question: How can we get the constraint matrix 'C' in inversion and which the inverse subproblem is used? about gimli HOT 5 CLOSED

1. The matrix can be accessed (through the forward operator), printed (in your case a SparseMapMatrix) and shown by

C=mgr.fop.constraintMatrix()
print(C)
pg.show(C, markersize=1)

2. By default, the reference model does not exist (or is zero) but can be set by using invert(startModel=model, isReference=True)
3. By default, GCLS is used but can be switched to LSQR by using the LSQRInversion framework. The solver yields the same model update but opens some new possibilities, e.g. to add parameter constraints, and will be the default in the future.

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I tried to obtain constraint matrix but its giving me following error
AttributeError: 'TravelTimeDijkstraModelling' object has no attribute 'constraintMatrix'

Similarly for LSQRInversion
I did

from pygimli.frameworks.lsqrinversion import LSQRInversion
pg.info("Traveltime Inversion")
velInv = mgr.LSQRInversion(data, mesh=mesh, lam=20, useGradient=1, zWeight=15.0, secNodes=5, absoluteError=error,
                    blockyModel = True, robustData = True,
                    lambdaFactor=1,
                    cType=1,
                    startModel=1/2200, isReference=True,
                    #verbose=True
                   )
mgr.inv.echoStatus()
print(mgr.inv.chi2History )
plt.plot(mgr.inv.chi2History)

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Sorry, I made a mistake, it is fop.constraints() but constraintMatrix is the better name, so we will create an alias for it.

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Thanks,
Does x and y-axis of constraint matrix shows the velocity values?

Other than that when I run inversion it prompt following error because there is no error column in data. Can I specify error in percent while doing inversion. As default it is 3%, I did define absolute error absoluteError=error

ERROR - <class 'pygimli.physics.traveltime.TravelTimeManager.TravelTimeManager'>.checkError(C:\Users\376189\Documents\pyGimli\pygimli\gimli\pygimli\physics\traveltime\TravelTimeManager.py:97)

Is this the correct way of doing that? Can we specify both error and absolute error?
data['err'] = mgr.estimateError(data['t'], errLevel=0.03, absError=1e-3)

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Please read some notes about inverse theory to learn about the constraint matrix. The model vector contains the (log-transformed) velocity vector and the constraint matrix is a smoothness operator.

Yes, the line you specified for the error estimation is correct. Instead, you can pass either relativeError or absoluteError to the inversion.

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