{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepvel-deep-learning-for-the-estimation-of","title":"DeepVel: deep learning for the estimation of horizontal velocities at the solar surface","arxiv_id":"1703.05128","date":"2017-03-15","proceeding":null,"authors":["A. Asensio Ramos","I. S. Requerey","N. Vitas"],"abstract":"Many phenomena taking place in the solar photosphere are controlled by plasma\nmotions. Although the line-of-sight component of the velocity can be estimated\nusing the Doppler effect, we do not have direct spectroscopic access to the\ncomponents that are perpendicular to the line-of-sight. These components are\ntypically estimated using methods based on local correlation tracking. We have\ndesigned DeepVel, an end-to-end deep neural network that produces an estimation\nof the velocity at every single pixel and at every time step and at three\ndifferent heights in the atmosphere from just two consecutive continuum images.\nWe confront DeepVel with local correlation tracking, pointing out that they\ngive very similar results in the time- and spatially-averaged cases. We use the\nnetwork to study the evolution in height of the horizontal velocity field in\nfragmenting granules, supporting the buoyancy-braking mechanism for the\nformation of integranular lanes in these granules. We also show that DeepVel\ncan capture very small vortices, so that we can potentially expand the scaling\ncascade of vortices to very small sizes and durations.","url_abs":"http://arxiv.org/abs/1703.05128v2","url_pdf":"http://arxiv.org/pdf/1703.05128v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepvel-deep-learning-for-the-estimation-of","repo_url":"https://github.com/aasensio/deepvel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"3d-depth-estimation","task_name":"3D Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}