Pseudosection approaches have severe limitations in that they represent a very poor approximate and linearised inversion of the true problem --- they are known to be sensitive to noise and to produce major spurious artefacts. Further, they demand a simple and rigidly defined data set: they cannot readily cope with missing data values or with the large and complex oversampled data sets capable of being provided by the new multiprobe resistive tomography systems.
The inversion of the data sets arising from resistivity surveying is a difficult problem which offers a number of unusual challenges to the Maximum Entropy method in that each data value is, in theory, influenced by changes in any region of the subsurface: a decentralised `geophysical blur' is involved, but the point-spread function of this blur is not constant over the data set --- it depends radically not only on the particular configuration of the probes and the distances between them, but also on the (unknown) nature of the subsurface. The single-boundary nature of the problem also means that the quality of the reconstruction must decrease rapidly with depth and, in combination with the limited sample size, means that instrumental noise will impact on different regions of the parameterised subsurface to different degrees.
In most applications a high resolution is required and the inversion should be robust to noise and easy to interpret: in this paper a number of comparative studies using a nonlinear finite-element forward model show that the Maximum Entropy approach offers significant advantages over not only the established, pragmatic, methods but also other inversion routines.