Simpeg

Simulation and inversion of time domain IP

- from pr: 590
- commits from: sgkang, fourndo, micmitch
- review from: lheagu

Implementation:

1. Use stretched exponential (chargeability, time constant, frequency dependency) for parameterization

2. Tested 2D and 3D SIP problems

3. Deprecate `Multiregularization` and use combo objective function, so pulled `ref/objectivefunctions` branch - This is for inverting multiple parameters. Thanks lheagy and fourndo for combo objective functions, and rowanc1 for wire implementation!

4. Implement `storeJ` option for both IP and SIP problems (2D and 3D), which boost up speed for relatively small problems.

Example:

True model

Recovered model

Data fit at the fist time channel:

- from pr 690 (supersedes 689)
- commits from lheagy
- review from dccowan and fourndo

Summary of changes
- ensure `indActive` set prior to cell weights in the regularization init so the validator looks for cell weights that are the same size as the indactive.
python
previously, most usage was the following
reg = Regularization.Simple(mesh=mesh, indActive=indActive)
reg.cell_weights = cell_weights

this should be equivalent but prior to the pr, this use-case failed
reg = Regularization.Simple(mesh=mesh, indActive=indActive, cell_weights=cell_weights)

- fixes the bug dccowan was encountering in the example he generated in 684

- from pr 687
- commits from: micmitch, lheagy
- review from: lheagy

Summary
Fixed the way E fields were being computed in the fields object for the DC_CC problem. Added tests the check E and J for a electric dipole in a fullspace against the analytic solutions. Still lacking a test for charges.

- from PR: 685
- commits from lheagy
- review from thast

Bug fix for the way that we set the `_nC_residual` in the Regularization; this is important for constructing a default `W` matrix used inside the norm. We also clean up the logic of how we set nC_residual and adds a test to ensure it doesn't break in the future.

Core code for VRM problem

Here, we add the core functionality for using potential fields to model viscous remanent magnetic responses. The approach has been adapted from the thesis "Forward modeling and inversion of viscous remanent magnetization responses in the time domain" (Cowan, 2016).

Details

* from pr 645
* commits from dccowan
* review from lheagy

Highlights

* Model VRM responses on 3D tensor and 3D tree meshes
* Model VRM responses for a large variety of current waveforms
* Model VRM responses for a multitude of transmitter types
* Invert the response to recover a representative physical property for VRM

Future work

* Provide adequate documentation
* Provide working examples with figures
* Provide workflow for removing VRM response from TEM data

Example


- from pr 682
- commits from lheagy, review from sgkang

New Map: ParametricBlock
- add a parametric block map (2D and 3D) (eg. as shown in https://github.com/simpeg-research/heagy_2018_AEM/blob/master/Heagy_etal_2018_aem_workshop.pdf)


Renamed maps
- rename Parametrized maps to Parametric maps
- update examples to be consistent with this