# A Machine Learning Benchmark for Facies Classification

arXiv: https://arxiv.org/abs/1901.07659 · HTML: https://mlsanity.com/paper/1901.07659/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2019.

## Built on by (most cited first, 27 of 27)

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- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Extracting Causal Visual Features for Limited label Classification](https://mlsanity.com/paper/2103.12322/) (2021)
- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [FOCAL: A Cost-Aware Video Dataset for Active Learning](https://mlsanity.com/paper/2311.10591/) (2023)
- [Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation](https://mlsanity.com/paper/2212.07568/) (2022)
- [Explaining Deep Models through Forgettable Learning Dynamics](https://mlsanity.com/paper/2301.04221/) (2023)
- [Benchmarking Human and Automated Prompting in the Segment Anything Model](https://mlsanity.com/paper/2410.22048/) (2024)
- [Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation](https://mlsanity.com/paper/2302.14644/) (2023)
- [A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior](https://mlsanity.com/paper/2505.08585/) (2025)
- [Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection](https://mlsanity.com/paper/2108.09605/) (2021)
- [CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults](https://mlsanity.com/paper/2408.11185/) (2024)
- [Explainable Machine Learning for Hydrocarbon Prospect Risking](https://mlsanity.com/paper/2212.07563/) (2022)
- [ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion](https://mlsanity.com/paper/2302.06441/) (2023)
- [BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation](https://mlsanity.com/paper/2603.10828/) (2026)
- [OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI](https://mlsanity.com/paper/2605.20539/) (2026)
- [Learning velocity model for complex media with deep convolutional neural networks](https://mlsanity.com/paper/2110.08626/) (2021)
- [CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification](https://mlsanity.com/paper/2210.04776/) (2022)
- [Efficient 3D affinely equivariant CNNs with adaptive fusion of augmented spherical Fourier-Bessel bases](https://mlsanity.com/paper/2402.16825/) (2024)
- [Effective Data Selection for Seismic Interpretation through Disagreement](https://mlsanity.com/paper/2406.05149/) (2024)
- [AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies](https://mlsanity.com/paper/2501.16760/) (2025)
- [Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding](https://mlsanity.com/paper/2507.00419/) (2025)
- [Scale-Aware Self-Supervised Learning for Segmentation of Small and Sparse Structures](https://mlsanity.com/paper/2601.18619/) (2026)
- [A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS](https://mlsanity.com/paper/2602.04890/) (2026)
- [RADMI: Latent Information Aggregation as a Proxy for Model Uncertainty](https://mlsanity.com/paper/2605.01502/) (2026)
- [Consensus-gated Multi-Agent Neural Architecture Search for Seismic Fault Segmentation](https://mlsanity.com/paper/2608.13889/) (2026)
- [Voxel-based 3D Facies Segmentation from Seismic Data: A Comparative Study](https://mlsanity.com/paper/2608.14058/) (2026)

## Abstract (arXiv)

The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply machine learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach. To address these issues, we open-source a fully-annotated 3D geological model of the Netherlands F3 Block. This model is based on the study of the 3D seismic data in addition to 26 well logs, and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on a deconvolution network architecture and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work helps advance research in this area by creating an objective benchmark for comparing the results of different machine learning approaches for facies classification.