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Accurate subsurface imaging is crucial for understanding complex geological structures. Traditional approaches often involve separate inversion of different geophysical datasets, which may not fully capture the structural similarities between the models. Joint inversion, integrating multiple datasets, offers a more comprehensive view by enhancing the resolution and the accuracy of subsurface models. In this study, we propose a joint inversion technique for DC resistivity and vertical gravity gradient data, leveraging the cross-gradient constraint to enforce structural similarities between the resulting models. This method is applied to both synthetic and real datasets, including case studies involving qanats in Iran and a dolerite dyke in South Africa. The results demonstrate that joint inversion significantly improves the accuracy of resistivity and density models compared to independent inversion, particularly in resolving intricate geological features. This approach has proven effective in enhancing subsurface mapping for multi-disciplinary purposes, including resource exploration, heritage conservation, and risk mitigation for the built environment.
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; Varfinezhad, Ramin 3
; Milano, Maurizio 4
; Fourie, Francois Daniel 5
; Tosti, Fabio 2
1 Department of Mining and Metallurgical Engineering, Yazd University, Yazd 8915818411, Iran;
2 School of Computing and Engineering, University of West London, London W5 5RF, UK;
3 Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran 1435944411, Iran;
4 Department of Earth, Environmental and Resources Science, University of Naples Federico II, 80126 Naples, Italy;
5 Institute for Groundwater Studies, University of the Free State, Bloemfontein 9300, South Africa;