1, params['ns'])
#~ pylab.imshow(params['vels'].T, aspect='auto')
#~ pylab.colorbar()
#~ pylab.show()
#agc and stack
toolbox.agc(data, None, **params)
params['smute'] = 200
toolbox.nmo(data, None, **params)
stack = toolbox.stack(data, None, **params)
params['gamma'] = -1
toolbox.tar(stack, None, **params)
stack.tofile('field_stack.su')
#display
params['primary'] = None
params['secondary'] = 'cdp'
toolbox.display(stack, **params)
pylab.show()
def tar(data, **kwargs):
#pull some values out of the
#paramter dictionary
gamma = kwargs['gamma']
t = kwargs['times']
#calculate the correction coeffieicnt
#applyt the correction to the data
return data
#-----------------------------------------------------------------------
# main functions
#-----------------------------------------------------------------------
if __name__ == "__main__":
#intialise dataset and parameter dictionary
#set the value of gamma you want to test here
params['gamma'] = 0
#and apply
tar(dataset, None, **params)
#and display
toolbox.display(dataset, None, **params)
pylab.show()
#build vels
vels = toolbox.build_vels(vels, **params)
params['primary'] = None
params['highcut'] = 100
params['lowcut'] = 30
params['smute'] = 30
params['vels'] = vels
v100 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v100, None, **params)
section100 = toolbox.stack(v100, None, **params)
toolbox.bandpass(section100, None, **params)
toolbox.display(section100, None, **params)
params['vels'] = vels * .9
v90 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v90, None, **params)
section90 = toolbox.stack(v90, None, **params)
toolbox.bandpass(section90, None, **params)
toolbox.display(section90, None, **params)
params['vels'] = vels *1.1
v110 = toolbox.co_nmo(workspace, None, **params)
toolbox.agc(v110, None, **params)
section110 = toolbox.stack(v110, None, **params)
toolbox.bandpass(section110, None, **params)
toolbox.display(section110, None, **params)
data, params = toolbox.initialise('prepro.su')
cdps = np.unique(data['cdp'])
#recreate original velocity field
#~ vels = {}
#~ vels[753]= (2456.0, 0.153), (2772.1, 0.413), (3003.2, 0.612), (3076.1, 0.704), (3270.7, 1.056), (3367.9, 1.668), (3538.2, 2.204), (3671.9, 3.566), (3915.1, 5.908),
#~ vels[3056]=(2456.0, 0.153), (2772.1, 0.413), (3003.2, 0.612), (3076.1, 0.704), (3270.7, 1.056), (3367.9, 1.668), (3538.2, 2.204), (3671.9, 3.566), (3915.1, 5.908),
#~ params['cdp'] = cdps
#~ params['vels'] = toolbox.build_vels(vels, **params)
#~ np.array(params['vels']).tofile('vels_initial.bin')
params['vels'] = np.fromfile('vels_initial.bin').reshape(-1, params['ns'])
#~ pylab.imshow(params['vels'].T, aspect='auto')
#~ pylab.colorbar()
#~ pylab.show()
#agc and stack
toolbox.agc(data, None, **params)
params['smute'] = 200
toolbox.nmo(data, None, **params)
stack = toolbox.stack(data, None, **params)
params['gamma'] = -1
toolbox.tar(stack, None, **params)
stack.tofile('field_stack.su')
#display
params['primary'] = None
params['secondary'] = 'cdp'
toolbox.display(stack, **params)
pylab.show()
sutype = np.result_type(dataset)
result = np.zeros(cdps.size, dtype=sutype)
for index, cdp in enumerate(cdps):
gather = dataset[dataset['cdp'] == cdp]
trace = _stack_gather(gather)
result[index] = trace
return result
if __name__ == '__main__':
#initialise your test cdp first
#first do the true amplitude recovery
#set nmo parameters
params['smute'] = 0
params['vels'] = toolbox.build_vels([0.5], [1], ns=params['ns'])
#then apply NMO
#we will apply a pre-stack agc
toolbox.agc(workspace, None, None)
#stack it
section = stack(workspace, None, **params)
#display it
#params['clip'] = 1e-5
toolbox.display(section, None, **params)
pylab.show()
import toolbox
import numpy as np
import pylab
#--------------------------------------------------
# useful functions
#-------------------------------------------------
None
if __name__ == "__main__":
#initialise dataset
print "initialising dataset"
workspace, params = toolbox.initialise('foybrook.su')
#find our test CDP
#~ print np.unique(workspace['cdp'])
#extract it
cdp = workspace[workspace['cdp'] == 396]
#display it
#~ toolbox.display(cdp, None, **params)
params['gamma'] = 6
toolbox.tar(cdp, None, **params)
#display it
toolbox.display(cdp, None, **params)
pylab.show()
