"""
import numpy as np
import matplotlib.pyplot as plt
from fatiando.seismic import conv
from fatiando.vis import mpl
# Define the parameters of our depth model
n_samples, n_traces = [600, 100]
velocity = 1500 * np.ones((n_samples, n_traces))
# We'll put two interfaces in depth
velocity[150:, :] = 2000
velocity[400:, :] = 3500
# We need to convert the depth model we made above into time
vel_l = conv.depth_2_time(velocity, velocity, dt=2e-3, dz=1)
# and we'll assume the density is homogeneous
rho_l = 2200 * np.ones(np.shape(vel_l))
# With that, we can calculate the reflectivity model in time
rc = conv.reflectivity(vel_l, rho_l)
# and finally perform our convolution
synt = conv.convolutional_model(rc, 30, conv.rickerwave, dt=2e-3)
# We can use the utility function in fatiando.vis.mpl to plot the seismogram
fig, axes = plt.subplots(1, 2, figsize=(8, 5))
ax = axes[0]
ax.set_title("Velocity model (in depth)")
tmp = ax.imshow(velocity,
extent=[0, n_traces, n_samples, 0],
cmap="copper",
"""
import numpy as np
import matplotlib.pyplot as plt
from fatiando.seismic import conv
from fatiando.vis import mpl
# Define the parameters of our depth model
n_samples, n_traces = [600, 100]
velocity = 1500*np.ones((n_samples, n_traces))
# We'll put two interfaces in depth
velocity[150:, :] = 2000
velocity[400:, :] = 3500
dt = 2e-3
# We need to convert the depth model we made above into time
vel_l = conv.depth_2_time(velocity, velocity, dt=dt, dz=1)
# and we'll assume the density is homogeneous
rho_l = 2200*np.ones(np.shape(vel_l))
# With that, we can calculate the reflectivity model in time
rc = conv.reflectivity(vel_l, rho_l)
# and finally perform our convolution
synt = conv.convolutional_model(rc, 30, conv.rickerwave, dt=dt)
# We can use the utility function in fatiando.vis.mpl to plot the seismogram
fig, axes = plt.subplots(1, 2, figsize=(8, 5))
ax = axes[0]
ax.set_title("Velocity model (in depth)")
tmp = ax.imshow(velocity, extent=[0, n_traces, n_samples, 0],
cmap="copper", aspect='auto', origin='upper')
fig.colorbar(tmp, ax=ax, pad=0, aspect=50)
"""
import numpy as np
import matplotlib.pyplot as plt
from fatiando.seismic import conv
from fatiando.vis import mpl
# Define the parameters of our depth model
n_samples, n_traces = [600, 100]
velocity = 1500*np.ones((n_samples, n_traces))
# We'll put two interfaces in depth
velocity[150:, :] = 2000
velocity[400:, :] = 3500
# We need to convert the depth model we made above into time
vel_l = conv.depth_2_time(velocity, velocity, dt=2e-3, dz=1)
# and we'll assume the density is homogeneous
rho_l = 2200*np.ones(np.shape(vel_l))
# With that, we can calculate the reflectivity model in time
rc = conv.reflectivity(vel_l, rho_l)
# and finally perform our convolution
synt = conv.convolutional_model(rc, 30, conv.rickerwave, dt=2e-3)
# We can use the utility function in fatiando.vis.mpl to plot the seismogram
fig, axes = plt.subplots(1, 2, figsize=(8, 5))
ax = axes[0]
ax.set_title("Velocity model (in depth)")
tmp = ax.imshow(velocity, extent=[0, n_traces, n_samples, 0],
cmap="copper", aspect='auto', origin='upper')
fig.colorbar(tmp, ax=ax, pad=0, aspect=50)
"""
Seismic: Synthetic convolutional seismogram for a simple two layer velocity
model
"""
import numpy as np
import matplotlib.pyplot as plt
from fatiando.seismic import conv
from fatiando.vis import mpl
# model parameters
n_samples, n_traces = [600, 20]
rock_grid = 1500.*np.ones((n_samples, n_traces))
rock_grid[300:, :] = 2500.
# synthetic calculation
vel_l = conv.depth_2_time(rock_grid, rock_grid, dt=2.e-3, dz=1.)
rho_l = np.ones(np.shape(vel_l))
rc = conv.reflectivity(vel_l, rho_l)
synt = conv.convolutional_model(rc, 30., conv.rickerwave, dt=2.e-3)
# plot input model
plt.figure()
plt.subplot(3, 1, 1)
plt.ylabel('Depth (m)')
plt.title("Depth Vp model", fontsize=13, family='sans-serif', weight='bold')
plt.imshow(rock_grid, extent=[0, n_traces, n_samples, 0],
cmap=mpl.pyplot.cm.bwr, aspect='auto', origin='upper')
# plot resulted seismogram using wiggle
plt.subplot(3, 1, 2)
mpl.seismic_wiggle(synt, dt=2.e-3)
mpl.seismic_image(synt, dt=2.e-3, cmap=mpl.pyplot.cm.jet, aspect='auto')
plt.ylabel('time (seconds)')
plt.title("Convolutional seismogram", fontsize=13, family='sans-serif',
weight='bold')
"""
Seismic: Synthetic convolutional seismogram for a simple two layer velocity
model
"""
import numpy as np
import matplotlib.pyplot as plt
from fatiando.seismic import conv
from fatiando.vis import mpl
# model parameters
n_samples, n_traces = [600, 20]
rock_grid = 1500. * np.ones((n_samples, n_traces))
rock_grid[300:, :] = 2500.
# synthetic calculation
vel_l = conv.depth_2_time(rock_grid, rock_grid, dt=2.e-3, dz=1.)
rho_l = np.ones(np.shape(vel_l))
rc = conv.reflectivity(vel_l, rho_l)
synt = conv.convolutional_model(rc, 30., conv.rickerwave, dt=2.e-3)
# plot input model
plt.figure()
plt.subplot(3, 1, 1)
plt.ylabel('Depth (m)')
plt.title("Depth Vp model", fontsize=13, family='sans-serif', weight='bold')
plt.imshow(rock_grid,
extent=[0, n_traces, n_samples, 0],
cmap=mpl.pyplot.cm.bwr,
aspect='auto',
origin='upper')
# plot resulted seismogram using wiggle
plt.subplot(3, 1, 2)
mpl.seismic_wiggle(synt, dt=2.e-3)
mpl.seismic_image(synt, dt=2.e-3, cmap=mpl.pyplot.cm.jet, aspect='auto')
