Cross-Gradient Joint Inversion of DC Resistivity and Gravity Gradient Data: A Multi-Disciplinary Approach for Geoscience, Heritage, and the Built Environment

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Bibliografiske detaljer
Udgivet i:Remote Sensing vol. 16, no. 23 (2024), p. 4468
Hovedforfatter: Ghari, Hosseinali
Andre forfattere: Parnow, Saeed, Varfinezhad, Ramin, Milano, Maurizio, Fourie, Francois Daniel, Tosti, Fabio
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MDPI AG
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024 7 |a 10.3390/rs16234468  |2 doi 
035 |a 3144158339 
045 2 |b d20240101  |b d20241231 
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100 1 |a Ghari, Hosseinali  |u Department of Mining and Metallurgical Engineering, Yazd University, Yazd 8915818411, Iran; <email>hghari@yazd.ac.ir</email> 
245 1 |a Cross-Gradient Joint Inversion of DC Resistivity and Gravity Gradient Data: A Multi-Disciplinary Approach for Geoscience, Heritage, and the Built Environment 
260 |b MDPI AG  |c 2024 
513 |a Journal Article 
520 3 |a 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. 
653 |a Software 
653 |a Similarity 
653 |a Datasets 
653 |a Built environment 
653 |a Geological mapping 
653 |a Gravity 
653 |a Bibliometrics 
653 |a Models 
653 |a Geological structures 
653 |a Electrical resistivity 
653 |a Algorithms 
653 |a Subsurface mapping 
653 |a Risk reduction 
653 |a Urban environments 
653 |a Resource exploration 
700 1 |a Parnow, Saeed  |u School of Computing and Engineering, University of West London, London W5 5RF, UK; <email>fabio.tosti@uwl.ac.uk</email>; The Faringdon Research Centre for Non-Destructive Testing and Remote Sensing, University of West London, London W5 5RF, UK 
700 1 |a Varfinezhad, Ramin  |u Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran 1435944411, Iran; <email>ramin.varfi@ut.ac.ir</email> 
700 1 |a Milano, Maurizio  |u Department of Earth, Environmental and Resources Science, University of Naples Federico II, 80126 Naples, Italy; <email>maurizio.milano@unina.it</email> 
700 1 |a Fourie, Francois Daniel  |u Institute for Groundwater Studies, University of the Free State, Bloemfontein 9300, South Africa; <email>fouriefd@ufs.ac.za</email> 
700 1 |a Tosti, Fabio  |u School of Computing and Engineering, University of West London, London W5 5RF, UK; <email>fabio.tosti@uwl.ac.uk</email>; The Faringdon Research Centre for Non-Destructive Testing and Remote Sensing, University of West London, London W5 5RF, UK 
773 0 |t Remote Sensing  |g vol. 16, no. 23 (2024), p. 4468 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3144158339/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3144158339/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3144158339/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch