# Linear Gravity Inversion¶

This example performs a linear inversion of synthetic gravity data. Topography is included and compact norms are used in the regularization

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from SimPEG import Mesh, Problem, Regularization, InvProblem, Optimization, Inversion, Directives, Utils, Maps,mkvc, DataMisfit
import SimPEG.PF as PF
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


## Mesh¶

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ndv = -100

# Create a mesh
dx = 5.

hxind = [(dx, 5, -1.3), (dx, 5), (dx, 5, 1.3)]
hyind = [(dx, 5, -1.3), (dx, 5), (dx, 5, 1.3)]
hzind = [(dx, 5, -1.3), (dx, 6)]

mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')

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mesh.plotGrid()

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# Get index of the center
midx = int(mesh.nCx/2)
midy = int(mesh.nCy/2)

# Lets create a simple Gaussian topo and set the active cells
[xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy)
zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-1]

# plot the topography
plt.colorbar(plt.pcolor(xx, yy, zz))
plt.title('topography (m)')

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# Go from topo to actv cells
topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)]
actv = Utils.surface2ind_topo(mesh, topo, 'N')
actv = np.asarray(
[inds for inds, elem in enumerate(actv, 1) if elem],
dtype=int
) - 1

# Create active map to go from reduce space to full
actvMap = Maps.ActiveCells(mesh, actv, -100)
nC = len(actv)


## Survey¶

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# Create and array of observation points
xr = np.linspace(-20., 20., 20)
yr = np.linspace(-20., 20., 20)
X, Y = np.meshgrid(xr, yr)

# Move the observation points 5m above the topo
Z = -np.exp((X**2 + Y**2) / 75**2) + mesh.vectorNz[-1] + 5.

# Create a MAGsurvey
locXYZ = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]
rxLoc = PF.BaseGrav.RxObs(locXYZ)
srcField = PF.BaseGrav.SrcField([rxLoc])
survey = PF.BaseGrav.LinearSurvey(srcField)

# plot the topography
plt.colorbar(plt.pcolor(xx, yy, zz), label='topography (m)')
plt.title('rx locations')
plt.plot(locXYZ[:,0], locXYZ[:,1], 'k.')


## Model and Mappings¶

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# We can now create a density model and generate data
# Here a simple block in half-space
model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz))
model[(midx-2):(midx+2), (midy-2):(midy+2), -6:-2] = 0.5
model = mkvc(model)
model = model[actv]

# Create active map to go from reduce set to full
actvMap = Maps.InjectActiveCells(mesh, actv, ndv)

# Creat reduced identity map
idenMap = Maps.IdentityMap(nP=nC)

plt.colorbar(mesh.plotSlice(actvMap * model, normal='Z')[0])
plt.title('depth slice')


## Forward Problem¶

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# Create the forward model operator
prob = PF.Gravity.GravityIntegral(mesh, rhoMap=idenMap,
actInd=actv)

# Pair the survey and problem
survey.pair(prob)

# Compute linear forward operator and compute some data
d = prob.fields(model)

# Add noise and uncertainties (1nT)
data = d + np.random.randn(len(d))*0.005
wd = np.ones(len(data))*.005

survey.dobs = data
survey.std = wd

PF.Gravity.plot_obs_2D(survey.srcField.rxList[0].locs,d=data)


## Inverse problem¶

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# Create sensitivity weights from our linear forward operator
wr = np.sum(prob.G**2.,axis=0)**0.5
wr = ( wr/np.max(wr) )

# Create a regularization
reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap)
reg.cell_weights = wr
reg.norms = [0, 1, 1, 1]
reg.eps_p=1e-2
reg.eps_q=1e-2

# Data misfit function
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.W = 1/wd

# Add directives to the inversion
opt = Optimization.ProjectedGNCG(maxIter=100, lower=-1., upper=1.,
maxIterLS=20, maxIterCG=10,
tolCG=1e-3)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
betaest = Directives.BetaEstimate_ByEig()

# Here is where the norms are applied
IRLS = Directives.Update_IRLS(f_min_change=1e-3,
minGNiter=3)
update_Jacobi = Directives.Update_lin_PreCond()
inv = Inversion.BaseInversion(invProb,
directiveList=[betaest, IRLS,
update_Jacobi])

# Run the inversion
mrec = inv.run(model**0 * 1e-3)

residual = np.linalg.norm(mrec-model) / np.linalg.norm(model)

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from ipywidgets.widgets import interact, IntSlider

# Here is the recovered susceptibility model
ypanel = midx
zpanel = -4
m_l2 = actvMap * IRLS.l2model
m_l2[m_l2==-100] = np.nan

m_lp = actvMap * mrec
m_lp[m_lp==-100] = np.nan

m_true = actvMap * model
m_true[m_true==-100] = np.nan

plt.figure()

#Plot L2 model
ax = plt.subplot(231)
mesh.plotSlice(m_l2, ax = ax, normal = 'Z', ind=zpanel, grid=True, clim = (0., model.max()),pcolorOpts={'cmap':'viridis'})
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCy[ypanel],mesh.vectorCCy[ypanel]]),color='w')
plt.title('Plan l2-model.')
plt.gca().set_aspect('equal')
plt.ylabel('y')
ax.xaxis.set_visible(False)

# Vertica section
ax = plt.subplot(234)
mesh.plotSlice(m_l2, ax = ax, normal = 'Y', ind=midx, grid=True, clim = (0., model.max()),pcolorOpts={'cmap':'viridis'})
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCz[zpanel],mesh.vectorCCz[zpanel]]),color='w')
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([Z.min(),Z.max()]),color='k')
plt.title('E-W l2-model.')
plt.gca().set_aspect('equal')
plt.xlabel('x')
plt.ylabel('z')

#Plot Lp model
ax = plt.subplot(232)
mesh.plotSlice(m_lp, ax = ax, normal = 'Z', ind=zpanel, grid=True, clim = (0., model.max()),pcolorOpts={'cmap':'viridis'})
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCy[ypanel],mesh.vectorCCy[ypanel]]),color='w')
plt.title('Plan lp-model.')
plt.gca().set_aspect('equal')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)

# Vertical section
ax = plt.subplot(235)
mesh.plotSlice(m_lp, ax = ax, normal = 'Y', ind=midx, grid=True, clim = (0., model.max()),pcolorOpts={'cmap':'viridis'})
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCz[zpanel],mesh.vectorCCz[zpanel]]),color='w')
plt.title('E-W lp-model.')
plt.gca().set_aspect('equal')
ax.yaxis.set_visible(False)
plt.xlabel('x')

#Plot True model
ax = plt.subplot(233)
mesh.plotSlice(m_true, ax = ax, normal = 'Z', ind=zpanel, grid=True, clim = (0., model.max()),pcolorOpts={'cmap':'viridis'})
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCy[ypanel],mesh.vectorCCy[ypanel]]),color='w')
plt.title('Plan true model.')
plt.gca().set_aspect('equal')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)

# Vertical section
ax = plt.subplot(236)
mesh.plotSlice(m_true, ax = ax, normal = 'Y', ind=midx, grid=True, clim = (0., model.max()),pcolorOpts={'cmap':'viridis'})
plt.plot(([mesh.vectorCCx[0],mesh.vectorCCx[-1]]),([mesh.vectorCCz[zpanel],mesh.vectorCCz[zpanel]]),color='w')
plt.title('E-W true model.')
plt.gca().set_aspect('equal')
plt.xlabel('x')
ax.yaxis.set_visible(False)

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