JacoPyAn

NOTE: This repository and in particular this description is work in progress, not all scripts have been finished and tested. Use at your own risk! However, we will succesively update the repository correcting bugs, and adding additional functionality.

This repository contains $\texttt{python}$ tools for manipulating, processing, analysing Jacobians from 3-D magnetotelluric inversion, including randomized singular value decompositions (SVD), and other tools related to the Nullspace-Shuttle method [1, 2]. Currently, we have implemented the tools necessary for using Jacobians generated by $\texttt{ModEM}$ [3, 4]. As of today, the routines for manipulating/sparsifying the full Jacobian, the SVD, and the calculation of sensitivity can be used.

Adapting $\texttt{ModEM}$ for Jacobian output

The Jacobian of a data and parameter set is defined as $J_{ij} = \dfrac{\delta d_i}{\delta m_j}$. Before being able to use it for further action, a few steps are necessary. $\texttt{ModEM}$ seeks the MAP solution to the usual Bayesian inverse problem [5] defined by:

$$\Theta = {({\mathbf{g}}({\mathbf{p}}) - {\mathbf{d}})^T}{\mathbf{C}}_{d}^{-1}({\mathbf{g}}({\mathbf{p}}) - {\mathbf{d}}) + {({\mathbf{p}} - {{\mathbf{p}}_a})^T}{\mathbf{C}}_{p}^{-1}({\mathbf{p}} - {{\mathbf{p}}_a}) = \left||| {{\mathbf{C}}_{d}^{-1/2}({\mathbf{g}}({\mathbf{p}}) - {\mathbf{d}})} \right|||_2^2 + \left||| {{\mathbf{C}}_{p}^{-1/2}({\mathbf{p}} - {{\mathbf{p}}_a})} \right|||_2^2$$

Transformig the parameter vector by

$${\mathbf{\tilde{p}}}={\mathbf{C}}_{m}^{-1/2} {({\mathbf{p}}-{\mathbf{p}_a})} ,$$

the data by

$${\mathbf{\tilde{d}}}={\mathbf{C}_{d}^{-1/2}} {\mathbf{d}},$$

leads to the further transformation

$${\mathbf{\tilde{g}}}({\mathbf{\tilde{p}}})={\mathbf{C}_{d}^{-1/2}} {\mathbf{g}} ({\mathbf{C}}_{m}^{1/2} {\mathbf{\tilde{p}}}).$$

From this we havethe simplified objective function

$$\tilde{\Theta} ({\mathbf{\tilde{p},\tilde{d}}}) = {\left||| {{\mathbf{\tilde d - \tilde g(\tilde p)}}} \right|||_2^2} + \lambda {\left|| {{\mathbf{\tilde p}}} \right||_2^2}.$$

The Jacobian used within $\texttt{ModEM}$ is also calculated in the transformed system:

$${\mathbf{\tilde{J}}} = {\mathbf{C}}_{d}^{-1/2} {\mathbf{J}} {\mathbf{C}}_{m}^{1/2}$$

For this reason, some minor changes in the $\texttt{ModEM}$ source code were made. They do not touch the original functionality, as they are controlled by compiler directives. Activating the new code is done by adding $\texttt{-DJAC}$ to the $\texttt{FFLAGS}$ line in the corresponding $\texttt{Makefile}$. The adapted code can be found in the $\texttt{modem}$ subdirectory of the $\texttt{JacoPyAn}$ repository, and can be simply copied to the original $\texttt{f90}$ subdirectory in the original source code.

The changes made in the souce code will only be relevant to the parts used by the calculation and storage of the Jacobian. In addition to the binary file $\texttt{Model.jac}$ containing the physical-space Jacobian (the name is arbitrary), also an ASCII file $\texttt{Model\_jac.dat}$ is created, which contains data information in the correct sequence and units.

Preprocessing the Jacobian

The generated Jacobians for realistic models can be large (several tens of Gb). For this reason the first step in working with the Jacobians is to put them into a format easier to handle by $\texttt{python}$, and, as many of the elements of these matrices are small, reduce their size. This is done in the script $\texttt{MT\_jac\_proc.py}$ .

Sensitivities

The use of sensitivities (in a variety of flavours) is comparatively easy, but needs some clarification, as it does not really conform to the everyday use of the word. Sensitivities are derived from the final model Jacobian matrix, which often is available from the inversion algorithm itself. It needs to be kept in mind that this implies any conclusions drawn are valid in the domain of validity for the Taylor expansion involved only. This may be a grave disadvantage in highly non-linear settings, but we believe that it still can be usefull for fast characterization of uncertainty.

Here, the parameter vector $\mathbf{m}$ is the natural logarithm of resistivity. This Jacobian is first normalized with the data error to obtain $\mathbf{\tilde{J}}$. While this procedure is uncontroversial, the definition of sensitivity is not unique, and various forms an be found in the literature, and $\texttt{JacoPyAn}$ calculates several of them:

1. "Raw" sensitivities, defined as $S_j = \sum_{i=1,n_d} \tilde{J}_{ij}$. No absolute values are involved, hence there may be both, positive and negative, elements. This does not conform to what we expect of sensitivity (positivity), but carries the most direct information on the role of parameter $j$ in the inversion.

2. "Euclidean" sensitivities, which are the most commonly used form. They are is defined as: $S^2_j = \sum_{i=1,n_d} \left||\tilde{J}_{ij}\right||^2=diag\left(\mathbf{\tilde{J}}^T\mathbf{\tilde{J}}\right)$. This solves the positivity issue of raw sensitivities. The square root of this sensitivity is often preferred, and implemented in many popular inversion codes.

3. Coverage. For this form, the absolute values of the Jacobian are used: $\sum_{i=1,n_d} \left||\tilde{J}_{ij}\right||$

For a definition of a depth of investigation (DoI), or model blanking/shading, forms (2) and (3) can be used. This, however, requires the choice of a threshold/scale is required, depending on the form applied.

When moving from the error-normalised Jacobian, $\mathbf{J}_d$ to sensitivity, there are more choices for further normalisation, depending on the understanding and use of this parameter. If sensitivity is to be interpreted as an approximation to a continuous field over the volume of the model, it seems useful normalize by the cell volume. On the other hand, effect of the size is important when investigating the true role of this cell in the inversion. Finally, for comparing different data (sub)sets, it is convenient to do a final normalization by the maximum value in the model. All these options are implemented in the $\texttt{JacoPyAn}$ toolbox.

[1] M. Deal and G. Nolet (1996) “Nullspace shuttles", Geophysical Journal International, 124, 372–380

[2] G. Muñoz and V. Rath (2006) “Beyond smooth inversion: the use of nullspace projection for the exploration of non-uniqueness in MT", Geophysical Journal International, 164, 301–311, 2006, doi: http://dx.doi.org/10.1111/j.1365-246X.2005.02825.x

[3] G. D. Egbert and A. Kelbert (2012) “Computational recipes for electromagnetic inverse problems”, Geophysical Journal International, 189, 251–267, doi: http://dx.doi.org/10.1111/j.1365-246X.2011.05347.x

[4] A. Kelbert, N. Meqbel, G. D. Egbert, and K. Tandon (2014) “ModEM: A Modular System for Inversion of Electromagnetic Geophysical Data”, Computers & Geosciences, 66, 440–53, doi: http://dx.doi.org/10.1016/j.cageo.2014.01.010

[5] A. Tarantola (2005) "Inverse Problem Theory and Methods for Model Parameter Estimation", SIAM, Philadelphia PA, USA