# OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI

arXiv: https://arxiv.org/abs/2605.20539 · HTML: https://mlsanity.com/paper/2605.20539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 5 of 5)

- [A Machine Learning Benchmark for Facies Classification](https://mlsanity.com/paper/1901.07659/) (2019)
- [WISE: full-Waveform variational Inference via Subsurface Extensions](https://mlsanity.com/paper/2401.06230/) (2023)
- [WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction](https://mlsanity.com/paper/2405.10327/) (2024)
- [Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models](https://mlsanity.com/paper/2406.05136/) (2024)
- [Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology](https://mlsanity.com/paper/2502.07169/) (2025)

## Built on by (most cited first, 1 of 1)

- [Priors learned from legacy reconstructions inherit undetectable overconfidence](https://mlsanity.com/paper/2607.21721/) (2026)

## Abstract (arXiv)

The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of traditionally expensive iterative methods. However, the development and evaluation of ML methods remain limited by the scarcity of realistic velocity models, as most high-quality data are privately owned by oil and gas companies. To address this gap, we present OpenSeisML, a collection of real seismic datasets designed to support generative AI (Gen-AI) workflows for seismic inversion. The datasets are curated from publicly available surveys in the UK National Data Repository (NDR). When seismic volumes are in the time domain and wells are in depth, a time-to-depth conversion is required. We use checkshot data to establish the time-depth relationship and construct a velocity model through interpolation for accurate conversion of post-stack seismic data. Here, we present an automated data curation pipeline that enables seismic data preparation while ensuring reproducibility. The objective is to train a generative model that captures the statistical distribution of subsurface properties, enabling the synthesis of multiple statistically consistent realizations for uncertainty quantification which can act as a prior for seismic inversion.