| Issue |
E3S Web Conf.
Volume 127, 2019
X Anniversary International Conference “Solar-Terrestrial Relations and Physics of Earthquake Precursors”
|
|
|---|---|---|
| Article Number | 02024 | |
| Number of page(s) | 7 | |
| Section | Geophysical Fields and their Interactions | |
| DOI | https://doi.org/10.1051/e3sconf/201912702024 | |
| Published online | 05 November 2019 | |
Search for geophysical structures by their mathematical models and samples
Institute of Cosmophysical Research and Radio Wave Propagation of the Far Eastern Branch of Russian Academy of Science, 684034, Kamchatskiy kray, Paratunka, Mirnaya str. 7, Russia
1 Corresponding author: vmochalov@ikir.ru
When we analyze geophysical data, the task of searching for structures by their samples and mathematical models often appears. We propose to use deep neural networks (DNN) to search and detect the forms of geophysical structures. At the same time, both the structure samples themselves and the synthesized structure samples according to their mathematical models act as a training dataset. End-to-end demonstration examples of the highlighting of reflection traces from different layers of the ionosphere in the ionograms, as well as the highlighting of whistler forms in the VLF spectrograms are presented.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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