def drawPic():
m = 200
n = 200
a = 20
b = 20
r = 1
vp0 = 1500
variance = 0.08
dx = 3
C = np.zeros((m, n))
au = autocorrelation_func(a, b)
for i in xrange(m):
for j in xrange(n):
C[i, j] = au.gaussian((i-(m-1)/2.)*dx, (j-(n-1)/2.)*dx)
S = fft2(C) # power spectral density
z = np.random.randn(m, n)
Z = fft2(z)
G = np.sqrt(dx * S)
GZ = G * Z
gz = ifft2(GZ)
A = np.real(gz)
K = np.real(A)
f = plt.figure()
plt.imshow(K)
return f
def fft_ma(m, n, a, b, r, dx, dy):
C = np.zeros((m, n))
au = autocorrelation_func(a, b)
for i in xrange(m):
for j in xrange(n):
C[i, j] = au.gaussian((i-(m-1)/2.)*dy, (j-(n-1)/2.)*dx)
S = fft2(C) # power spectral density
z = np.random.randn(m, n)
Z = fft2(z)
G = np.sqrt(dx * S)
GZ = G * Z
gz = ifft2(GZ)
A = np.real(gz)
K = np.real(A)
return K