Comments (5)
In contrast to frequency domain, where one could invert for complex resistivity, there is no "joint inversion" in time-domain IP. Instead, first a resistivity inversion is done (calling the normal ERTManager inversion) and then a IP inversion that can also be called by ERTIPManager.invertTDIP()
. You should see things like RMS in the output but I just see that the inversion object is not kept for further analysis. I will change this.
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So it's okay to save both inversion objects by doing inv_ert, inv_ip = mgr2.invert(args)
? I have access to the inversion verbose, but I don't know how to access to certain metrics specifically. Thank you.
Edit: does the inversion work for msec as well? I just changed the label, but I see that the ip domain is "mV/V".
Also, when I do this to invert only IP:
inv_ip = mgr.invertTDIP(secNodes=1, paraMaxCellSize=10.0,
zWeight=0.2, verbose=1)
I get this error:
RuntimeError: RegionManager knows no mesh.
But when I do the regular mgr.invert(secNodes=1, paraMaxCellSize=10.0, zWeight=0.2, verbose=1
, it just does both inversions without defining any mesh.
from gimli.
- No, the inversion object is not returned. We have to change this in the source code (to keep it as a member of the manager).
- Formally it is working and the results will be ok as a first-order approximation but the inversion is based on a instantaneous and not integrated chargeability.
- Before calling
mgr.invertTDIP()
you need to do a resistivity inversion throughmgr.invert()
(which will already do an IP inversion).
Thank you for pointing out the shortcomings in the code.
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Alright, thank you so much. I've got another question, is there a way to remove those data points where data/response relation is very high or very low and therefore lead to a higher error%? Probably doing a scatterplot for data vs response and delete those exceeding certain thereshold. so I can reduce the chi2 directly with the inversion data.
I'm not pretty sure how to actually handle errors post-inversion. Thanks in advance.
from gimli.
Yes, of course. The data vector is in data['rhoa']
and the response vector in mgr.inv.response
. You can compute the rms or error-weighted misfit and look at the histogram or the misfit distribution and then remove points
misfit = (data['rhoa'] - mgr.inv.response) / data['err']
data.remove(np.abs(misfit) > 10)
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