Magnetic Anomalies Characterization: Deep Learning and Explainability - Sorbonne Université
Journal Articles Computers and Geosciences Year : 2022

Magnetic Anomalies Characterization: Deep Learning and Explainability

Julio José Cárdenas Chapellín
Christophe Denis
Agents Cognitifs et Apprentissage Symbolique Automatique
CV
Hajar Mousannif
  • Function : Author
  • PersonId : 993367
Christian Camerlynck
  • Function : Author
  • PersonId : 1043831
Nicolas Florsch
  • Function : Author

Domains

Artificial Intelligence [cs.AI] Geophysics [physics.geo-ph]

Christophe DENIS  : Connect in order to contact the contributor

https://hal.sorbonne-universite.fr/hal-03771561

Submitted on : Wednesday, September 7, 2022-2:41:46 PM

Last modification on : Friday, September 13, 2024-10:46:03 AM

No file

Dates and versions

hal-03771561 , version 1 (07-09-2022)

Identifiers

  • HAL Id : hal-03771561 , version 1

Cite

Julio José Cárdenas Chapellín, Christophe Denis, Hajar Mousannif, Christian Camerlynck, Nicolas Florsch. Magnetic Anomalies Characterization: Deep Learning and Explainability. Computers and Geosciences, In press. ⟨hal-03771561⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CNRS LIP6 SORBONNE-UNIVERSITE SU-SCIENCES
299 View
0 Download

Share

More