11 September (Online) & 15 September (On Site)
Building 1, Level 2 Multi-Purpose Room (MPR)
KAUST, University Campus, Saudi Arabia
We are thrilled to invite you to an exciting and transformative event at the upcoming High Performance Workshop at KAUST, Saudi Arabia. Join us in this exciting journey to transform the way we handle and interpret seismic data. By developing and utilizing embeddings, we can unlock new potentials in seismic exploration and subsurface analysis. Let your creativity and expertise shine as you tackle these challenges and contribute to the future of seismic data processing. Happy hacking!
ONLINE via MS Teams / Zoom Link: Introduction & Information
ONSITE at Building 1, Level 2 Multi Purpose Room (MPR): In-person Meeting
Participation is included in the workshop registration, NO additional registration is required, however please do let us know if you are attending to ensure you receive all the updates leading up to the event.
If you will be attending the Hackathon, please inform us by emailing nlr@eage.org
The hackathon at EAGE HPC Workshop 2024 at KAUST aims to explore the feasibility of building embeddings for pre-stack seismic data and their applications. Participants will fine-tune or train vision, text, or multimodal transformer models to generate embeddings for entire 2D shot-gathers. These embeddings will then be used to enhance various seismic analysis tasks.
Image credit: Metamorworks
Develop embeddings for 2D shot-gathers arranged in the gather of their choice. These embeddings should be capable of capturing the essential features of the seismic data while significantly reducing its dimensionality.
Utilize the embeddings to cluster and classify shots based on characteristics such as marine or land data, shallow or deep water environments, synthetic or real data, and the presence of strong ground roll. This task will help in evaluating the discriminative power of the embeddings.
Integrate additional values or data sources, such as geological or environmental data, to enrich the embeddings and improve the robustness of the analysis.