3D stochastic inversion of magnetic data

A stochastic inversion method based on a geostatistical approach is presented to recover 3D susceptibility models from magnetic data. The aim of applying geostatistics is to provide quantitative descriptions of natural variables distributed in space or in time and space. Cokriging, the method which is used in this paper, is a method of estimation that minimizes the theoretical estimation error variance by using auto- and cross-correlations of several variables. The covariances for total field, susceptibility and total field-susceptibility are estimated using the observed data. Then, the susceptibility is cokriged or simulated as the primary variable. In order to avoid the natural tendency of the estimated structure to lay near the surface, depth weighting is included in the cokriging system. The algorithm assumes there is no remanent magnetization and the observation data represent only induced magnetization effects. The method is applied on different synthetic models to demonstrate its suitability for 3D inversion of magnetic data. A case study using ground measurements of total field at the Perseverance mine (Quebec, Canada) is presented. The recovered 3D susceptibility model provides beneficial information that can be used to analyze the geology of massive sulfide for the domain under study.

Research Highlights
► A 3D stochastic magnetic inversion is presented using Geostatistics.
► The simulations allow identification of stable features of the inverted fields.
► Depth weighting is included in the cokriging system.
► The susceptibility provides beneficial information to detect massive sulfide.