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Announcing JUDI's Support for DevitoPRO
A New Era of Seismic Imaging and Inversion
As part of our ongoing efforts to enable rapid innovation and performance portability across the industry we are pleased to announce that as of JUDI, (Julia Devito Inversion framework) v3.4.5, DevitoPRO is supported as a bring-your-own-license (BYOL) feature. This brings production-grade performance-portability and scalability to JUDI solvers for modeling, inversion, machine learning, and much more.
JUDI, (Julia Devito Inversion framework) is an open-source Julia-based package for large-scale seismic modeling and inversion designed by Georgia Tech’s Seismic Laboratory for Imaging and Modeling (SLIM) to translate wave-based algorithms into fast, scalable code suitable for industry-size 3D problems on clusters or in the cloud. Built on top of Devito, a Python domain-specific language for automated finite-difference computations, JUDI leverages Devito’s symbolic API to generate high-performance wave propagation kernels. This integration combines Devito’s computational power with Julia’s flexibility, enabling efficient simulations and the implementation of PDE-constrained optimizations like full-waveform inversion (FWI) and imaging (LS-RTM). JUDI’s modeling operators can also be integrated into neural networks for physics-augmented deep learning, as highlighted in SLIM’s Leading Edge article, Learned multiphysics inversion with differentiable programming and machine learning.
The integration of DevitoPRO into JUDI enables large-scale and real-world seismic inversion simulations by introducing key performance and memory management improvements. These include support for CUDA/HIP/SYCL for GPUs, domain-specific optimizations such as automatic source/receiver expanding box in the propagator for both forward and adjoint solves, and asynchronous wavefield serialization with lossy/lossless compression. Additionally, the fully supported DevitoPRO decoupler allows single-shot domain decomposition over multiple devices or NUMA domains while maintaining a serial Julia process. These features enable state-of-the-art performance and scalability, particularly for long-offset FWI, high-frequency RTM, and full-wavefield imaging, all of which have a large memory footprint and computational requirements. Users with a DevitoPRO license benefit from enhanced performance, improved scalability, and an easier transition from prototyping to production environments, facilitating the application of their projects to real-world scenarios.
JUDI use cases from SLIM
JUDI has been successfully applied in various advanced research and cloud imaging projects, highlighting its versatility and power in seismic modeling and inversion. One significant use case is WISE: full-Waveform variational Inference via Subsurface Extensions, where JUDI is used to combine variational inference and conditional normalizing flows for probabilistic full-waveform inference. This approach helps reduce the reliance on accurate initial migration-velocity models and enables reliable uncertainty quantification in velocity models, showcasing JUDI’s capability to generate high-quality seismic images that include uncertainty.
Another notable application is in compressive time-lapse seismic monitoring of carbon storage and sequestration. JUDI was employed in this project to improve the efficiency and accuracy of time-lapse seismic data acquisition using a joint recovery model. This method allows for high-quality monitoring of CO~2~ plumes over extended periods, which is crucial for carbon capture and storage (CCS) projects.
In the realm of machine learning and inversion, JUDI was integral to the development of a learned coupled inversion framework for carbon sequestration monitoring. This framework uses Fourier Neural Operators (FNOs) to estimate permeability from time-lapse seismic data, enabling the forecasting of CO~2~ plume behavior with improved accuracy and computational efficiency.
JUDI also forms an essential component in the development of a Digital Twin for Geological Carbon Storage. In this approach, JUDI provides wave simulation and imaging capabilities that undergird training of the Digital Twin’s generative neural networks (checkout President’s Page: Digital twins in the era of generative AI).
Furthermore, JUDI has played a role in the development of serverless imaging on the cloud, which involves deploying seismic imaging processes on cloud infrastructure without the need for traditional server management. This approach allows for scalable and cost-effective seismic data processing, making it accessible for large-scale industrial applications (see [An Event-Driven Approach to Serverless Seismic Imaging in the Cloud])https://ieeexplore.ieee.org/document/9044390)).
These examples underline JUDI’s extensive applicability across various domains in seismic research, particularly in leveraging advanced computational methods and cloud-based solutions.
Conclusion and Outlook
The integration of DevitoPRO into JUDI represents a significant advancement in the field of seismic imaging and inversion, offering users enhanced performance, scalability, and new capabilities. As we look to the future, several promising developments are on the horizon. These include the continuous expansion of features, such as more sophisticated wave-equation solvers and advanced optimization techniques. Additionally, the integration of cutting-edge machine learning algorithms, generative AI, and physics-informed neural networks is expected to further improve the accuracy and computational efficiency of seismic imaging and inversion.
Continued collaboration within the user community will be essential to drive innovation and address emerging challenges. Open-source contributions, feedback, and joint research initiatives will play a crucial role in advancing JUDI and DevitoPRO, ensuring they remain at the forefront of seismic technology.
The ongoing support and development by Devito Codes, particularly through the contributions of Dr Mathias Louboutin, who has been instrumental in both JUDI’s initial development and its continued evolution, underline the commitment to delivering value to the open-source community. This collaboration bridges the gap between academic research and production-level applications, positioning JUDI and Devito Codes as leaders in providing comprehensive solutions to accelerate innovations in the field of seismic imaging and inversion.
We encourage users to explore these new functionalities and actively engage with the community to advance further this powerful toolset, continuing the journey of innovation and excellence in seismic research and applications.
