3D Cross-Correlation Modelling of Shavvaz mine’s Magnetometry data, Yazd, Iran

The paper introduces the 3D Cross-Correlation for modeling of total magnetic intensity and its vertical gradient which is the fast way to model data, detect anomalies and estimate their depths and locations. In this approach first, we divide the subsurface space into a 3D regular grid, after computing the correlation value for each node of the grid, these values are plotted. It is noted that the results fall in the range [-1, +1] that represents the mass excess or mass deficit of magnetization (or susceptibility) relative to the magnetization (or susceptibility) of the host volume. This approach is applied to 2 synthetic models. The results show acceptable accuracy of this method in depth estimation and expansion of buried masses. After this method is verified and validated, it will be applied to the Shavvaz mine's total magnetic intensity (TMI) data of Yazd and its vertical gradient, and the results will be discussed.

There are two major approaches for 3D inversion of magnetic data: (i) direct inversion of the density contrast using a linear or nonlinear algorithm, and (ii) modeling of the source distribution in a purely probabilistic sense, in which the results are equivalent physical parameters between +1 and -1. Direct inversion has an inherent problem called Non-uniqueness of solutions and requires a lot of computer memory because of the number of model parameters and data.
In this paper, we introduce and evaluate the 3D Cross-Correlation (CC) method for 3D modeling of magnetic data (or its vertical gradient). This method was applied to 2 different synthetic models and its strengths in modeling of total magnetic field anomaly and vertical magnetic gradient data are discussed and finally, the method was applied to the Shavvaz mine's TMI data and its vertical gradient.
 

Methodology and Approaches:

The 3D cross-correlation approach is a method for modeling the magnetic data (or its vertical gradient) without any external constraints and any linearization.

The results of synthetic examples showed the high accuracy of the CC method in determining the shape and depth of the buried mass. This method is simple and easy to run and there is no need for prior information. In the end, this method was applied to the Shavvaz mine's TMI data and its vertical gradient. These results showed that orebody continues deeper than 40m (estimated from the previous modelling).