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D Fournier
282 lines
10 KiB
Python
282 lines
10 KiB
Python
#%%
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from SimPEG import *
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import simpegPF as PF
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import pylab as plt
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import os
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import numpy as np
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#home_dir = 'C:\Egnyte\Private\craigm\PHD\LdM\Gravity\Bouguer\SIMPEG\models\\all_models\\density_-1.2_0.3'
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home_dir = '.\\'
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inpfile = 'PYGRAV3D_inv.inp'
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#inpfile = 'PYGRAV3D_inv_checkerboard.inp'
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dsep = '\\'
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os.chdir(home_dir)
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plt.close('all')
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#%% User input
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# Initial beta
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beta_in = 1e-2
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# Plotting parameter
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vmin = -0.1
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vmax = 0.5
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#weight exponent for default weighting
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wgtexp = 3. #dont forget the "."
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#%%
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driver = PF.GravityDriver.GravityDriver_Inv(home_dir + dsep + 'PYGRAV3D_inv.inp')
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mesh = driver.mesh
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survey = driver.survey
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rxLoc = survey.srcField.rxList[0].locs
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d = survey.dobs
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wd = survey.std
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ndata = survey.srcField.rxList[0].locs.shape[0]
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active = driver.activeCells
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nC = len(active)
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# Create active map to go from reduce set to full
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activeMap = Maps.InjectActiveCells(mesh, active, -100)
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# Create static map
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static = driver.staticCells
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dynamic = driver.dynamicCells
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staticCells = Maps.InjectActiveCells(None, dynamic, driver.m0[static], nC=nC)
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mstart = driver.m0[dynamic]
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# Create reduced identity map
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#idenMap = Maps.IdentityMap(nP=nC)
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# Get index of the center
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midx = int(mesh.nCx/2)
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midy = int(mesh.nCy/2)
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#%% Plot obs data
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#PF.Gravity.plot_obs_2D(survey,'Observed Data')
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#%% Run inversion
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prob = PF.Gravity.GravityIntegral(mesh, mapping = staticCells, actInd = active)
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prob.solverOpts['accuracyTol'] = 1e-4
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survey.pair(prob)
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# Write out the predicted file and generate the forward operator
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pred = prob.fields(mstart)
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# Load weighting file
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if driver.wgtfile == 'DEFAULT':
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wr = PF.Magnetics.get_dist_wgt(mesh, rxLoc, active, wgtexp, np.min(mesh.hx)/4.)
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wr = wr**2.
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else:
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wr = Mesh.TensorMesh.readModelUBC(mesh, home_dir + dsep + wgtfile)
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wr = wr[active]
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wr = wr**2.
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#%% Plot depth weighting
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plt.figure()
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ax = plt.subplot(211)
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datwgt=mesh.plotSlice(activeMap*wr, ax = ax, normal = 'Y', ind=midx+1 ,clim = (-1e-1, wr.max()), pcolorOpts={'cmap':'jet'})
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plt.title('Distance weighting')
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plt.xlabel('x');plt.ylabel('z')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(datwgt[0],orientation="vertical")
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cb.set_label('Weighting')
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ax=plt.subplot(212)
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plt.hist(wr,bins=100)
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plt.yscale('log', nonposy='clip')
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plt.title('Distribution of weights')
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plt.tight_layout()
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plt.savefig(home_dir + dsep + 'Weighting_' +str(wgtexp) +'.png', dpi=300)
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#%% Create inversion objects
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reg = Regularization.Sparse(mesh, indActive = active, mapping = staticCells)
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reg.mref = driver.mref[dynamic]
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reg.cell_weights = wr*mesh.vol[active]
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eps_p = driver.eps[0]
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eps_q = driver.eps[1]
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opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
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dmis = DataMisfit.l2_DataMisfit(survey)
