Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03321334
Contributor : Florent BREUIL Connect in order to contact the contributor
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

File

actes_CNIA_CH_PFIA2021 - carde...
Explicit agreement for this submission

Identifiers

  • HAL Id : hal-03321334, version 1

Citation

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⟩

Share

Metrics

Record views

151

Files downloads

55