3D stochastic assimilation of gravity data in Lalor volcanogenic massive sulphide, Manitoba, Canada

In this study, we propose a stochastic assimilation workflow with the main objective of producing a geologically meaningful density model with the minimum misfit between the observed and measured surface gravity data. First, we get the geological model of the study area as an initial structural model of the parameters and we generate multiple scenarios of this model by using the Multiple Point Simulation method (MPS). Then, we perform forward modelling on gradually combined generated realizations to obtain measured gravity data. Finally, the results are compared by the observed data to validate the accuracy of the method and to have the model corresponding to the minimum misfit. It is concluded that the optimized model of density contrasts represents an improved structural similarity with the reference model compared to conventional simulations.