Event-driven workflows for large-scale seismic imaging in the cloud

TitleEvent-driven workflows for large-scale seismic imaging in the cloud
Publication TypeSubmitted
Year of Publication2019
AuthorsPhilipp A. Witte, Mathias Louboutin, Henryk Modzelewski, Charles Jones, James Selvage, Felix J. Herrmann
Keywordscloud, Imaging, large-scale, LS-RTM, private, RTM, workflow
Abstract

Cloud computing has seen a large rise in popularity in recent years and is becoming a cost effective alternative to on-premise computing, with theoretically unlimited scalability. However, so far little progress has been made in adapting the cloud for high performance computing (HPC) tasks, such as seismic imaging and inversion. As the cloud does not provide the same type of fast and reliable connections as conventional HPC clusters, porting legacy codes developed for HPC environments to the cloud is ineffective and misses out on an opportunity to take advantage of new technologies presented by the cloud. We present a novel approach of bringing seismic imaging and inversion workflows to the cloud, which does not rely on a traditional HPC environment, but is based on serverless and event-driven computations. Computational resources are assigned dynamically in response to events, thus minimizing idle time and providing resilience to hardware failures. We test our workflow on two large-scale imaging examples and demonstrate that cost-effective HPC in the cloud is possible, but requires careful reconsiderations of how to bring software to the cloud.

Notes

Submitted to SEG on March 29, 2019

URLhttps://www.slim.eos.ubc.ca/Publications/Private/Submitted/2019/witte2019SEGedw/witte2019SEGedw.html
Citation Keywitte2019SEGedw