3D inversion of magnetic data seeking sharp boundaries: a case study for a porphyry copper deposit from Now Chun in central Iran

This paper describes an application of 3D inversion of magnetic data to recover a susceptibility model from magnetic anomalies. For this purpose, the subsurface of the desired area of the magnetic anomaly is divided into a mesh with a large number of rectangular prisms with unknown susceptibilities. A Tikhonov cost function with multi‐term regularizers involving boundaries of susceptibility distribution and an edge‐preserving penalty function, as a tool to recover sharp boundaries, was used. Three methods (i.e., the U‐curve, Tikhonov‐curve and L‐curve methods) are applied to determine the optimum regularization parameter during the inversion process. Testing of the applied methods showed that the application of the U‐curve (a well‐known method in applied mathematics) in geophysical inverse problems and Tikhonov‐curve as a proposed technique can be appropriate candidates, like a common L‐curve method, for choosing the optimal regularization parameter. To avoid the natural tendency of magnetic structures to concentrate at the shallow depths in models created by inversion, a depth weighting function derived from information of the depth‐to‐the‐bottom of a generating source was applied. The AN‐EUL technique as a combination of the analytic signal and the Euler deconvolution methods is used to estimate the structural index of causative sources in order to construct an appropriate depth weighting function. Here, it is assumed that there is no remanent magnetization and the observed data are influenced by only the induced magnetization. A case study involving ground based measurements over a porphyry‐Cu deposit located in Kerman providence of Iran, Now Chun deposit, is included. The recovered 3D susceptibility model provided beneficial information for design of the exploration drilling programme. The susceptibility lows in the constructed model, in particular, their depths down to 410 m, coincides with the known locations of copper mineralization.