Seismic datasets with seismic facies and facies boundaries labels (public)
Abstract
Compilation of the three most widely used publicly available seismic datasets with facies annotations in machine learning studies, namely Netherlands F3 Block, Parihaka and Penobscot. The dataset includes the seismic volumes, facies annotations, and facies boundary labels derived from the interpreted facies. All data were organized, standardized, and stored as 3D arrays in .npy format. Note: The datasets included in this compilation originate from previously published sources. Proper credit should be given to the original authors and data providers listed bellow. 1. Netherlands F3 Block Silva, R. M., Baroni, L., Ferreira, R. S., Civitarese, D., Szwarcman, D., & Brazil, E. V. (2019).Netherlands dataset: A new public dataset for machine learning in seismic interpretation.arXiv preprint arXiv:1904.00770. 2. Penobscot Baroni, L., Silva, R. M., Ferreira, R. S., Civitarese, D., Szwarcman, D., & Brazil, E. V. (2019).Penobscot dataset: Fostering machine learning development for seismic interpretation.arXiv preprint arXiv:1903.12060. 3. Parihaka Chevron U.S.A. Inc. (2020).SEG Annual Meeting Machine Learning Interpretation Workshop Dataset (Parihaka).Available at: https://public.3.basecamp.com/p/JyT276MM7krjYrMoLqLQ6xST
Fields of Study & Subjects
Seismic Imaging and Inversion Techniques
Geological Studies and Exploration
Geological Modeling and Analysis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 March 2026
- Language
- EN
- Document type
- Dataset