wassname/simpeg
1
0
Fork 0
mirror of https://github.com/wassname/simpeg.git synced 2026-07-18 12:40:30 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update grav code and example

Browse Source
This commit is contained in:
D Fournier
2016-04-18 12:53:11 -07:00
parent c61da61061
commit 3bb71f0fd4
7 changed files with 66982 additions and 66963 deletions
Download Patch File Download Diff File Expand all files Collapse all files
simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver.py
+70 -51
View File
@@ -2,42 +2,41 @@
from SimPEG import *
import simpegPF as PF
import pylab as plt
import os
#home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev'
#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Nut_Cracker\\Induced_MAG3C'
#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Block_Gaussian_topo\\GRAV'
#home_dir ='C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\CraigModel'
home_dir = '.\\'
inpfile = 'PYGRAV3D_inv.inp'
dsep = '\\'
os.chdir(home_dir)
## New scripts to be added to basecode
#from fwr_MAG_data import fwr_MAG_data
#from read_MAGfwr_inp import read_MAGfwr_inp
plt.close('all')
#%% User input
# Initial beta
beta_in = 1e+2
# Treshold values for compact norm
eps_p = 1e-2 # Small model values
eps_q = 1e-2 # Small model gradient
# Plotting parameter
vmin = -0.1
vmax = 0.2
#%%
# Read input file
[mshfile, obsfile, topofile, mstart, mref, wgtfile, chi, alphas, bounds, lpnorms] = PF.Gravity.read_GRAVinv_inp(home_dir + dsep + inpfile)
# Load mesh file
mesh = Mesh.TensorMesh.readUBC(mshfile)
#mesh = Utils.meshutils.readUBCTensorMesh(mshfile)
# Load in observation file
survey = PF.Gravity.readUBCgravObs(obsfile)
# Get obs location and data
rxLoc = survey.srcField.rxList[0].locs
d = survey.dobs
wd = survey.std
ndata = survey.srcField.rxList[0].locs.shape[0]
beta_in = 1e+1
# Load in topofile or create flat surface
if topofile == 'null':
@@ -53,7 +52,7 @@ else:
nC = len(actv)
# Create active map to go from reduce set to full
actvMap = Maps.ActiveCells(mesh, actv, -100)
actvMap = Maps.InjectActiveCells(mesh, actv, -100)
# Creat reduced identity map
idenMap = Maps.IdentityMap(nP = nC)
@@ -73,7 +72,7 @@ else:
mref = Mesh.TensorMesh.readModelUBC(mesh,mref)
mref = mref[actv]
# Get index of the center
# Get index of the center for plotting
midx = int(mesh.nCx/2)
midy = int(mesh.nCy/2)
@@ -85,46 +84,47 @@ PF.Gravity.plot_obs_2D(survey,'Observed Data')
prob = PF.Gravity.GravityIntegral(mesh, mapping = idenMap, actInd = actv)
prob.solverOpts['accuracyTol'] = 1e-4
#survey = Survey.LinearSurvey()
survey.pair(prob)
#survey.makeSyntheticData(data, std=0.01)
#survey.dobs=d
#survey.mtrue = model
# Write out the predicted
# Write out the predicted file and generate the forward operator
pred = prob.fields(mstart)
PF.Gravity.writeUBCobs(home_dir + dsep + 'Pred0.dat',survey,pred)
wr = np.sum(prob.G**2.,axis=0)**0.5
# Make depth weighting
wr = np.sum(prob.G**2.,axis=0)**0.5 / mesh.vol[actv]
wr = ( wr/np.max(wr) )
wr_out = actvMap * wr
plt.figure()
ax = plt.subplot()
mesh.plotSlice(actvMap*mstart, ax = ax, normal = 'Y', ind=midx+1 ,clim = (-1e-1, mstart.max()))
plt.title('Distance weighting')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
#%% Plot depth weighting
#plt.figure()
#ax = plt.subplot()
#mesh.plotSlice(actvMap*mstart, ax = ax, normal = 'Y', ind=midx+1 ,clim = (-1e-1, mstart.max()))
#plt.title('Distance weighting')
#plt.xlabel('x');plt.ylabel('z')
#plt.gca().set_aspect('equal', adjustable='box')
#%% Create inversion objects
# First start with an l2 regularization
reg = Regularization.Simple(mesh, indActive = actv, mapping = idenMap)
reg.mref = mref
reg.wght = wr
#reg.alpha_s = 1.
# Create pre-conditioner
diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*wr
PC = Utils.sdiag(diagA**-1.)
