arXiv:2605.20539v1 Announce Type: new Abstract: 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
The increasing maturity of generative AI models and the critical need for robust, real-world datasets across various scientific domains are converging to address data scarcity challenges.
A strategic reader should care because this initiative democratizes access to crucial seismic data, potentially accelerating innovation in energy exploration and geological modeling through AI.
The availability of OpenSeisML shifts the landscape from proprietary, siloed seismic data to a more open, accessible resource for AI research and development.
- · AI researchers
- · Energy exploration startups
- · Generative AI companies
- · Academic institutions
- · Oil and gas companies reliant on proprietary data advantage
- · Traditional seismic inversion software vendors
OpenSeisML provides a standardized benchmark for evaluating new AI methods in seismic inversion.
Improved and more efficient energy exploration could lead to lower operational costs and potentially new discoveries.
The success of OpenSeisML could inspire similar open data initiatives in other resource-heavy industries, fostering broader AI-driven innovation.
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Read at arXiv cs.LG