def __init__(self, data, nlay=2, verbose=False):
"""Parameters: FDEM data class and number of layers."""
super(FDEM2dFOP, self).__init__(verbose)
self.nlay = nlay
self.header = {}
self.pos, self.z, self.topo = None, None, None
self.FOP = data.FOP(nlay)
self.nx = len(data.x)
self.nf = len(data.freq())
npar = 2 * nlay - 1
self.mesh1d = pg.createMesh1D(self.nx, npar)
self.mesh_ = pg.createMesh1D(self.nx, 2 * nlay - 1)
self.setMesh(self.mesh_)
# self.J = NDMatrix(self.nx, self.nf*2, npar)
self.J = pg.RBlockMatrix()
self.FOP1d = []
for i in range(self.nx):
self.FOP1d.append(
pg.FDEM1dModelling(nlay, data.freq(), data.coilSpacing,
-data.height))
n = self.J.addMatrix(self.FOP1d[-1].jacobian())
self.J.addMatrixEntry(n, self.nf * 2 * i, npar * i)
self.J.recalcMatrixSize()
print(self.J.rows(), self.J.cols())
def __init__(self, nlay, ab2, mn2, freq, coilspacing, verbose=False):
"""Init number of layers, AB/2, MN/2, frequencies & coil spacing."""
pg.ModellingBase.__init__(self, verbose)
self.nlay_ = nlay
self.fDC_ = pg.DC1dModelling(nlay, ab2, mn2, verbose)
self.fEM_ = pg.FDEM1dModelling(nlay, freq, coilspacing, verbose)
self.mesh_ = pg.createMesh1DBlock(nlay)
self.setMesh(self.mesh_)
def __init__(self, frequencies, coilspacing, nlay=2, verbose=False):
"""Set up class by frequencies and geometries."""
pg.ModellingBase.__init__(self, verbose)
self.nlay_ = nlay # real layers (actually one more!)
self.FOP_ = pg.FDEM1dModelling(nlay + 1, frequencies, coilspacing, 0.0)
self.mesh_ = pg.createMesh1D(nlay, 2) # thicknesses and resistivities
self.mesh_.cell(0).setMarker(2)
self.setMesh(self.mesh_)
def FOP(self, nlay=2): # createFOP deciding upon block or smooth
"""Forward modelling operator using a block discretization.
Parameters
----------
nlay : int
Number of blocks
"""
return pg.FDEM1dModelling(nlay, self.freq(), self.coilSpacing,
-self.height)
thk = pg.RVector(nlay - 1, 15.0) # 15m thickness each
res = pg.RVector(nlay, 200.0) # 200 Ohmm
res[1] = 10.
res[2] = 50.
model = pg.cat(thk, res) # paste together to one model
###############################################################################
# We first set up EM forward operator and generate synthetic data with noise
coilspacing = 50.
nf = 10
freq = pg.RVector(nf, 110.)
for i in range(nf - 1):
freq[i + 1] = freq[i] * 2.
fEM = pg.FDEM1dModelling(nlay, freq, coilspacing)
dataEM = fEM(model)
for i in range(len(dataEM)):
dataEM[i] += np.random.randn(1)[0] * noiseEM
###############################################################################
# We define model transformations: logarithms and log with upper+lower bounds
transRhoa = pg.RTransLog()
transThk = pg.RTransLog()
transRes = pg.RTransLogLU(1., 1000.)
transEM = pg.RTrans()
fEM.region(0).setTransModel(transThk)
fEM.region(1).setTransModel(transRes)
###############################################################################
