{"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/181201429","title":"Automatic salt deposits segmentation: A deep learning approach","arxiv_id":"1812.01429","date":"2018-11-21","proceeding":null,"authors":["Mikhail Karchevskiy","Insaf Ashrapov","Leonid Kozinkin"],"abstract":"One of the most important applications of seismic reflection is the\nhydrocarbon exploration which is closely related to salt deposits analysis.\nThis problem is very important even nowadays due to it's non-linear nature.\nTaking into account the recent developments in deep learning networks TGS-NOPEC\nGeophysical Company hosted the Kaggle competition for salt deposits\nsegmentation problem in seismic image data. In this paper, we demonstrate the\ngreat performance of several novel deep learning techniques merged into a\nsingle neural network which achieved the 27th place (top 1%) in the mentioned\ncompetition. Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as\nour base architecture, we implemented Spatial-Channel Squeeze & Excitation,\nLovasz loss, CoordConv and Hypercolumn methods. The source code for our\nsolution is made publicly available at\nhttps://github.com/K-Mike/Automatic-salt-deposits-segmentation.","url_abs":"http://arxiv.org/abs/1812.01429v1","url_pdf":"http://arxiv.org/pdf/1812.01429v1.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":"181201429","repo_url":"https://github.com/K-Mike/Automatic-salt-deposits-segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"181201429","repo_url":"https://github.com/woans0104/sk_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"coordconv","method_name":"CoordConv"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"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}