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dmis.Wd = 1./wd
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invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
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#beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)
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#update_beta = Directives.Scale_Beta(tol = 0.05, coolingRate=5)
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betaest = Directives.BetaEstimate_ByEig()
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IRLS = Directives.Update_IRLS( norms=driver.lpnorms, eps=driver.eps, f_min_change = 1e-2)
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update_Jacobi = Directives.Update_lin_PreCond()
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inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
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# Run inversion
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mrec = inv.run(mstart)
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m_out = activeMap*staticCells*reg.l2model
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# Write result
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Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l2l2_' +str(wgtexp) + '.den', m_out)
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#Utils.meshutils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr_out)
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pred_compact = prob.fields(mrec)
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PF.Gravity.writeUBCobs(home_dir + dsep + 'Pred_compact' +str(wgtexp) + '_' + str(eps_p) + '_' + str(eps_q) +'.dat',survey,pred_compact)
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pred = prob.fields(mrec)
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# Plot predicted
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#pred_smooth = prob.fields(mrec)
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#PF.Gravity.writeUBCobs(home_dir + dsep + 'Pred_smooth' +str(wgtexp) + '_' + str(eps_p) + '_' + str(eps_q) +'.dat',survey,pred_smooth)
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#PF.Magnetics.plot_obs_2D(rxLoc,pred,wd,'Predicted Data')
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#PF.Magnetics.plot_obs_2D(rxLoc,(d-pred),wd,'Residual Data')
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print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) )
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print "Misfit sum(obs-calc)/nobs: %.3f mGal" %np.divide(np.sum(np.abs(d-pred)), len(d))
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print "RMS misfit: %.3f mGal" %np.sqrt(np.divide(np.sum((d-pred)**2),len(d)))
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#%% Plot out sections of the smooth model
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yslice = midx+1
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m_out[m_out==-100]=np.nan # set "air" to nan
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print "\nMax density:" + str(np.nanmax(m_out))
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print "\nMin density:" + str(np.nanmin(m_out))
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plt.figure(figsize=(15,10))
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plt.suptitle('Smooth Inversion: Depth weight = ' + str(wgtexp))
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ax = plt.subplot(221)
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dat1=mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-10, clim = (vmin,vmax), pcolorOpts={'cmap':'bwr'})
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='gray',linestyle = '--')
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plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='k',s=1)
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plt.title('Z: ' + str(mesh.vectorCCz[-10]) + ' m')
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plt.xlabel('Easting (m)');plt.ylabel('Northing (m)')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4))
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cb.set_label('Density (g/cc$^3$)')
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ax = plt.subplot(222)
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dat = mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-13, clim = (vmin,vmax), pcolorOpts={'cmap':'bwr'})
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='gray',linestyle = '--')
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plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='k',s=1)
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plt.title('Z: ' + str(mesh.vectorCCz[-13]) + ' m')
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plt.xlabel('Easting (m)');plt.ylabel('Northing (m)')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4))
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cb.set_label('Density (g/cc$^3$)')
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ax = plt.subplot(212)
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mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax), pcolorOpts={'cmap':'bwr'})
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plt.title('Cross Section')
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plt.xlabel('Easting(m)');plt.ylabel('Elevation')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4), cmap='bwr')
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cb.set_label('Density (g/cc$^3$)')
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plt.savefig(home_dir + str('\Figure1_' +str(wgtexp) + '.png'), dpi=300, bb_inches='tight')
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#plot histograms
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plt.figure(figsize=(15,10))
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ax = plt.subplot(121)
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plt.hist(reg.l2model,100)
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plt.yscale('log', nonposy='clip')
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plt.xlim(reg.l2model.mean() - 6.*(reg.l2model.std()), reg.l2model.mean() + 6.*(reg.l2model.std()))
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plt.xlabel('Density (g/cc$^3$)')
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plt.title('Histogram of model values - Smooth')
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ax = plt.subplot(122)
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plt.hist(reg.regmesh.cellDiffxStencil*(staticCells*reg.l2model),100)
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plt.yscale('log', nonposy='clip')
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plt.xlim(reg.l2model.mean() - 2.*(reg.l2model.std()), reg.l2model.mean() + 2.*(reg.l2model.std()))
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plt.xlabel('Density (g/cc$^3$)')
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plt.title('Histogram of model gradient values - Smooth')
