1887
Volume 67, Issue 3
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Interpretation of a single geophysical data set is not sufficient to get complete subsurface information. Cooperative or joint inversion of geophysical data sets is the preferred method for most case studies. In the present study, we present the results of the cooperative inversion approach of direct current resistivity and gravity data. The algorithm uses fuzzy c‐means clustering to determine the petrophysical relationship between density and resistivity to obtain the similarity. Synthetic data set has demonstrated that the cooperative inversion approach can produce more reliable and better resistivity and density models of the subsurface as compared to those obtained through individual inversions. To utilize the presented cooperative inversion algorithm, the number of geologic units (number of clusters) in the study region must be known . As a field study, the cooperative inversion approach was used to identify the extension of uranium‐bearing target rock around the Beldih open cast mine. We noted the inconsistencies in both resistivity and density models obtained from the individual inversions. However, the presented cooperative inversion approach was able to produce similar resistivity and density models while maintaining the same error level of the respective individual inversions. We have considered four geologic units in the presented cooperative inversion as a field case study. We have also compared our cooperative results with drilled borehole and found to be a reliable tool to differentiate between the target rocks (kaolinite and quartz–magnetite–apatite rocks) and the ultramafic rock (host rock quartzite/alkaline granite). However, this study is subject to certain limitations such as the inability to differentiate between closely spaced kaolinite and quartz–magnetite–apatite rocks.

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2019-02-28
2024-04-19
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