Datasets for Deep Learning for Classifying and Characterizing Atmospheric Ducting Within the Maritime Setting

Published: 26-05-2020| Version 1 | DOI: 10.17632/58hzp48z8y.1
Contributors:
Hilarie Sit,
Christopher Earls

Description

Propagation factors are sparsely sampled along a horizontal flight path, at an altitude of 6m and range between 20 and 50km, within the rectangular 2D problem domain described in the associated article. We consider evaporation ducting environments with varying duct heights as well as surface-based ducting environments with varying combinations of base height, duct thickness, and M-deficit. Datasets are generated using the open source software, PETOOL, by Ozgun et al. 2011 (doi: 10.17632/yh42r43cpy.1). Code for processing data and training/evaluating the deep learning model can be found at: https://github.com/nonlinearfun/deep-learning-em-ducting

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