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plt.savefig(home_dir + str('\Figure2_' +str(wgtexp) + '.png'), dpi=300, bb_inches='tight')
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#%% Plot obs data
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PF.Magnetics.plot_obs_2D(rxLoc,pred_compact,'Predicted Data', vmin = np.min(d), vmax = np.max(d))
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plt.savefig(home_dir + str('\Figure3_' +str(wgtexp) + '_' + str(eps_p) + '_' + str(eps_q) +'.png'), dpi=300, bb_inches='tight')
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PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data')
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plt.savefig(home_dir + str('\Figure4.png'), dpi=300, bb_inches='tight')
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print "\nFinal misfit:" + str(np.sum( ((d-pred_compact)/wd)**2. ) )
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print "Misfit sum(obs-calc)/nobs: %.3f mGal" %np.divide(np.sum(np.abs(d-pred_compact)), len(d))
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print "RMS misfit: %.3f mGal" %np.sqrt(np.divide(np.sum((d-pred_compact)**2),len(d)))
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#%% Plot out a section of the compact model
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m_out = activeMap*staticCells*mrec
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Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l0l2_' +str(wgtexp) + '_' + str(eps_p) + '_' + str(eps_q) +'.den',m_out)
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yslice = midx+1
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m_out[m_out==-100]=np.nan # set "air" to nan
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print "\nMax density:" + str(np.nanmax(m_out))
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print "\nMin density:" + str(np.nanmin(m_out))
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plt.figure(figsize=(15,10))
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plt.suptitle('Compact Inversion: Depth weight = ' + str(wgtexp) + ': $\epsilon_p$ = ' + str(eps_p) + ': $\epsilon_q$ = ' + str(eps_q))
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ax = plt.subplot(221)
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dat = mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-10, clim = (vmin,vmax), pcolorOpts={'cmap':'bwr'})
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='gray',linestyle = '--')
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plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='k',s=1)
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plt.title('Z: ' + str(mesh.vectorCCz[-10]) + ' m')
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plt.xlabel('Easting (m)');plt.ylabel('Northing (m)')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(dat[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4))
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cb.set_label('Density (g/cc$^3$)')
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ax = plt.subplot(222)
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dat = mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-13, clim = (vmin,vmax), pcolorOpts={'cmap':'bwr'})
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='gray',linestyle = '--')
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plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='k',s=1)
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plt.title('Z: ' + str(mesh.vectorCCz[-13]) + ' m')
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plt.xlabel('Easting (m)');plt.ylabel('Northing (m)')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(dat[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4))
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cb.set_label('Density (g/cc$^3$)')
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ax = plt.subplot(212)
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dat = mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax), pcolorOpts={'cmap':'bwr'})
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plt.title('Cross Section')
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plt.xlabel('Easting (m)');plt.ylabel('Elevation (m)')
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plt.gca().set_aspect('equal', adjustable='box')
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cb = plt.colorbar(dat[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4))
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cb.set_label('Density (g/cc$^3$)')
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plt.savefig(home_dir + str('\Figure5_' +str(wgtexp) + '_' + str(eps_p) + '_' + str(eps_q) +'.png'), dpi=300, bb_inches='tight')
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#plot histograms
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plt.figure(figsize=(15,10))
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ax = plt.subplot(121)
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plt.hist(mrec,100)
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#plt.xlim(mrec.mean() - 6.*(mrec.std()), mrec.mean() + 6.*(mrec.std()))
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plt.yscale('log', nonposy='clip')
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plt.xlabel('Density (g/cc$^3$)')
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plt.title('Histogram of model values - Sparse lp:'+str(driver.lpnorms[0]))
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ax = plt.subplot(122)
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plt.hist(reg.regmesh.cellDiffxStencil*(staticCells*mrec),100)
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#plt.xlim(mrec.mean() - 4.*(mrec.std()), mrec.mean() + 4.*(mrec.std()))
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plt.xlabel('Density (g/cc$^3$)')
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plt.yscale('log', nonposy='clip')
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plt.title('Histogram of model gradient values - Sparse lqx: ' + str(driver.lpnorms[1]) + ' lqy:'+ str(driver.lpnorms[2]) + ' lqz:' + str(driver.lpnorms[3]))
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plt.savefig(home_dir + str('\Figure6_' +str(wgtexp) + '_' + str(eps_p) + '_' + str(eps_q) +'.png'), dpi=300, bb_inches='tight')
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#make a plot of the obs -calc, ie residual
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plt.figure(figsize=(10,8))
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residual = d - pred_compact
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plt.hist(residual, 100)
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plt.savefig(home_dir + str('\Figure7_residuals.png'), dpi=300, bb_inches='tight')
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