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Réseaux de Neurones Convolutifs pour la Caractérisation d’Anomalies Magnétiques

Abstract : This contribution introduces the use of convolutional neural networks for the characterization of magnetic anomalies. The developed approach allows one the localization of magnetic dipoles, including counting the number of dipoles, their geographical position, and the prediction of their parameters (magnetic moment, depth, and declination). Subsequently, it will be tested on real data, for example, in the framework of pyrotechnic detection for unexploded ordnance prospection. Finally, an application to other geophysical methods will be considered.
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Submitted on : Tuesday, August 17, 2021 - 2:30:15 PM
Last modification on : Wednesday, September 28, 2022 - 4:20:11 PM
Long-term archiving on: : Thursday, November 18, 2021 - 6:44:16 PM


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  • HAL Id : hal-03321334, version 1


Julio José Cárdenas Chapellín, Christophe Denis, Hajar Mousannif, Christian Camerlynck, Nicolas Florsch. Réseaux de Neurones Convolutifs pour la Caractérisation d’Anomalies Magnétiques. CNIA 2021 : Conférence Nationale en Intelligence Artificielle, Jun 2021, Bordeaux (en ligne), France. pp.84-90. ⟨hal-03321334⟩



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