# Data misfit function
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.Wd = 1/wd
opt = Optimization.ProjectedGNCG(maxIter=10,lower=0.,upper=1., maxIterCG= 20, tolCG = 1e-3)
dmis.Wd = 1./wd
opt = Optimization.ProjectedGNCG(maxIter=20,lower=bounds[0],upper=bounds[1], maxIterCG= 20, tolCG = 1e-3)
opt.approxHinv = PC
# opt = Optimization.InexactGaussNewton(maxIter=6)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in)
beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)
#betaest = Directives.BetaEstimate_ByEig()
target = Directives.TargetMisfit()
inv = Inversion.BaseInversion(invProb, directiveList=[beta,target])
@@ -147,32 +147,40 @@ pred = prob.fields(mrec)
print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) )
#%% Plot out a section of the model
#%% Plot out sections of the smooth model
yslice = midx+1
plt.figure()
ax = plt.subplot(221)
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (-1e-3, mrec.max()))
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (vmin,vmax))
plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
ax = plt.subplot(222)
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (-1e-3, mrec.max()))
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (vmin,vmax))
plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
ax = plt.subplot(212)
mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (-1e-3, mrec.max()))
mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax))
plt.title('Cross Section')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
plt.figure()
ax = plt.subplot(121)
plt.hist(mrec,100)
plt.yscale('log', nonposy='clip')
plt.title('Histogram of model values - Smooth')
ax = plt.subplot(122)
plt.hist(reg.Wsmooth*mrec,100)
plt.yscale('log', nonposy='clip')
plt.title('Histogram of model gradient values - Smooth')
#%% Run one more round for sparsity
phim = invProb.phi_m_last
phid = invProb.phi_d
@@ -181,11 +189,12 @@ reg = Regularization.Sparse(mesh, indActive = actv, mapping = idenMap)
reg.recModel = mrec
reg.mref = mref
reg.wght = wr
reg.eps = 1e-2
reg.eps_p = eps_p
reg.eps_q = eps_q
reg.p = lpnorms[0]
reg.qx = lpnorms[1]
reg.qz = lpnorms[2]
reg.qy = lpnorms[3]
reg.qy = lpnorms[2]
reg.qz = lpnorms[3]
diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*(wr)
PC = Utils.sdiag(diagA**-1.)
@@ -193,8 +202,8 @@ PC = Utils.sdiag(diagA**-1.)
#reg.alpha_s = 1.
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.Wd = 1/wd
opt = Optimization.ProjectedGNCG(maxIter=10 ,lower=0.,upper=1., maxIterCG= 10, tolCG = 1e-4)
dmis.Wd = 1./wd
opt = Optimization.ProjectedGNCG(maxIter=10 ,lower=bounds[0],upper=bounds[1], maxIterCG= 25, tolCG = 1e-4)
opt.approxHinv = PC
#opt.phim_last = reg.eval(mrec)
@@ -203,7 +212,7 @@ invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = invProb.beta)
beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)
#betaest = Directives.BetaEstimate_ByEig()
target = Directives.TargetMisfit()
IRLS =Directives.update_IRLS( phi_m_last = phim, phi_d_last = phid )
IRLS =Directives.Update_IRLS( phi_m_last = phim, phi_d_last = phid )
inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS])
@@ -219,29 +228,39 @@ Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l0l2.sus',m_out)
pred = prob.fields(mrec)
#%% Plot obs data
PF.Magnetics.plot_obs_2D(rxLoc,pred,wd,'Predicted Data')
PF.Magnetics.plot_obs_2D(rxLoc,d,wd,'Observed Data')
PF.Magnetics.plot_obs_2D(rxLoc,pred,'Predicted Data', vmin = np.min(d), vmax = np.max(d))
PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data')
print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) )
#%% Plot out a section of the model
yslice = midx
plt.figure()
ax = plt.subplot(221)
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (-1e-2, mrec.max()))
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (vmin,vmax))
plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
ax = plt.subplot(222)
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (-1e-2, mrec.max()))
mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (vmin,vmax))
plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
ax = plt.subplot(212)
mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (-1e-2, mrec.max()))
mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax))
plt.title('Cross Section')
plt.xlabel('x');plt.ylabel('z')
plt.gca().set_aspect('equal', adjustable='box')
plt.gca().set_aspect('equal', adjustable='box')
plt.figure()
ax = plt.subplot(121)
plt.hist(mrec,100)
plt.yscale('log', nonposy='clip')
plt.title('Histogram of model values - Sparse lp:'+str(lpnorms[0]))
ax = plt.subplot(122)
plt.hist(reg.Wsmooth*mrec,100)
plt.yscale('log', nonposy='clip')
plt.title('Histogram of model gradient values - Sparse lqx: ' + str(lpnorms[1]) + ' lqy:'+ str(lpnorms[2]) + ' lqz:' + str(lpnorms[3]))