Abstractions for at-scale seismic inversion

TitleAbstractions for at-scale seismic inversion
Publication TypeConference
Year of Publication2022
AuthorsMathias Louboutin, Ali Siahkoohi, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Yijun Zhang, Philipp A. Witte, Gabrio Rizzuti, Felix J. Herrmann
Conference NameRice Oil and Gas High Performance Computing Conference 2022
KeywordsCCS, devito, FWI, HPC, inversion, JUDI, machine learning, RHPC, software, Uncertainty quantification

We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.


Rice Oil and Gas High Performance Computing Conference 2022

Citation Keylouboutin2022RHPCafa