This post is cowritten with Altay Sansal and Alejandro Valenciano from TGS.
TGS, a geoscience data provider for the energy sector, supports companies’ exploration and production workflows with advanced seismic foundation models (SFMs). These models analyze complex 3D seismic data to identify geological structures vital for energy exploration. To help enhance their next-generation models as part of their AWS infrastructure modernization, TGS partnered with the AWS Generative AI Innovation Center (GenAIIC) to optimize their SFM training infrastructure.
This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger than previously possible.
Addressing seismic foundation model training challenges
TGS’s SFM uses a…