Lithologic tomography: an application to geophysical data from the Cadomian belt of northern Brittany, France

A probabilistic description of subsurface lithologic structures can be esta blished by inverting multidisciplinary geophysical data constrained by geol ogical and geostatistical priors. The methodology is based on the joint mod elling of several media properties and on a statistical description of the relationships between them. The information provided by the geophysical dat a and the geological and geostatistical priors is represented by probabilit y density functions (pdf) that are combined into a posterior pdf composed b y: (1) a prior pdf in the space of the primary (lithologic) model parameter s, (2) a pdf of the secondary (physical) model parameters conditional to th e primary model parameters and (3) a joint likelihood function that is the product of the independent likelihood functions for each observed geophysic al field. Applying a Markov chain sampling method enables a large sample of joint models to be generated from the posterior pdf. The true configuratio n of the media is then determined from the representation of models pulled from the chain and the elaboration of statistics from the large sample of p osterior joint models. The method was used to invert gravity and magnetic d ata jointly characterising the mass density field, the magnetic susceptibil ity field and the lithotype field along two 2-D sections of the geological units in the Cadomian belt of northern Brittany. Besides generating 10(6) j oint models consistent with the observations and priors, some features of t he joint models and the statistical tomographic images provided additional insights to the geologic configuration of the area. For example, the Main C adomian Thrust shows an irregular geometry that could have resulted from th e belt emplacement and/or from Variscan tectonism, and the Hercynian granit ic intrusion shows a deep subsurface continuation. The cosimulation of magn etic susceptibility and mass density inside each lithologic region was perf ormed according to a multivariate Gaussian model or a mixed multivariate Ga ussian functions model that was developed specially to describe multimodal distributed properties. (C) 2001 Elsevier Science B,V, All rights reserved.