Document Type: Case Study

Authors

1 Department of Mining Engineering, University of Tehran

2 Department of Mining Engineering, College of Engineering, University of Tehran, Iran

3 Department of Mining Engineering, Isfahan University of Technology, Iran

4 Institute of Geophysics, University of Tehran, Tehran, Iran

Abstract

This paper describes the application of approximate methods to invert airborne magnetic data as well as helicopter-borne frequency domain electromagnetic data in order to retrieve a joint model of magnetic susceptibility and electrical resistivity. The study area located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential of mineral occurrences, especially porphyry copper. Based on previous studies, which assume a homogenous half-space earth model, two approximate methods involving the Siemon and the Mundry approaches are used in this study to generate a resistivity-depth image of underground geologically plausible porphyry unit derived from airborne electromagnetic data. The 3D visualization of the 1D inverted resistivity models along all flight lines provides a resistive geological unit which corresponds to the desired porphyry andesite. To reduce uncertainty arising from single geophysical model, i.e., the resistivity model acquired from the frequency domain electromagnetic data, a fast implementable approach for 3D inversion of magnetic data called the Lanczos bidiagonalization method is also applied to the large scale airborne magnetic data in order to construct a 3D distribution model of magnetic susceptibility, by which the obtained model consequently confirms the extension of an arc-shaped porphyry andesite at depth. The susceptible-resistive porphyry andesite model provided by integrated geophysical data indicates a thicker structure than what is shown on the geological map while extends down at depth. As a result, considering simultaneous interpretation of airborne magnetic and frequency domain electromagnetic data certainly yield lower uncertainty in the modeling of andesite unit as a potential source of copper occurrences.

Keywords