mirror of
https://github.com/wassname/simpeg.git
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D Fournier
200 lines
6.2 KiB
Python
200 lines
6.2 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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home_dir = '.'
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#inpfile = 'PYGRAV3D_inv.inp'
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dsep = '\\'
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dsep = os.path.sep
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plt.close('all')
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#%% User input
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# Plotting parameter
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vmin = -0.3
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vmax = 0.3
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#%%
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# Read input file
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#[mshfile, obsfile, topofile, mstart, mref, wgtfile, chi, alphas, bounds, lpnorms] = PF.Gravity.read_GRAVinv_inp(home_dir + dsep + inpfile)
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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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actv = driver.activeCells
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nC = len(actv)
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# Create active map to go from reduce set to full
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actvMap = Maps.InjectActiveCells(mesh, actv, -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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# 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 = actv)
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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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PF.Gravity.writeUBCobs(home_dir + dsep + 'Pred0.dat',survey,pred)
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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, actv, 3., np.min(mesh.hx)/4.)
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wr = wr**2.
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# Make depth weighting
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#wr = np.sum(prob.G**2.,axis=0)**0.5 / mesh.vol[actv]
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#wr = ( wr/np.max(wr) )
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#wr_out = actvMap * wr
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else:
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wr = Mesh.TensorMesh.readModelUBC(mesh, home_dir + dsep + wgtfile)
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wr = wr[actv]
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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()
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#mesh.plotSlice(actvMap*wr, ax = ax, normal = 'Y', ind=midx+1 ,clim = (0, wr.max()))
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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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#%% Create inversion objects
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reg = Regularization.Sparse(mesh, indActive=actv, mapping=staticCells)
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reg.mref = driver.mref[dynamic]
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reg.cell_weights = wr*mesh.vol[actv]
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opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=driver.bounds[0],upper=driver.bounds[1], 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-4, minGNiter=3)
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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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# Plot predicted
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pred = prob.fields(mrec)
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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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survey.dobs = pred
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PF.Gravity.plot_obs_2D(survey,'Observed Data')
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print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) )
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#%% Plot out a section of the model
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yslice = midx
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m_out = actvMap*staticCells*reg.l2model
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# Write result
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Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l2l2.den',m_out)
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# Nan aircells for plotting
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m_out[m_out==-100] = np.nan
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plt.figure()
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ax = plt.subplot(221)
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mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-10, clim = (mrec.min(), mrec.max()))
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
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plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m')
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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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ax = plt.subplot(222)
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mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-15, clim = (mrec.min(), mrec.max()))
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
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plt.title('Z: ' + str(mesh.vectorCCz[-15]) + ' m')
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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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ax = plt.subplot(212)
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mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (mrec.min(), mrec.max()))
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plt.title('Cross Section')
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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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plt.figure()
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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.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.title('Histogram of model gradient values - Smooth')
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#%% Plot out a section of the model
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yslice = midx
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m_out = actvMap*staticCells*mrec
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# Write result
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Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_lplq.den',m_out)
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m_out[m_out==-100] = np.nan
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plt.figure()
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ax = plt.subplot(221)
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mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-10, clim = (mrec.min(), mrec.max()))
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
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plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m')
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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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ax = plt.subplot(222)
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mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-15, clim = (mrec.min(), mrec.max()))
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plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--')
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plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m')
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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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ax = plt.subplot(212)
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mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (mrec.min(), mrec.max()))
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plt.title('Cross Section')
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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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plt.figure()
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ax = plt.subplot(121)
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plt.hist(mrec,100)
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plt.yscale('log', nonposy='clip')
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plt.title('Histogram of model values - Compact')
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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.yscale('log', nonposy='clip')
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plt.title('Histogram of model gradient values - Compact')
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plt.show() |