| DOI | Trouver le DOI : https://doi.org/10.1190/segam2019-3215629.1 |
|---|
| Auteur | Rechercher : Naprstek, Tomas1; Rechercher : Smith, Richard |
|---|
| Affiliation | - Conseil national de recherches du Canada. Aérospatiale
|
|---|
| Format | Texte, Article |
|---|
| Conférence | SEG Technical Program Expanded Abstracts 2019, 15 September 15-20, 2019, San Antonio, Texas |
|---|
| Résumé | We investigate potential applications of two machine learning methods, random forest regression and support vector regression, in aeromagnetic data interpolation. By developing relevant predictors, and training these two methods on synthetic aeromagnetic data, we are able to interpolate a new dataset with success. We show that a “filter”-based approach where standard interpolation methods are fed into the training as predictors allows the machine learning to produce an interpolation that is better than any one of the standard interpolations by themselves. |
|---|
| Date de publication | 2019-08-10 |
|---|
| Maison d’édition | Society of Exploration Geophysicists |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | cb39dad9-f13e-4849-ae74-87e9e9d91bca |
|---|
| Enregistrement créé | 2021-12-20 |
|---|
| Enregistrement modifié | 2021-12-20 |
|---|
