Seismic datasets with seismic facies and facies boundaries labels (public)

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Authors: Jorge Leonardo
Cited by: 0

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