{"id":19398132,"url":"https://github.com/jesperdramsch/seismic-transfer-learning","last_synced_at":"2025-04-24T05:31:24.479Z","repository":{"id":68621131,"uuid":"152241632","full_name":"JesperDramsch/seismic-transfer-learning","owner":"JesperDramsch","description":"Deep-learning seismic facies on state-of-the-art CNN architectures","archived":false,"fork":false,"pushed_at":"2022-05-14T21:18:34.000Z","size":9317,"stargazers_count":80,"open_issues_count":1,"forks_count":55,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-04-11T23:46:33.497Z","etag":null,"topics":["deep-learning","interpretation","machine-learning","malenov","resnet-50","seismic","transfer-learning","vgg16"],"latest_commit_sha":null,"homepage":"https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JesperDramsch.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"custom":"https://dramsch.net/donate"}},"created_at":"2018-10-09T11:45:49.000Z","updated_at":"2025-02-20T23:55:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"e377e2a6-3f29-45cc-b81e-b5dcddfa409f","html_url":"https://github.com/JesperDramsch/seismic-transfer-learning","commit_stats":{"total_commits":10,"total_committers":1,"mean_commits":10.0,"dds":0.0,"last_synced_commit":"2bac5c7271692e6f9baaf7920569fa389968fc78"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JesperDramsch%2Fseismic-transfer-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JesperDramsch%2Fseismic-transfer-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JesperDramsch%2Fseismic-transfer-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JesperDramsch%2Fseismic-transfer-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JesperDramsch","download_url":"https://codeload.github.com/JesperDramsch/seismic-transfer-learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250572302,"owners_count":21452329,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","interpretation","machine-learning","malenov","resnet-50","seismic","transfer-learning","vgg16"],"created_at":"2024-11-10T11:04:58.093Z","updated_at":"2025-04-24T05:31:22.919Z","avatar_url":"https://github.com/JesperDramsch.png","language":"Jupyter Notebook","funding_links":["https://dramsch.net/donate"],"categories":[],"sub_categories":[],"readme":"# Deep learning seismic facies on state-of-the-art CNN architectures\n*[Jesper S. Dramsch](http://orcid.org/0000-0001-8273-905X), Technical University of Denmark, and Mikael Lüthje, Technical University of Denmark*\n\n## Abstract\n\u003e We explore propagation of seismic interpretation by deep learning in stacked 2D sections. We show the application of state-of-the-art image classification algorithms on seismic data. These algorithms were trained on big labeled photograph databases. We use transfer learning to benefit from pre-trained networks and evaluate their performance on seismic data.\n\n```\nPresentation Date: Wednesday, October 17, 2018\nStart Time: 8:30:00 AM\nLocation: 204B (Anaheim Convention Center)\nPresentation Type: Oral\n```\n---\n\n## Citation\n\n### Paper\n\u003e Jesper S. Dramsch and Mikael Lüthje (2018) Deep-learning seismic facies on state-of-the-art CNN architectures. SEG Technical Program Expanded Abstracts 2018: pp. 2036-2040.\n\n### Presentation\n\u003e Dramsch, Jesper Soeren; Lüthje, Mikael (2018): Deep-learning seismic facies on state-of-the-art CNN architectures. figshare. Presentation.\nhttps://doi.org/10.6084/m9.figshare.7301645.v1\n\n### Code\n\u003e Dramsch, Jesper Soeren; Lüthje, Mikael (2018): Deep-learning seismic facies on state-of-the-art CNN architectures. figshare. Code.\nhttps://doi.org/10.6084/m9.figshare.7227545\n\n## Usage\n\n- Open the [Notebook](Seismic%20Classifiers-Pub.ipynb)\n- Download the [F3 Seismic Data](https://terranubis.com/datainfo/Netherlands_Offshore_F3_Block_-_Complete)\n- Download Models from the [Model Zoo](https://keras.io/applications/#models-for-image-classification-with-weights-trained-on-imagenet)\n- Have Fun Experimenting\n\n## Interpretation of VGG\n\n![Interpretation of VGG](vgg1_i.png)\n\n## Loss of VGG\n\n![Loss of VGG](vgg1_loss.png)\n\n## References\n\n- Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, 2015, TensorFlow: Large-scale machine learning on heterogeneous systems. (Software available from tensorflow.org). \n- Baxter, J., 1998, Theoretical models of learning to learn, in Learning to learn: Springer, 71–94. \n- Charles Rutherford Ildstad, P. B., 2017, MalenoV. Machine learning of Voxels. \n- Chollet, F., et al., 2015, Keras, \n- Dahl, G. E., T. N. Sainath, and G. E. Hinton, 2013, Improving deep neural networks for LVCSR using rectified linear units and dropout: Presented at the IEEE International Conference on Acoustics Speech and Signal Processing. \n- Deng, J., W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, 2009, ImageNet: A large-scale hierarchical image database: Presented at the CVPR09. \n- He, K., X. Zhang, S. Ren, and J. Sun, 2016, Deep residual learning for image recognition: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778. \n- Krizhevsky, A., I. Sutskever, and G. E. Hinton, 2012, ImageNet classification with deep convolutional neural networks, in Advances in neural information processing systems: Curran Associates, Inc. 25, 1097–1105. \n- Lecun, Y., 1989, Generalization and network design strategies, in Connectionism in perspective: Elsevier. \n- Lin, T.-Y., P. Goyal, R. Girshick, K. He, and P. Dollar, 2017, Focal loss for dense object detection: arXiv preprint arXiv:1708.02002. \n- Long, J., E. Shelhamer, and T. Darrell, 2015, Fully convolutional networks for semantic segmentation: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431–3440. \n- Ruder, S., 2016, An overview of gradient descent optimization algorithms: arXiv preprint arXiv:1609.04747. \n- Rumelhart, D., G. Hinton, and R. Williams, 1988, Learning internal representations by error propagation, in Readings in cognitive science: Elsevier, 399–421. \n- Simonyan, K., and A. Zisserman, 2014, Very deep convolutional networks for large-scale image recognition: arXiv preprint arXiv:1409.1556. \n- Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, 2014, Dropout: A simple way to prevent neural networks from overfitting: Journal of Machine Learning Research, 15, 1929–1958. \n- Waldeland, A., and A. Solberg, 2016, 3D attributes and classification of salt bodies on unlabelled datasets: 78th Annual International Conference and Exhibition, EAGE, Extended Abstracts, https://doi.org/10.3997/2214-4609.201600880\n- Widrow, B., and M. Lehr, 1990, 30\u0026nbsp;years of adaptive neural networks: Perceptron Madaline, and backpropagation: Proceedings of the IEEE, 78, 1415–1442,  https://doi.org/10.1109/5.58323\n- Yilmaz, Ö., 2001, Seismic data analysis: SEG.\n\n## Notes\nWe explore transfer training for automatic seismic interpretation without fine-tuning.\nSee and cite the [Powerpoint](https://doi.org/10.6084/m9.figshare.7301645.v1)\n\n---\nRead More: https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1\nOr at: https://dramsch.net/#portfolio\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesperdramsch%2Fseismic-transfer-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjesperdramsch%2Fseismic-transfer-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesperdramsch%2Fseismic-transfer-learning/lists"}