Search for geophysical structures by their mathematical models and samples
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 synthe...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2019-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/53/e3sconf_strpep2019_02024.pdf |
| _version_ | 1811270820003053568 |
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| author | Mochalov Vladimir Mochalova Anastasia |
| author_facet | Mochalov Vladimir Mochalova Anastasia |
| author_sort | Mochalov Vladimir |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T22:08:59Z |
| format | Article |
| id | doaj.art-c62df846d37745c6b8bbb7e655066e32 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-12T22:08:59Z |
| publishDate | 2019-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c62df846d37745c6b8bbb7e655066e322022-12-22T03:14:49ZengEDP SciencesE3S Web of Conferences2267-12422019-01-011270202410.1051/e3sconf/201912702024e3sconf_strpep2019_02024Search for geophysical structures by their mathematical models and samplesMochalov VladimirMochalova AnastasiaWhen 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/53/e3sconf_strpep2019_02024.pdf |
| spellingShingle | Mochalov Vladimir Mochalova Anastasia Search for geophysical structures by their mathematical models and samples E3S Web of Conferences |
| title | Search for geophysical structures by their mathematical models and samples |
| title_full | Search for geophysical structures by their mathematical models and samples |
| title_fullStr | Search for geophysical structures by their mathematical models and samples |
| title_full_unstemmed | Search for geophysical structures by their mathematical models and samples |
| title_short | Search for geophysical structures by their mathematical models and samples |
| title_sort | search for geophysical structures by their mathematical models and samples |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/53/e3sconf_strpep2019_02024.pdf |
| work_keys_str_mv | AT mochalovvladimir searchforgeophysicalstructuresbytheirmathematicalmodelsandsamples AT mochalovaanastasia searchforgeophysicalstructuresbytheirmathematicalmodelsandsamples |