A hybrid optimization scheme for self-potential measurements due to multiple sheet-like bodies in arbitrary 2D resistivity distributions |
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| Authors: | Iraklis Giannakis Panagiotis Tsourlos Costas Papazachos George Vargemezis Antonios Giannopoulos Nikos Papadopoulos Fabio Tosti Amir Alani |
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| Institution: | 1. School of Computing and Engineering, University of West London, St Mary's Road, London, W5 5RF United Kingdom;2. Department of Geophysics, School of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;3. School of Engineering, Institute for Infrastructure and Environment, The University of Edinburgh, Edinburgh, EH9 3FG United Kingdom;4. Foundation for Research and Technology, Hellas (FORTH), Laboratory of Geophysical Satellite Remote Sensing and Archaeo-Environment, Institute for Mediterranean Studies, Melissinou and Nik Foka 130, 74100 Rethymno, Crete, Greece |
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| Abstract: | Self-potential is a passive geophysical method that can be applied in a straightforward manner with minimum requirements in the field. Nonetheless, interpretation of self-potential data is particularly challenging due to the inherited non-uniqueness present in all potential methods. Incorporating information regarding the target of interest can facilitate interpretation and increase the reliability of the final output. In the current paper, a novel method for detecting multiple sheet-like targets is presented. A numerical framework is initially described that simulates sheet-like bodies in an arbitrary 2D resistivity distribution. A scattered field formulation based on finite differences is employed that allows the edges of the sheet to be independent of the grid geometry. A novel analytical solution for two-layered models is derived and subsequently used to validate the accuracy of the proposed numerical scheme. Lastly, a hybrid optimization is proposed that couples linear least-squares with particle-swarm optimization in order to effectively locate the edges of multiple sheet-like bodies. Through numerical and real data, it is proven that the hybrid optimization overcomes local minimal that occurs in complex resistivity distributions and converges substantially faster compared to traditional particle-swarm optimization. |
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| Keywords: | Inversion Modelling Numerical study Passive method Potential field |
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