datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SeismicX-Cont-mini
SeismicX-Cont Mini Two-Hour Subset
This folder is the compact, Zenodo-archived two-hour mini release for
SeismicX-Cont. It is designed for quick download, tutorial use, software smoke
tests, and checking that the HDF5, annotation, SQLite, dataloader, picker, and
validation workflow all fit together before using the full 14-day data product.
Zenodo record: https://zenodo.org/records/21331024
DOI: https://doi.org/10.5281/zenodo.21331024
Hugging Face record:… See the full description on the dataset page: https://huggingface.co/datasets/cangyeone/SeismicX-Cont-mini.Seismicnasa_space_apps_2024_seismic_detection
Welcome, Space Apps challengers, to “Seismic Detection across the Solar System”.
Today, we challenge YOU to parse through seismic data collected on the Moon and Mars and figure out how to detect moonquakes and marsquakes!
To get you started on the data, we present to you a training set containing the following:
A catalog of quakes identified in the data
Seismic data collected by the Apollo (one day segments) or InSight (one hour segments) missions in miniseed and CSV format.… See the full description on the dataset page: https://huggingface.co/datasets/MH0386/nasa_space_apps_2024_seismic_detection.seismicfoundationmodel-geobodyThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-geobody.seismicfoundationmodel-interpolationThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-interpolation.seismicfoundationmodel-denoiseThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-denoise.seismicfoundationmodel-denoise-fieldThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-denoise-field.seismicfoundationmodel-faciesThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-facies.seismicfoundationmodel-inversion-seamThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-inversion-seam.crossdomainfoundationmodeladaption-seismicfaciesThis dataset is part of the work by Guo Zhixiang et al.https://github.com/ProgrammerZXG/Cross-Domain-Foundation-Model-Adaptation?tab=readme-ov-file
The dataset is originally available on Zenodo https://zenodo.org/records/12798750
And licensed under Creative Commons Attribution 4.0 International
Please cite the following article if you use this dataset:
@misc{guo2024crossdomainfoundationmodeladaptation,
title={Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for… See the full description on the dataset page: https://huggingface.co/datasets/porestar/crossdomainfoundationmodeladaption-seismicfacies.seismicfoundationmodel-inversion-syntheticThis dataset is part of the work by Hanlin Sheng et al. https://github.com/shenghanlin/SeismicFoundationModel
Please cite the following article if you use this dataset:
@article{sheng2023seismic,
title={Seismic Foundation Model (SFM): a new generation deep learning model in geophysics},
author={Sheng, Hanlin and Wu, Xinming and Si, Xu and Li, Jintao and Zhang, Sibio and Duan, Xudong},
journal={arXiv preprint arXiv:2309.02791},
year={2023}
}
Additional information can be found at… See the full description on the dataset page: https://huggingface.co/datasets/porestar/seismicfoundationmodel-inversion-synthetic.synthetic-seismic-vlm
Synthetic Seismic VLM
This dataset contains synthetic seismic multimodal QA rows with raw seismic
images, segmentation masks, evidence-grounded questions, answers, and compact
region metadata.
Rows: 1261
Repository: https://huggingface.co/datasets/thirdExec/synthetic-seismic-vlm
Columns:
images: sequence of all raw images
masks: sequence of all mask images
instruction: task instruction
question: question text
answer: answer text
evidence: JSON string of supporting text evidence… See the full description on the dataset page: https://huggingface.co/datasets/thirdExec/synthetic-seismic-vlm.seismic_data_graphspace_apps_2024_seismic_detectionseismic_data_9_fold_for_train