Magnetic anomalies characterization: Deep learning and explainability

Cardenas J.; Denis C.; Mousannif H.Florsch N.Camerlynck C.

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Magnetic anomalies characterization: Deep learning and explainability

机译：Magnetic anomalies characterization: Deep learning and explainability

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? 2022 Elsevier LtdIn recent years, deep learning methods have shown great promise in the field of geophysics, especially for seismic interpretation. However, there is very little information with regard to its application in the field of magnetic methods. Our research introduces the use of convolutional neural networks for the characterization of magnetic anomalies. The models developed allow 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). To go even further, we applied visualization tools to understand our model's predictions and its working principle. The Grad-CAM tool improved prediction performance by identifying several layers that had no influence on the prediction and the t-SNE tool confirmed the strong capacity of our model to differentiate between different parameter combinations. Then, we tested our model with real data to establish its limitations and application domain. Results demonstrate that our model detects dipolar anomalies in a real magnetic map even after learning from a synthetic database with a lower complexity, which indicates a significant generalization capability. We also noticed that it is unable to identify dipole anomalies of shapes and sizes different from those considered for the creation of the synthetic database. Finally, the perspectives for this work consist of creating a more complex database to approach the complexity traditionally observed in magnetic maps, using real data from multiple acquisition campaigns, and other applications with alternative geophysical methods.

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《Computers & geosciences》 |2022年第12期|40-53|共14页
作者
Cardenas J.; Denis C.; Mousannif H.Florsch N.Camerlynck C.;
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Sorbonne Université IRD Unité de Modélisation Mathématique et Informatique des Systèmes Complexes UMMISCOSorbonne Université IRD Unité de Modélisation Mathématique et Informatique des Systèmes Complexes UMMISCO||UMR 7619 METIS Sorbonne Université;

||Sorbonne Université IRD Unité de Modélisation Mathématique et Informatique des Systèmes Complexes UMMISCO||LIP6 Sorbonne Université;

Sorbonne Université IRD Unité de Modélisation Mathématique et Informatique des Systèmes Complexes UMMISCO||LISI Laboratory FSSM Cadi Ayyad University Bd. Prince My AbdellahSorbonne Université IRD Unité de Modélisation Mathématique et Informatique des Systèmes Complexes UMMISCO||UMR 7619 METIS Sorbonne UniversitéUMR 7619 METIS Sorbonne Université;

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Convolutional neural networks; Deep learning; Explainability; Geophysics; Magnetic methods; Unexploded ordnance;

Magnetic anomalies characterization: Deep learning and explainability

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