Comments (5)
Your startmodel is somehow automatically determined with 2.28, which is outside of your given saturation limit [0, 1]. Can you try with a valid starting model invert(limits=[0, 1], startModel=0.5, ...)
?
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The range of the Saturation model is [0,1]. No matter how I change the startModel, I cannot obtain the desired result. If I remove the model range, I can obtain the result. However, the range of the Saturation model is [0.81, 5.35], which is the wrong result?
Actual behavior
satERT = ERTPetro.invert(data, lam=20, limits=[0, 1], startModel=0.5, verbose=True)
fop: <pygimli.frameworks.modelling.PetroModelling object at 0x000002DC84675EA0>
Data transformation: <pgcore._pygimli_.RTrans object at 0x000002DC8465FD60>
Model transformation (cumulative):
0 <pgcore._pygimli_.RTransLogLU object at 0x000002DC8464D340>
min/max (data): 11.44/87.51
min/max (error): 2%/2.58%
min/max (start model): 0.5/0.5
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
inv.iter 1 ... chi² = 402481.45 (dPhi = 78.89%) lam: 20
--------------------------------------------------------------------------------
inv.iter 2 ... chi² = 132241.06 (dPhi = 67.14%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 3 ... chi² = 99527.0 (dPhi = 24.7%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 4 ... chi² = 99372.5 (dPhi = 0.22%) lam: 20.0
################################################################################
# Abort criteria reached: dPhi = 0.22 (< 2.0%) #
################################################################################
satERT = ERTPetro.invert(data, lam=20, verbose=True)
fop: <pygimli.frameworks.modelling.PetroModelling object at 0x000002DC9A609900>
Data transformation: <pgcore._pygimli_.RTrans object at 0x000002DC9A5D6D00>
Model transformation: <pgcore._pygimli_.RTransLog object at 0x000002DC9A609A90>
min/max (data): 11.44/87.51
min/max (error): 2%/2.58%
min/max (start model): 2.28/2.28
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
inv.iter 1 ... chi² = 94.54 (dPhi = 92.29%) lam: 20
--------------------------------------------------------------------------------
inv.iter 2 ... chi² = 11.3 (dPhi = 87.39%) lam: 20.0
--------------------------------------------------------------------------------
inv.iter 3 ... chi² = 0.6 (dPhi = 87.54%) lam: 20.0
################################################################################
# Abort criterion reached: chi² <= 1 (0.60) #
################################################################################
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You took the lake case (https://www.pygimli.org/_tutorials_auto/3_inversion/plot_8-regionWise.html) which is an underwater case but you don't treat it like that. In the pure resistivity inversion, you obtain subsurface resistivities of about 8 Ohmm, which is 2.5 times below the specified fluid resistivity of 20 Ohmm. If you formally convert 8 Ohmm into saturations you end up with values above 1. So technically all is working well but the data set does not make sense in terms of Archie unless you include the water body. (In this case there is a lot of clay and Archie won't work anyway)
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I understand. Thank you for your patient explanation.
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I guess it can be closed. If there is still a problem, just reopen.
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