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Machine Learning for Geomagnetic Data Reconstruction by nagalaxmi is a document available to read on EtoBox.

This document presents a new approach for reconstructing undersampled geomagnetic data using machine learning techniques. The traditional linear interpolation approaches are time inefficient and labor intensive, while the proposed machine learning approach shows significant improvement. The approach uses support vector machines, random forests, gradient boosting, and recurrent neural networks to specify a continuous regression hyperplane from training data to reconstruct missing geomagnetic traces. Numerica

Author

nagalaxmi

Language

EN